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People News for January 2018

Mon, 01/01/2018 - 6:00am
Cheiyenne Seerattan

Winston Richards (left) presents a shield and cash prize to Cheiyenne Seerattan.

While in Trinidad recently, Winston A. Richards—professor emeritus at The Pennsylvania State University—presented the Winston A. Richards Prize in Statistics to Cheiyenne Seerattan.

This cash prize is awarded to a student who has the best II and III performance in statistics.

The idea for the prize was conceived in 2010 at Richards’ 75th birthday celebration when Ingram Olkin of Stanford University gave a series of lectures.

Mir Masoom Ali

Mir Masoom Ali, George and Frances Ball Distinguished Professor of Statistics emeritus at Ball State University, served as the colloquium speaker during a recent World Statistics Day celebration at Ball State.

Ali’s talk was titled “History of the Statistics Program at Ball State University.” As the founder of the statistics program at Ball State in 1971, he spoke about the success of the students and growth of the statistics program during nearly five decades.

The day ended with a discussion session between Ali and the statistics faculty and students.

William F. Rosenberger

William Rosenberger

William F. Rosenberger, university professor and chair of the department of statistics at George Mason University, was named the 15th Armitage Lecturer at the University of Cambridge MRC Biostatistics Unit.

The Armitage Lecture was the keynote address at the November 9 symposium, dedicated to Peter Armitage’s extensive contributions to biostatistics. Rosenberger presented the lecture, “Randomization: The Forgotten Component of the Randomized Clinical Trial.” He was also invited to spend two weeks at the unit working with postdoctoral fellows and faculty.

Armitage, who is 93 years old, was unable to attend the lecture, but he was able to watch a videotape of the lecture and sent his best wishes to Rosenberger.

Analyze Weather Forecasts for Data Expo 2018

Mon, 01/01/2018 - 6:00am
Wendy Martinez and Jenny Guarino

    The Statistical Computing and Graphics sections will host the Data Expo in 2018.

    The data consist of three years of weather forecasts for 113 cities in the United States harvested from the National Weather Service website. Historical data that do not necessarily match the location of the forecasts are also provided, and contestants are allowed to use additional weather data in their analyses.

    Possible questions for analysis include the following:

    • What is the distribution of the errors in the forecast?
    • Are some locations more stable or variable than others?
    • How has the weather changed over the three years?

    Contestants must present their results at JSM 2018 in Vancouver, British Columbia. Group entries are welcome. To enter, submit a speed session abstract by February 1, 2018. (It doesn’t need to be perfect or specific. Abstracts can be modified later.) 

    After the abstract is submitted, contestants must send an expression of interest/intention by February 2, 2018, to Radu Herbei and Leanna House. The email should include the submitted abstract and abstract number.

    Going Back … Way Back

    The Statistical Computing and Graphics sections have been sponsoring the Data Exposition (Expo) for many years, during which they have challenged contestants to analyze a given data set. The first challenge took place in 1982 and was sponsored by the then Committee on Statistical Graphics. The stated purposes of the first exposition were “(1) to provide a forum in which users and providers of statistical graphics technology can exchange information and ideas and (2) to expose those members of the ASA community who are less familiar with statistical graphics to its capabilities and potential benefits.”

    More about past data expos can be found at the ASA Sections on Statistical Computing and Statistical Graphics website.

    It is interesting to see the changes in the data sets over the years. The data set for the 1983 Data Expo had measurements of mpg, number of cylinders, and displacement on 406 automobiles.

    It is not surprising that the size of the data sets has grown. For example, the airline on-time data used in 2009 contains approximately 120 million records (12 gigabytes) consisting of flight arrival and departure details for all commercial flights within the Unites States from October 1987 to April 2008. The airline data set has become widely used in machine learning and data science research.

    The Government Statistics Section (GSS) started to issue annual data challenges in 2015. The contests were open to anyone interested in participating, including college students and professionals from the private or public sector. These contests challenged participants to analyze a government data set using statistical and visualization tools and methods.

    Mike Jadoo from the Bureau of Labor Statistics participated in two data challenges as a contestant in the professional category. He has this to say about the experience:

    I participated in two data challenges, and in my opinion, the experience was great. From my participation, I gained more skills in programming and analyzing data and was able to bring those abilities back to the office that I work for. I have also shared the skills I attained with the students I teach, which has made a big difference in the classroom experience. Students love to hear how the topics they are learning [about] can actually be applied in different situations.

    Professors have found the data challenges to be good teaching tools. Several entries into the GSS expos have been a team of students from a statistics class in which the analysis of the challenge data set was the main focus.

    Eric Kolaczyk of the Center for Information and Systems Engineering at Boston University used the 2017 Data Challenge in a unique way. He held his own contest in the classroom, where each student was asked to learn about the data and conduct their own analysis. The winning student’s project was then submitted as the entry.

    In some cases, the contestants continue to interact with government personnel providing the data. For instance, Jonathan Auerbach of Columbia University and a winner in the 2016 GSS Data Challenge was funded by the Evaluation of Low Cost Safety Improvements Pooled Fund Study to present his award-winning paper to 40 state member representatives in its annual Technical Advisor Meeting. The paper offered a new statistical methodology for highway safety evaluations and presented a fresh perspective on the evaluation of pedestrian safety improvement, according to Roya Amjadi of the Federal Highway Administration.

    Contestants in the Data Expo and Data Challenge have also had the opportunity to publish their results in a special issue of the refereed journal Computational Statistics. Editor-in-chief Juergen Symanzik and the co-editors of the special issues are currently working on the 2016 and 2017 challenge issues, making the articles fully reproducible.

    Making New Year’s Transitions

    Mon, 01/01/2018 - 6:00am

    The New Year holiday offers a time to reflect upon past accomplishments and plan for new resolutions. This year, transition is also in the air, as I take on the wonderful honor of serving as ASA president in 2018 while making a career change at the same time. This first President’s Corner gives me an opportunity to reflect on both the job I am leaving and the one I am about to assume, while giving Amstat News readers an inside look into what makes this president tick.

    Lisa LaVange

    The US Food and Drug Administration (FDA) employs more mathematical statisticians in the GS 1529 series than any other government agency—more than 350 by recent count. Statistical reviewers across the seven FDA centers occupy this civil service position to carry out the important work of regulating food, drugs, biologics, medical devices, veterinary medicines, toxicological research, and tobacco products for the American people.

    As director of the Office of Biostatistics in the Center for Drug Evaluation and Research (CDER) since 2011, I have had the good fortune to lead an extremely talented and dedicated staff who develop and apply statistical methodology for drug regulation. Our staff members—predominantly holding doctoral degrees in statistics or biostatistics—advise pharmaceutical companies on statistical aspects of drug development; review clinical trial protocols; assess the benefit and risk of new drugs, biologics, generic drugs, and biosimilar products based on data submitted to the FDA for approval; and monitor the risk of drugs after they are marketed and their use expands. Our mission is to make sure safe and effective drugs are available to improve the health of people in the US.

    A day in the life of a CDER statistician involves assessing the integrity of clinical data submitted in support of a marketing application and assessing the validity of the sponsor’s analyses intended to provide evidence that the drug is safe and effective. But our work does not stop with these basic components of a statistical review. We conduct research in clinical trial design and analysis to improve the efficiency of drug development; we investigate other data sources—popularly termed real-world data—for evidence of safety signals that need further study; and we conduct meta-analysis to investigate trends across trials or drugs in the same class.

    One of our most important jobs is to develop statistical policy and issue guidance so pharmaceutical companies know what to expect in our reviews. The development of statistical guidance documents is a frequent commitment agreed to under the Prescription Drug User Fee Acts (PDUFAs), first passed into law in 1992 and renewed every five years. With these acts, fees associated with marketing applications are used to provide additional resources for hiring review staff and improving processes to enable faster turnaround times for regulatory reviews and marketing decisions. The user-fee programs have shortened review times for new drug applications from 2.5–3 years to under a year, and their success has led to similar programs for biosimilar products and generic drugs.

    About a year into my job at FDA, I wrote about the challenges statistical reviewers faced in CDER and made a case for the indispensable role statistical thinking plays in meeting those challenges. This past year, I gave a JSM talk about my six-year FDA career highlighting the accomplishments of the talented statisticians I have had the privilege to lead. As a brief sampling of those highlights, CDER’s Office of Biostatistics did the following:

    • Grew from 165 statisticians and support staff to 216 (as of November 30, 2017). This growth is analogous to hiring an entire academic department—no small feat given the flat-line growth of doctoral-level candidates in our field.
    • Issued statistical guidance on noninferiority trials, multiple endpoints, and statistical assessment of analytical similarity for biosimilar products; completed draft guidance on adaptive designs and meta-analysis. These were in addition to collaborating on numerous disease-specific guidances for both new and generic drugs.
    • Sponsored collaborative workshops and research efforts in several areas of unmet medical need, most notably development of anti-bacterial products, products to treat a variety of rare diseases with no available therapies, and oncology/hematology products. Research topics spanned new methods for trial design, new biomarkers to enrich trial enrollment, and new surrogate endpoints suitable for accelerated approval of ground-breaking therapies. Trial designs that make use of prior information through Bayesian modeling were a breakthrough for CDER during this period as a way of substituting information when patients are scarce.
    • Encouraged pharmaceutical sponsors to join forces in master protocols and platform trials designed to study more than one therapy in more than one disease in a perpetual fashion. The FDA supported efforts of patient advocacy groups to serve as a catalyst in these multi-organizational endeavors in breast cancer, lung cancer, and drug-resistant bacterial infections, as examples.
    • Contributed to finding a solution to the opioid addiction problem through the evaluation of abuse-deterrent formulations and monitoring of drug abuse rates to determine the impact of marketing these formulations. Our leadership in developing and evaluating methodologies and data sources for post-market surveillance has been instrumental in FDA’s continuing efforts to combat this immense public health problem.

    This is not an advertisement for employment as an FDA statistician, but clearly, the impact of the job—the sheer number of topics and interesting problems our staff addresses—speaks for itself. What an immensely rewarding place to work!

    As I leave one of the best leadership jobs in statistics, you might ask, “What were you thinking?” My decision was based on several factors. First, I felt a strong urge to attend to the training of statisticians, having struggled in their hiring for so long. My tenure at FDA has given me new insights into the importance of statisticians’ being able to navigate data sources of all types, sizes, and levels of complexity to search for signals and solve complex problems. Our leadership in this area—our ability as data scientists to quantify uncertainty and make meaningful interpretations of the results—has never been needed more.

    Second, I felt the need to resume my work in leadership training. Having launched a doctoral-level course in statistical leadership in 2011 and continuing with numerous short courses and lectures on the topic, the time is right to return to this important task in earnest.

    Third, I wanted more dedicated time to serve as ASA president than continuing my leadership role at FDA would practically allow.

    And fourth, my family calls. After six and a quarter years of commuting from Chapel Hill, North Carolina, to Washington, DC, it was time to go home.

    On January 1, I rejoined the faculty in biostatistics in the Gillings School of Global Public Health at The University of North Carolina at Chapel Hill. Here, I serve as associate chair of the department and director of the Collaborative Studies Coordinating Center and am coordinating development of the data science curriculum and reinstatement of leadership training. I am excited to return to a workplace and colleagues I know well and enjoy working with. The department is fully supportive of my ASA presidency, and my commute has been reduced to a 10-minute walk.

    My new responsibilities as a biostatistics faculty member at UNC align well with the various initiatives and activities underway at the ASA. In future issues of Amstat News, I look forward to sharing details of my 2018 ASA presidential initiatives in leadership and professional development with you. In the meantime, Happy New Year!

    Rob Kass: Brain Research Is Underserved by Statistics

    Mon, 01/01/2018 - 6:00am

    Brain-related disorders affect almost everyone, either directly or through family or friends. For many of the disorders, whether they’re psychiatric or neurological, there are basic scientific descriptions and valuable treatment options, but none has a satisfactory cure because the underlying mechanisms are not fully understood.

    The federal government launched the BRAIN Initiative in 2013 to ignite the development and application of new technologies needed for major advances in understanding the brain. Carnegie Mellon University’s Rob Kass thinks brain research is in desperate need of cutting-edge statistics, which can and should supply a crucial link between new, highly complex data and the thorough scientific explanations the research aims to generate.

    As the Committee of Presidents of Statistical Societies’ 2017 R. A. Fisher Lecturer, Kass outlined his case in “The Importance of Statistics: Lessons from the Brain Sciences.” Watch the whole talk on the JSM 2017 website.

    “Most people have no idea how advanced statistical thinking can elevate research and accelerate scientific discovery,” said Kass, the Maurice Falk Professor of Statistics and Computational Neuroscience in CMU’s Dietrich College of Humanities and Social Sciences. “In my lecture, I pointed to some difficulties that arise when statistical ideas are ignored in the analysis of complex neuroscience data.”

    After illustrating how recordings of neuron activity have played a fundamental role in the brain sciences, Kass gave examples of neuroscience questions that led to interesting statistical problems, and how good solutions to those problems have been guided by the teachings of statistics.

    “This lecture eloquently describes the central role of statistics in scientific inference, showing how several modern advances in neuroscience have been built on Fisher’s remarkable foundational work in statistics, nearly 100 years ago,” said Nancy Reid, university professor of statistical sciences at the University of Toronto.

    At CMU, Kass holds faculty appointments in the statistics and data science and machine learning departments and is the interim director of the Center for the Neural Basis of Cognition. He is the co-author of Geometrical Foundations of Asymptotic Inference and Analysis of Neural Data, and has also written widely read articles about statistical education, including, “Ten Simple Rules for Effective Statistical Practice.”

    The R. A. Fisher Lecture recognizes individuals whose statistical achievements and scholarship have had a highly significant effect on scientific investigations. Kass was honored “for ground-breaking contributions to several areas of statistics, including use of differential geometry in statistical theory as well as theory and methodology of Bayesian inference; for strong commitment to the application of principled statistical thinking and modeling to problems in computational neuroscience; and for his strong dedication to training of students and users of statistics.”

    External Nominations and Awards Committee: Why, What, and How?

    Mon, 01/01/2018 - 6:00am
    Narayanaswamy Balakrishnan
    Christy Chuang-Stein
    Mary K. Batcher
    Joseph C. Cappelleri
    Katherine B. Ensor
    Joel B. Greenhouse
    Regina Y. Liu
    Sastry G. Pantula
    James L. Rosenberger
    Katherine K. Wallman
    Steve Pierson (Staff Liaison)

    Statisticians have an important role in helping to make evidence-based decisions in a world awash in data. For this reason, statisticians should serve on expert panels, advisory committees, and commissions where their expertise can enrich deliberations. Equally, statisticians who have been brilliantly playing these roles for a long period should be acknowledged for their contributions.

    Part of the American Statistical Association’s mission is to promote the practice and profession of statistics. The ASA wants to proactively ensure that qualified statisticians are nominated for external advisory boards/committees and that worthy statisticians are nominated for external awards.

    To this end, the ASA has convened the External Nominations and Awards Committee (EN&AC) with a specific charge to do the following:

    • Identify boards, committees, and other bodies external to statistics to which statisticians should be appointed to assist in advancing science and in raising the profile of the profession
    • Identify high-profile awards (external to statistics) for which statisticians might be eligible
    • Identify people who should be nominated for these positions or awards and identify and reach out to people who would be able to effectively nominate them

    This new committee will operate similarly to other ASA committees, with members serving a three-year term and having an opportunity for a second-term reappointment.

    The committee has begun its work and developed the following principles and process to guide its work:

    • Identify and recommend the most worthy candidates
    • Solicit input from pertinent ASA section(s) and the ASA membership as much as time allows
    • Encourage self-nominations
    • Recommend nominees with the requested expertise and open minds to serve on external boards/committees; the ability to maintain one’s objectivity on the issues of focus is an important consideration
    • Recognize the need for diverse representation for nominees for external boards/committees and external awards

    When appropriate, recommend multiple nominees with distinctive backgrounds to allow a richer overall composition of an external board/committee

    To foster its mission, EN&AC is compiling a list of existing external boards/committees on which statisticians are represented or there is a potential for a statistician to contribute significantly. The committee is also compiling a list of existing awards for which statisticians should be nominated. Members plan to reach out to leaders of the statistical community to help populate these lists. We are seeking ASA members’ help also. If you have information for the lists we are compiling, please send it to the committee co-chairs, Narayanaswamy Balakrishnan and Christy Chuang-Stein. If you have suggestions about the principles and process outlined above, please share it with the committee co-chairs, as well.

    Biometrics Section News for January

    Mon, 01/01/2018 - 6:00am
    Edited by Zheyu Wang, Biometrics Section Publications Officer

      Abstracts for contributed and topic-contributed papers will be accepted online until February 1.

      Call for Proposals

      The Biometrics Section invites applications for funding to support projects developing innovative outreach focused on enhancing awareness of biostatistics among quantitatively talented US students. Of particular interest are projects that encourage students to pursue advanced training in biostatistics. Questions should be addressed to Tanya Garcia.

      Graph of the Month

      The graph of the month and other topics of broader interest will be moved to a blog post, which can be assessed without logging in to the ASA Community.

      What Does Rob Santos Like to Do When He Is Not Being a Statistician?

      Mon, 01/01/2018 - 6:00am

      As an SXSW crew chief, Rob Santos helps manage about 100 photographers every year at the SXSW Festival in Austin, Texas.

      Who are you, and what is your statistics position?
      I am a native Texan, born and raised in the west side neighborhoods of San Antonio. I’ve always had a love for mathematics and statistics, and, to my delight, was able to pursue a career in survey research first as a sampling statistician and then as a survey methodologist and statistical consultant.

      After about 40 years, I find myself in the rewarding role of chief methodologist and director of the Statistical Methods Group at the Urban Institute. It is an interesting and challenging position that allows me to work with researchers in a broad swath of policy research—immigration, housing discrimination, travel behavior, firefighter safety, education, program evaluation, health policy, justice policy, food insecurity, nonprofits collaborations, to name a few.

      Rob Santos took this photo of St. Vincent, one of the many musical artists he has captured with his Nikon.

      Tell us about what you like to do for fun when you are not being a statistician.
      Being a native Texan who lived (and now lives) in Austin, I always enjoyed attending the Austin City Limits Festival, a three-day outdoor music fest at Zilker Park near downtown Austin. (Regarding Zilker Park, think “moontower” in the movie Dazed and Confused, shot in Austin.)

      In 2009, as the ACL Fest was approaching, I awoke one morning and decided I wanted to be a live music photographer. I knew literally nothing about photography. I somehow managed to get a music magazine from New York City to give me media credentials and photoshot from the music pits at the foot of the stages during the 2010 and 2011 ACL Music Fests.

      In 2012, the magazine figured out I knew nothing about photography and dropped me like a hot potato, so I submitted a portfolio with the few good images I had captured by chance, and was selected as a photographer for SXSW Film, Interactive/Music. I have now been photoshooting and learning event photography for SXSW since 2012.

      I am a SXSW photo crew chief and help manage about 100 photographers in our crew each year at the SXSW Festival in Austin. I have had the opportunity to photograph artists of many musical genres who come from all over the world to play at SXSW. And I have learned and been inspired by the high-tech innovations of the Interactive Festival, plus the creative approaches to education at the SXSWEDU conference. In fact, a lot of my ideas for new programs at conferences like JSM and the annual conference of the American Association for Public Opinion Research stem from my exposure to the SXSW Festival. It’s always surprising that sources of inspiration and insight can come from the most unusual places. All this, while having fun taking pictures of celebrities, artists, and world leaders.

      What drew you to this hobby, and what keeps you interested?
      It’s weird, but my interest in live music photography was spontaneous. Once it occurred, I listened to and acted upon that sudden interest and it quickly grew into a passion. It is a way to be creative and use both sides of the brain in a fun way.

      MORE: To see more photos, visit Santos’ Flickr page.

      Statistics in Physical and Engineering Sciences Section News for January

      Mon, 01/01/2018 - 6:00am
      A Message from the Chair James G. Wendelberger, Los Alamos National Laboratory and University of New Mexico

        It has been a successful year for SPES. As I mentioned in my January message, SPES was able to partner with other sections to sponsor Joint Statistical Meetings (JSM) 2017 talks and roundtables in Baltimore, allowed other sections to take advantage of our JSM mixer, continued our Marquardt speakers program, co-sponsored the Spring Research Conference (SRC), co-sponsored the Fall Technical Conference (FTC), and provided various awards to our members.

        The Spring Research Conference on Statistics in Industry and Technology took place at Rutgers University May 17–19, 2017.

        In August, SPES sponsored numerous sessions and roundtables at the 2017 Joint Statistical Meetings. This year, the joint mixer with the Quality and Productivity Section was expanded to include other sections and continued to be full of fun and fabulous door prizes.

        The 61st annual Fall Technical Conference was held in Philadelphia October 4–6, 2017. The ASA’s 112th president, Barry Nussbaum, gave a luncheon talk, “It’s Not What We Said, It’s Not What They Heard, It’s What They Say They Heard.” SPES provided the wine and cheese reception and the conference was a resounding success.

        Let us keep the SPES membership growing.

        In closing, I would like to thank all the 2017 SPES officers and many volunteers for a successful year. Your dedication and service makes SPES a valuable organization for our members. Thank you for the opportunity to serve as your chair this past year. I wish you all a happy holiday season and continued success in 2018!

        Nominations Sought Ming Li, SPES Awards Chair

          The SPES award committee is seeking nominations for the 2018 award. In even-numbered years, the award is presented for distinguished work performed during the previous two years by a collaborative team of statisticians and practitioners in an industrial, manufacturing, or research organization. To be eligible for the award, at least one member of the team must be a member of the ASA and a member of the Section on Physical and Engineering Sciences when nominated.

          The deadline to submit nomination letters is February 20. The letters, along with any supporting materials, should be sent to Ming Li, chair of the Statistics in Physical Engineering Sciences Award committee. Unpublished work should be described in a format similar to a published paper.

          Joint Research Conference Joanne Wendelberger, Council of Sections Representative

            The 2018 Joint Research Conference on Statistics in Quality, Industry, and Technology will take place June 11–14 in Santa Fe, New Mexico. It is a joint meeting of the 25th Spring Research Conference on Statistics in Industry and Technology and the 35th Quality and Productivity Research Conference.

            The goal of this conference is to stimulate interdisciplinary research and innovative solutions to practical problems through interaction among statisticians, quality professionals, engineers, and scientists from diverse fields. The theme of this year’s conference is “The Art and Science of Statistics.” The technical program will focus on statistical methodology and creative problem solving to address scientific, industrial, and business challenges, drawing upon advances from the fields of statistics, machine learning, and data science.

            The conference will be hosted by Los Alamos National Laboratory and co-sponsored by SPES, the Quality and Productivity Section, and the Institute of Mathematical Statistics.

            If you are interested in presenting a paper or poster on a topic relevant to Statistics in Quality, Industry, and Technology, email your abstract to and indicate your preference for a presentation or poster. The abstract should include the following information:

            • Author(s) and Affiliation(s)
            • Title of the Presentation
            • Purpose of the Presentation (one sentence – i.e., “To inform, motivate, enlighten, etc.”)
            • A Concise Summary of the Work Done

            The deadline to submit abstracts is February 15. For more information, email or contact a member of the program committee:

            Statistical Education Section News for January

            Mon, 01/01/2018 - 6:00am

            The Statistical Education Section navigated a successful JSM 2017 under Section Program Chair Dalene Stangl and Roundtable Chair Kelly McConville, sponsoring or co-sponsoring five invited panels/sessions, nine topic-contributed panels/sessions, six contributed paper sessions, one traditional poster session, one speed poster session, and five roundtables. Also, Carol Blumberg and Rebecca Nichols organized the Statistical Education booth, coordinating with Teaching Statistics in the Health Sciences Section to create a one-stop shop for all things educational.

            Slides from many JSM 2016 talks are available on the section’s website, and others are being collected from JSM 2017. You can submit these to Brigitte Baldi.

            The following six section members were named as new ASA fellows:

            • Sam Behseta, California State University, Fullerton
            • Joe Fred Gonzalez, US Department of Health and Human Services
            • Charles Hall, Albert Einstein College of Medicine/li>
            • Tim Hesterberg, Google
            • Shonda Kuiper, Grinnell College
            • Michael Posner, Villanova University

            Contact Nicholas Horton—Fellows Committee chair—with nominations, questions, or suggestions for next year.

            The section also announced the following education award honorees:

            • Robin Lock, St. Lawrence University, 2016 Ron Wasserstein Award for Best Contributed Paper, for “Data Surfing on the World Wide Web – Part 2”
            • Kelly McConville, Swarthmore College, 2016 Statistical Education Section Speed Session Award for “Are Volcanic Eruptions Increasing? An Example of Teaching Data Wrangling and Visualization in Stat 2”
            • Jane Watson, University of Tasmania, and Lyn English, Queensland University of Technology, 2016 Jackie Dietz Award for Best JSE Paper for “Repeated Random Sampling in Year 5”
            • James Cochran, University of Alabama, 2017 Distinguished Teaching Career Award
            • Anna Bargagliotti, Loyola Marymount, 2017 Waller Education Award
            • Ann Cannon, Cornell College, 2017 Mu Sigma Rho William D. Warde Statistics Education Award
            • Nicholas Horton, Amherst College, 2017 Founders Award
            • Jane Pendergast, Duke University, 2017 Founders Award

            The following items of interest to section members were discussed at the open meeting: 

            The section’s new blog provides a modern way to keep up with the latest section news. It is mobile device–friendly, and you can subscribe to receive email notifications. Please send any ideas for posts to Kay Endriss, communications officer.

            Registration for the Electronic Undergraduate Statistics Research Conference (eUSR) is open. This free conference will take place November 3 from 12:00 p.m. – 5:00 p.m. EST and is open to all undergraduate students and faculty. This is a great opportunity for students at all stages of their undergraduate career to learn more about undergraduate statistics research. If you have any questions about eUSR, send an email to Kelly McConville.

            Resources for eCOTS 2018 (the electronic Conference on Teaching Statistics), taking place May 21–25, will soon appear online. Rebecca Nugent is the program chair.

            The 10th International Conference on Teaching Statistics will take place July 8–13 in Kyoto, Japan. The theme is “Looking back, looking forward.” Submission deadlines vary; see the website for details.

            Kelly McConville will chair the section’s 2018 JSM program; Stacey Hancock will chair the 2019 JSM program.

            Election Results

            In the spring 2017 section elections, the following members were elected and officially began their terms January 1:

            • Beth Chance of Cal Poly, San Luis Obispo, Chair
            • Mine Çetinkaya-Rundel of Duke University, Chair-elect
            • Matthew Hayat of Georgia State University, Council of Sections Representative
            • Sharon Lane-Getaz of St. Olaf College, Executive Committee at Large
            • Cassandra Pattanayak of Wellesley College, Executive Committee at Large
            • Stacey Hancock of Montana State University, JSM Program Chair-elect

            A detailed handout and minutes from the business meeting will be posted to the section’s website.

            Arizona Chapter Promotes DataFest

            Mon, 01/01/2018 - 6:00am

            Arizona Chapter officers promote a DataFest hackathon to Arizona State University students. From left: vice president Jie (Jane) Pu, treasurer Yongzhao Peng, officers Shuo Jiang and Chantal Jubinville, and president Rodney Jee.

            The Arizona Chapter kicked off the academic year with a meeting at Arizona State University in which newly elected chapter officers pitched the ASA’s DataFest to students and faculty.

            The officers explained to 30 undergraduates from math, business, computer science, industrial engineering, and other majors what a great opportunity it was for them to take part in the 48-hour data hackathon.

            Vice president Jie (Jane) Pu of Banner Health referenced past DataFest data to give the students a sense of what an open-ended data analysis task might look like. Officers Shuo Jiang and Chantal Jubinville, both of Discover Financial Services, talked about what student competitors or competition advisers could expect from taking part in the event.

            Treasurer Yongzhao Peng of Arizona Heath Services explained that the chapter would take care of all expenses for this DataFest, so the event would essentially be free for them. President Rodney Jee of Discover held pre-registration for the competition and received 22 student entries.

            With this strong degree of interest, the chapter will move into preparations for the event by soliciting sponsors, recruiting judges and advisers, arranging for meal service, and working with the university for a date and venue.

            The meeting was hosted by John Stufken of the School of Mathematics and Statistical Sciences, which will provide event coordination for the spring competition.

            Obituaries for December 2017

            Fri, 12/01/2017 - 6:00am

            ASA Fellow Andre I. Khuri passed away August 30, 2017, surrounded by his family after an extended battle with cancer.

            Born in Damascus, Syria, on March 1, 1940, Khuri received his early education in Damascus, graduating from Damascus University before going to the American University of Beirut. He then pursued a PhD in mathematics from the University of Florida, graduating in 1969. He married in 1970 and moved to the Middle East for a few years before deciding to return to the US and further his studies at Virginia Polytechnic University. After finishing a PhD in statistics in 1976, he moved to the University of Florida, where he spent the rest of his career, retiring as full professor after 31 years. He was a prolific author, having published more than 100 journal articles and seven books.

            In addition to being a Fellow of the ASA, Khuri was also a fellow of AAAS and an elected member of the International Statistical Institute. He served as editor of several statistical journals. He loved history, international politics, and astronomy.

            Visit Khuri’s memorial page to read more.

            People News for December

            Fri, 12/01/2017 - 6:00am
            Bani K. Mallick

            Bani K. Mallick, distinguished professor and Susan M. Arseven Chair of Statistics at Texas A&M, has been selected to receive a Fulbright Distinguished Chair for the 2017–2018 year.

            Fulbright Distinguished Chair Awards are viewed as among the most prestigious appointments in the Fulbright Scholar Program. Only about 40 awards are given globally each year to eminent scholars who have a significant publication and teaching record.

            Mallick has been recognized with the Fulbright-Nehru Distinguished Chair, named to honor his host country India and its first prime minister, Jawaharlal Nehru. As part of the chair, he will spend four months later this year conducting research and lecturing at institutes across India on big data cancer research, which he specifically selected.

            Emmanuel Candès

            Stanford University’s Emmanuel Candès, the Barnum-Simons Chair in Mathematics and Statistics, has been named a MacArthur Fellow. The prestigious recognition is a five-year grant to individuals who show exceptional creativity in their work and the prospect for still more in the future. It is designed to provide recipients with flexibility to pursue their own artistic, intellectual, and professional activities without specific obligations or reporting requirements.

            One of 24 recipients in the class of 2017, Candès is recognized for exploring the limits of signal recovery and matrix completion from incomplete data sets with implications for high-impact applications in multiple fields. His research focuses on reconstructing high-resolution images from small numbers of random measurements, as well as recovering the missing entries in massive data tables. At the interface of applied and theoretical mathematics, his work is generating new lines of research in information theory, as well as laying the groundwork for improvements in many devices that make use of signal and image processing methods.

            Read more about Candès and the MacArthur award winners on the Fellow website.

            Scott Clark

            From left: Scott Clark, 2016 award recipient; Michael Kutner; Brent Blumenstein, 2017 award recipient; and Lance Waller

            On October 23, the department of biostatistics and bioinformatics in the Emory University Rollins School of Public Health presented the 2016 Kutner Distinguished Alumni Award to Scott Clark, senior director of statistics at Eli Lilly & Company, and the 2017 Kutner Distinguished Alumni Award to Brent Blumenstein, founder and president of TriARC Consulting. The award, given annually to a former graduate of the department, is for distinguished service to the discipline.

            The event also honored the 40+-year exemplary Emory career of Kutner, Rollins professor of biostatistics. Lance Waller, Rollins professor and chair of the Emory Department of Biostatistics and Bioinformatics, served as the master of ceremonies. Invited speakers in addition to Waller included Rollins School of Public Health dean, James Curran; former department chair E.C. Hall; and senior executive associate dean for academic affairs Richard Levinson. John Neter, professor emeritus of the University of Georgia, and Fadlo Khuri, president of American University, Beirut, Lebanon, sent congratulatory letters.

            Kutner, in his closing remarks, thanked all the speakers and attendees for their participation. He then gave a brief account of his most important and influential research projects while at Emory University and when he chaired the department of biostatistics and epidemiology at the Cleveland Clinic Foundation (1994–1999).

            Sastry Pantula

            From left: Ellen Kulinsky, University of California, Berkeley; Sastry Pantula, Oregon State University; Aaron Huang, University of Washington; Betsy Hensel, University of Virginia; Shelby Taylor, Brigham Young University
            Photo by Megan Griffiths

            Oregon State University (OSU) was chosen by the ASA as one of three REU sites this past summer and hosted students from across the country from June 19 until August 25. The four REU participants were selected from more than 100 applicants in a highly competitive process. To read more about the OSU REU program, visit OSU’s website.

            Registration Open for International Conference on Health Policy Statistics

            Fri, 12/01/2017 - 6:00am
            Bonnie Ghosh-Dastidar, Laura Lee Johnson, and Elizabeth Stuart

              Registration is open for the International Conference on Health Policy Statistics (ICHPS), to be held in Charleston, South Carolina, January 10–12, 2018. Join fellow statisticians, economists, policy researchers, practitioners, and other stakeholders in the health policy arena to discuss research needs and solutions to the methodological challenges in the design of studies and analysis of data for health policy research.

              This year’s program features plenary talks by Robert Califf and Suchi Saria. Califf is an expert in cardiovascular medicine, health outcomes research, and clinical research. He is the Donald F. Fortin, MD, Professor of Cardiology at Duke University School of Medicine and former commissioner of the US Food and Drug Administration (FDA). Before his time at the FDA, Califf was vice chancellor for clinical and translational research at Duke University, director of the Duke Translational Medicine Institute, and founding director of the Duke Clinical Research Institute.

              Saria’s research interests are in statistical machine learning and precision health care, particularly designing data-driven tools for improving health care delivery. She is assistant professor of computer science, health policy, and statistics at The Johns Hopkins University. She was added to the Institute of Electrical and Electronics Engineers (IEEE) Intelligent Systems 2015 “AI’s 10 to Watch” list, which acknowledges and celebrates rising stars in the artificial intelligence field. Additionally, she was selected as a DARPA Young Faculty awardee and to Popular Science’s “Brilliant 10” in 2016.

              The ICHPS program also includes workshops on January 10 and 12, which are free for students. Workshop topics include a three-part session on causal inference; sign up for one, two, or all three sections, depending on the topics that interest you most. Other workshops include interpretation of patient-reported outcome and complex health survey data; social network analysis; fitting models in Python and Apache Spark; and using data from MEPS, NHANES, and MCBS.

              The conference committee is setting up a free two-hour health communications training workshop Friday afternoon, after the closing plenary. Once details are available, all conference registrants will be notified and sent an Evite to sign up for a seat.

              The contributed and invited sessions will include participants from around the world and cover a variety of topics. With research on impacts of health policy changes on pre-term births, Medicare expansion, cancer screening methods, environmental health, and linking registries and administrative data sets, the program includes methods and applications for everyone.

              Finally, distinguished researchers will be honored Friday with mid-career and long-term excellence awards sponsored by the ASA Health Policy Statistics Section.

              This year, participants can get to know each other through networking dinners, receptions, and tours of historical Charleston. Most local activities will have additional costs, but group discounts are being arranged where possible.

              Register today. Also, visit the website for hotel and travel information. If the group rate is not available online for your dates, you can contact the hotel directly by phone and they may be able to assist you.

              Asian Initiative Workshop Concludes with Sage Advice

              Fri, 12/01/2017 - 6:00am
              Amarjot Kaur, David Morganstein, Cyrus Mehta, Tony Cai, Donsig Jang, Barry Nussbaum, and Donna LaLonde

                “Coming together is a beginning; keeping together is progress; working together is success.”
                — Henry Ford

                During the 2017 Joint Statistical Meetings (JSM) in Baltimore, the American Statistical Association, International Indian Statistical Association (IISA), International Chinese Statistical Association (ICSA), and Korean International Statistical Society (KISS) organized a workshop on career development and leadership skills. The workshop was organized by the taskforce created under ASA President Barry Nussbaum’s initiative to identify ways the societies could collaborate to identify and address distinctive needs and concerns of Asian statisticians and to help enhance their professional growth.

                The workshop was successful in providing an opportunity to interact with and learn from the leaders who are passionate about the development of Asian statisticians. The keen interest of the audience of more than 30 was exemplified with their active participation, leaving no time for the prepared questions. And that was great!

                The distinguished panelists, representing all four societies, included David Morganstein from Westat and former ASA president, Cyrus Mehta from Cytel and former IISA president, Tony Cai from the University of Pennsylvania and current ICSA president, and Donsig Jang from NORC at the University of Chicago and KISS president-elect. The discussion was moderated by Amarjot Kaur from Merck Research Labs and past-president of IISA. Nussbaum made the opening remarks and provided motivation behind the initiative.

                Topics Considered

                The statistical leaders’ diverse backgrounds affected their own professional experiences, challenges, and ability to overcome cultural barriers. They discussed unique challenges they encountered due to different cultural backgrounds and communication skills and the approaches they took to overcome them, professional benefits of working with statisticians with diverse backgrounds, and the important skill set of Asian statisticians and potential areas of improvement.

                Morganstein provided his perspective as a native speaker during both his interactions with family members from Asia and colleagues at Westat. He emphasized communication skills and networking and shared solutions to overcome barriers, such as joining in ASA and other society activities. Cai focused on new researchers in academia and gave practical advice for research and journal publications based on his experience as past editor of the Annals of Statistics. Mehta emphasized presentation and leadership skills that are fundamental to entrepreneurship. He brought in the perspective of Cytel employees. Jang emphasized collaborative skills and that clients define the true value of statisticians. He also described various career stages of the Asian statistician. Kaur weighed in on communication and leadership skills and shared anecdotes from her journey.

                Typically, the early struggle for Asian statisticians includes language and cultural barriers, along with the general fight for survival.

                Sage Advice

                Volunteer Your Time

              • Join committees of statistical societies.
              • Organize sessions at conferences.
              • Show up at other peoples’ presentations.
              • If chairing sessions, prepare questions in advance.
              • Look for apprenticeships and internships.
                Find a Mentor and Network

              • Get involved in professional societies.
              • Find a local mentor. Receiving mentoring and reinforcement from senior members of the profession helps.
                Learn to Communicate

              • Talk in English at professional meetings, especially when other colleagues do not speak your native language.
              • Socialize with locals
                • —Don’t hang out solely with colleagues from ‘back home.’
              • When English is not the mother tongue, focus on the following:
                • —Accent reduction, while acknowledging that good communication is not merely accent reduction
                  —Writing style; join writing workshops
                  —Logical presentation
              • Give talks
                • —Seek feedback.
                  —Practice and do dry runs.
              • Write papers
              • Learn to be influential
                • —Speak up at meetings.
                  —Know when to intervene.
                  —Overcome a sense of inferiority.
                  —Realize that technical prowess alone is not sufficient.

                Partner with Colleagues and Collaborate

              • Partner with colleagues whose knowledge and skills complement your own.
              • Be a team player.
              • Be willing to let go of control.
                Publication Pointers

              • Do a good job writing your manuscript. Make sure the submission version is of good quality.
              • Pay attention to the referees’ comments and follow up thoughtfully when responding to journal comments.
              • The good effort of authors does eventually get noticed.
                Imbibe the Culture and Socialize

              • Read novels.
              • Watch movies and sports.
              • Go to mixers and parties.
                Other Professional Tips

              • Have passion for your profession.
              • Understand the big picture and be willing to help outside of your roles/assignments for the quality of the end product.
              • Approach your assignments as if quality of the end product is up to you.
              • Develop a keen understanding of your value in the appropriate context (whether methods or technology).
              • Manage your time well.
              • Develop good judgment
              • Know what you know and what you don’t.

                All these activities can help build confidence, develop social and presentation skills, and increase the chances of finding the right path toward your professional goals.

                Looking Forward

                Statistical societies such as the ASA provide encouragement to young statisticians by offering opportunities to network and enhance statistical skills when attending conferences, workshops, and other statistical activities. Determining the specific needs of statisticians with an Asian background is an important first step to paving the way for further discussion and development of future statistical leaders.

                The ‘Asian Initiative’ taskforce led by Morganstein included two members each from the four societies (ASA, IISA, ICSA, and KISS). They identified several actions that might enhance further growth of Asian statisticians. Two broader areas of early action include career development and leadership skills training and increased involvement of Asian statisticians with the ASA committees.

                This workshop was the first step toward career development and leadership skills training, and there are plans to offer such workshops at future ASA-sponsored conferences such as the Conference on Statistical Practice. The taskforce will continue to evaluate the effectiveness of the planned activities. If you have additional ideas for the taskforce to consider, contact Morganstein at, Kaur at, or Donna LaLonde at

                SPES Hosts Spring Conference, Offers Funding for Speakers

                Fri, 12/01/2017 - 6:00am
                Joanne Wendelberger, Joint Research Conference Chair, and Greg Piepel, Marquardt Memorial Speakers Program Chair

                  The 2018 Joint Research Conference will be held in Santa Fe, New Mexico, June 11–14. This conference is a joint meeting of the Spring Research Conference on Statistics in Industry and Technology, sponsored by the ASA Section on Physical and Engineering Sciences and the Institute of Mathematical Statistics, and the Quality and Productivity Research Conference, sponsored by the ASA Section on Quality and Productivity.

                  Program chairs Xinwei Deng and Brian Weaver are putting together a program with the help of program committee members Anne Hansen, Matt Pratola, C. Devon Lin, and Shane Reese. A website announcement and call for contributed papers and posters is forthcoming.

                  For further information, contact conference chair, Joanne Wendelberger.

                  Funding Available for Speakers on Applied Statistics

                  The SPES Marquardt Memorial Speakers Program facilitates visits of experienced applied statisticians to colleges and universities to give a seminar and meet with students and professors. SPES reimburses the host institution up to $1,000 to cover the expenses of the speaker’s visit.

                  Speakers provide information to students about the following:

                  • What an applied statistician does
                  • How applied statisticians solve problems in science, engineering, technology, and business
                  • What nontechnical skills are required to be successful as an applied statistician

                  The Marquardt Industrial Speakers Program was established by SPES in the early 1990s to encourage careers in applied statistics. If you are an institution interested in having a speaker or a SPES member interested in being on the speakers list (or working directly with a local institution to set up a visit), contact Vaneeta Grover.

                  Tennessee Chapter Introduces High-School Students to Statistics

                  Fri, 12/01/2017 - 6:00am

                  As part of a member initiative award from the ASA, members of the West Tennessee Chapter (WTASA) and graduate students in statistics at the University of Memphis visited Central High School and White Station High School on consecutive Fridays in October to host several sessions designed to introduce local high-school students to the many opportunities in the field of statistics.

                   The initiative consists of the following two parts:
                  • Working statisticians visit local classrooms to share information about statistics careers and their experiences being a statistician
                  • “Bring a Student to Work Day,” in which WTASA statisticians are paired with interested students and invite the students to shadow them for a day or more

                  Dale Bowman (left), who led the program, watches Yu Jiang speak in front of high-school students during the West Tennessee Chapter Bring a Student to Work Day.

                  Hui Zhang, president of WTASA; biostatisticians at St. Jude Children’s Research Hospital; Joyce Jiang, vice president of WTASA; biostatisticians in the school of public health at the University of Memphis; and Dale Bowman, chapter representative and assistant professor at the University of Memphis, visited statistics and applied math classes at the two high schools.

                  Also visiting were WTASA members Fridtjof Thomas from UT Health Science Center, Jun Shi from AutoZone, and Chris Pfeiffer from Tegra Analytics to share their experiences as working statisticians.

                  Jun Shi from the West Tennessee Chapter of the ASA speaks to students.

                  Graduate students in statistics at the University of Memphis Robert Vaughn, John Appiah Kubi, Yunusa Olufadi, Camden Harrell, and Andrew Ott were on hand to give a student’s perspective of studying statistics beyond high school.

                  During the one-hour presentation, students had the chance to ask questions and interact with local statisticians and students as they learned about the various branches of statistics, the average salaries statisticians earn, the skills needed to be a statistician, and the importance of statisticians in the world today.

                  Students are being recruited for the second part of the program, where they will shadow volunteer WTASA mentors to see what a typical statistician’s day looks like. These students will then work with their mentors to prepare a poster highlighting their experience that they will have the opportunity to enter into a poster contest.

                  ASA members and groups are invited to submit proposals for initiatives that support the mission of the association. For information about 2018 initiatives, visit the ASA website.

                  Master’s and Doctoral Programs in Data Science and Analytics

                  Fri, 12/01/2017 - 6:00am
                  Steve Pierson, ASA Director of Science Policy More universities are starting master’s programs in data science and analytics, of which statistics is foundational, due to the wide interest from students and employers. Amstat News reached out to those in the statistical community who are involved in such programs. Given their interdisciplinary nature, we identified programs involving faculty with expertise in different disciplines to jointly reply to tour questions. We profiled a few universities in our April and June issues; here are several more, plus a few PhD programs.


                  Harvard University

                  David C. Parkes is the George F. Colony Professor of Computer Science at the John A. Paulson School of Engineering and Applied Sciences and the co-director of the Harvard Data Science Initiative.


                  Rachel Schutt is lecturer in data science and education program director of Harvard’s Institute for Applied Computational Science.



                  Neil Shephard is professor of economics and statistics and department chair of statistics at Harvard University and the co-chair of the Data Science Education Committee of the Harvard Data Science Initiative.



                  Master of Science in Data Science

                  Year in which first students graduated/expected to graduate: 2019

                  Number of students enrolled: We are anticipating a class of 40–50.

                  Partnering departments: Institute for Applied Computational Science, Statistics, Computer Science, Applied Math

                  Program format: This is an in-person, full-time program. Twelve courses are required, and the degree will typically be completed over three semesters. At least one research experience is required and can be satisfied by a capstone project course or a semester-length independent study project. A final requirement is the presentation of a poster on a data science project at the annual project showcase.

                  Additionally, PhD students in other departments can specialize in data science as a secondary field by completing a selection of course requirements.

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  The basic elements of our curriculum are the following:

                  • Four required technical courses, including our full-year “Introduction to Data Science” sequence and a course in advanced scientific computing
                  • One statistics elective
                  • One computer science elective
                  • One “critical thinking in data science” course
                  • One research experience
                  • Four data science electives that can be satisfied across many departments

                  Our data science curriculum was developed as a joint effort between the statistics department and computer science working with the Institute of Applied Computational Science (IACS) within Harvard’s Engineering School. We already had in place a master’s in computational science and engineering program (run by IACS), which is now in its fifth year, and were attracting many students who placed into data science positions, so we wanted to expand our offerings to include a second master’s program in data science.

                  Additionally, we already had in place a one-year Introduction to Data Science course—now in its fourth year—jointly taught by IACS, statistics, and computer science faculty. This is a survey of all topics we think are essential to data science: the data science process, machine learning, data visualization, statistical inference, algorithmic and computational thinking, experimental design, best practices in coding, and ethics and algorithmic accountability. This course maps very well to a master’s curriculum and is one of the core required elements of the program.

                  We have a faculty standing committee, which includes faculty from across disciplines. We have an advisory board that includes industry experts from Google, Microsoft Research, and other leading companies and national laboratories. We met with them in a full-day event to review the curriculum of the new master’s program, clarify learning objectives, and understand employer needs.

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  Data science is a rapidly emerging field. It includes a hybrid set of skills and thinking from statistics and computer science, along with new methodologies and skills not traditionally taught in universities. Students typically graduating from statistics programs or computer science programs were not being exposed to ideas from across disciplines. We needed to train students whose thinking could transcend departmental boundaries, as well as develop a new curriculum that gives students the foundations in these new skills (statistics at scale, experimental design at scale, algorithms for large data sets, building data products in production, and algorithmic accountability and ethics). A new curriculum needed to be created to train students with the foundation and discipline to be leaders in industry and research.

                  How do you view the relationship between statistics and data science/analytics?

                  The field of statistics is foundational for data science.

                  What types of jobs are you preparing your graduates for?

                  We are preparing students with computational and analytical skills to get jobs across sectors and disciplines. Students are able to work at technology companies like Facebook and Google; media companies like BuzzFeed and The New York Times; and finance companies like Morgan Stanley, Two Sigma, and Citadel. They also are able to work in start-ups, ed-tech, fin-tech, marketing, and consulting.

                  A few students each year go on to PhD programs in robotics, computer science, statistics, neuroscience, and business.

                  What advice do you have for students considering a data science/analytics degree?

                  If you want to build a computational and statistical foundation that can be applied across multiple application areas (e.g., finance, genetics, marketing), this is a good area to get into. If there is already an application area you are interested in, you should examine whether it may be better for you to deeply specialize in that area. However, we try to construct the program so as to build a strong foundation while giving students the flexibility to go deeper into their areas of interest through the electives.

                  Describe the employer demand for your graduates/students.

                  We don’t yet have any graduates for the master’s in data science. But, for our master’s in computational science, all our students get jobs before graduation. They tend to have multiple offers to consider. We’re highly selective in who we admit in the first place. Many students tend to have formed relationships with potential employers during the course of the program through internships and capstone projects.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  Collaboration and a shared vision between the statistics department and computer science was essential. When you start with the premise that you want to create the best educational experience that you can for students, then organizational politics becomes secondary. While there sometimes are additional bureaucratic hoops when more than one department is involved, these aren’t insurmountable.

                  Part of the success of the program is due to having the Institute for Applied Computational Science already in place, with a full-time staff dedicated to administering these master’s programs and working to create a holistic experience for students, including advising, mentoring, community-building, job talks, seminars, and conferences for faculty and students.

                  The University of British Columbia

                  Milad Maymay earned his BS at The University of British Columbia. He has more than 17 years of experience managing projects and programs in both the nonprofit and public sectors.


                  Giuseppe Carenini has been teaching artificial intelligence, machine learning, and natural language processing for more than 10 years. He is also collaborating with companies that aim to make data more useful in supporting complex decisions (Compass) and for public engagement (Metroquest).


                  Paul Gustafson earned his BS in mathematics and master’s in statistics from The University of British Columbia and his PhD in statistics from Carnegie Mellon University. Gustafson also won the CRM-SSC Prize and is a Fellow of the ASA.



                  Master of Data Science

                  Year in which first students graduated/expected to graduate: 2017

                  Number of students enrolled: 45

                  Partnering departments: Departments of Statistics and Computer Science

                  Program format: 10-month professional master’s program, in-person, 30 credits, two-month capstone project

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
                  The UBC Master of Data Science is a professional program harnessing the combined expertise of the UBC departments of computer science and statistics. It helps meet the growing need for people who can apply computational and statistical techniques to data and then effectively communicate results from analyses to various stakeholders.

                  Using descriptive and prescriptive techniques, students extract and analyze data from both unstructured and structured forms and then communicate the findings of their analyses in ways that promote informed decisions based on data. Multidisciplinary in nature, the Master of Data Science program enables graduates to span both the statistical and computational perspectives. The curriculum, informed by consultations with local industry, takes a scientific approach to use data to explore different hypotheses.

                  The program includes 24 one-credit courses offered four at a time, in four-week segments. Courses are lab oriented and delivered in-person. Graduates can appropriately select and tailor data science methods to deal with diverse data types (numerical, categorical, text, dates, graphs, etc.) across diverse subject-area domains.

                  The program also includes a two-month (six-credit) capstone project, allowing students to work alongside their peers with real-life data sets. In this project, students determine questions of interest for the data in conjunction with mentors drawn from academia, industry, and nonprofits. Students experience the complete data science value chain, applying techniques they have learned to investigate the questions relevant to the mentors.

                  Although professional experience is desired, it is not a mandatory requirement.

                  Students have backgrounds across a wide range of fields, including biology, business, engineering, and the social sciences.

                  Prerequisites include the following:

                  • One course in programming
                  • One course in probability and/or statistics
                  • One course in calculus or one course in linear algebra (completion of a course in both calculus and linear algebra is recommended)

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  In every domain, from health care and e-commerce to utilities and gaming, a staggering amount of data has led to a new field and an unprecedented demand. There is a growing need in many fields (especially in western Canada, the Pacific North West, and Silicon Valley) for people who can apply computational and statistical techniques to data and then effectively communicate results from analyses to various stakeholders.

                  Students’ reactions to the program have been extremely positive. Almost all the students in our first cohort—which completed in June 2017—are employed, and more than 500 candidates applied for September 2017 entry to the program.

                  How do you view the relationship between statistics and data science/analytics?

                  In this program, and generally on this campus, data science is viewed as being built upon a solid foundation in statistics and a solid foundation in computer science.

                  What types of jobs are you preparing your graduates for?

                  The extraordinary thing about data science is that our graduates can work in almost every industry and sector—government, education, health care, consulting, tourism, and technology. Depending on the background and experience of the students prior to the program, our graduates get jobs in entry- to mid-level positions as data scientists, data architects, or data analysts. Our graduates are working for organizations such as Microsoft, Electronic Arts, Visier, The University of British Columbia, and Translink (Metro Vancouver’s public transportation authority).

                  What advice do you have for students considering a data science/analytics degree?

                  Data scientists need to be familiar and comfortable with a variety of skills and tools. That is why our program is almost an equal combination of core computer science concepts and tools (about one-third of the courses), core statistics concepts and tools (another third), and more emerging and data science–specific topics (the final third). Students should be aware of this prior to choosing a career in data science. Those who prefer more programming and are less interested in interacting with domain experts should consider big data programs or computer science programs. But those who prefer variety in their day-to-day work or prefer to be generalists can consider a career in data science.

                  Describe the employer demand for your graduates/students.

                  The demand for our graduates/students is significant. We organized more than 15 employer/industry talks for our first student cohort. Furthermore, we received numerous job postings and requests from industry partners to participate in our capstone course. The demand seems to be increasing even further this year.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  The program benefits from great collaboration between the department of computer science and the department of statistics. Drawing a program co-director from each department and having about a 50-50 split in resources and responsibilities has proved effective. Each department has pride and “skin in the game” when it comes to making this program successful.


                  Texas A&M University

                  Simon Sheather is the academic director of the MS (analytics) program at Texas A&M University. From 2005–2014, he served as professor and head of the department of statistics at Texas A&M University.



                  Jon (Sean) Jasperson earned his PhD in business administration with an emphasis in information management science from Florida State University.


                  Master of Science in Analytics

                  Year in which first graduated: 2015

                  Number of students enrolled: 110. Class of 2018 = 45. Class of 2019 = 65.

                  Partnering Departments: Texas A&M Department of Statistics and Mays Business School

                  Program format: The program is 36 credit hours, five semesters long, and taught live online to working professionals on a part-time basis. Full-time employment with at least three years of work experience is required. The program has no thesis option; rather, students bring data from their organization and build predictive models as a graduation requirement. Our work-based capstone project is what makes the program unique. Admissions are cohort-based and only offered in August.

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  Taught in partnership with Texas A&M Mays Business School, the part-time five-semester curriculum in the program consists of 67% statistics and 33% business. The program is taught by distinguished faculty credited with receiving numerous teaching awards at the university level in addition to having countless citations in their field. The lectures use relevant case studies and real-world applications designed to teach students how to apply what they learn immediately. The curriculum was designed to teach students how to apply statistical methods using big data to solve business problems.

                  What was your primary motivation(s) for developing a master’s data science/analytics program?

                  To narrow the gap. The main motivation for developing the program was to help students and companies get a competitive advantage in analytics. The extensively quoted U.S. News and World Report analysis about the growing gap between people with skills to analyze data and those who don’t inspired us to get started. Wanting to be different and still help the cause, our program focused on working professionals and partnering with companies to train and develop their own.

                  To offer a unique experience with a work-based capstone project. The program graduated its first class in 2015, and the reaction since has been strong. Students have valued their experience in the course as first-rate—a true testament to the innovative curriculum, the distinguished faculty who teach, and the strong impact of the work-based capstone project. A significant number of the graduates have received generous promotions from their employers and been asked to expand and optimize analytics in their organizations.

                  Companies also value the program in this aspect, as they have sent more employees from different divisions to enroll after graduating employees in previous cohorts.

                  How do you view the relationship between statistics and data science/analytics?

                  The “buzz” word frenzy. Today, it’s data science. Tomorrow, it’s machine learning. The day after? Who knows? It’s difficult to pin an exact definition of what data science is given its broad reach—and compare it to a set discipline. Statistics/statistician is a “sexy” field/job in itself. The ability to predict with impressive accuracy, using sophisticated modeling and a touch of genius, is a unique skill that not many have. What should the relationship between statistics and whatever the buzz word be? Strong. A firm grasp on one yields astonishing results when used with the other.

                  What types of jobs are you preparing your graduates for?

                  The leaders in analytics. The MS analytics program prepares our students to be leaders and pioneers of analytics in their organizations. Designed for the working professional, our students are not active job seekers—rather, they receive generous promotions. Some have shared that they received promotions to chief data scientist, senior data scientist, and lead performance analyst. Our students are also asked to optimize and expand analytics within their organizations.

                  What advice do you have for students considering a data science/analytics degree?

                  Research. There are more than 150 analytics/data science programs offering degrees, advanced degrees, and certificates. Each one offers unique classes tailored to fit the wants and needs of prospective students.

                  What are your needs? Answer the following questions:

                  • What am I looking for in an analytics program?
                  • How will that specific program meet my needs, both personal and professional—short term/long term?

                  A degree in an area like data science/analytics offers a much broader skill set than traditional computer science/engineering or statistics. With such programs, students receive a healthy dose of both fields, with application capabilities in many areas across many industries.

                  Describe the employer demand for your graduates/students?

                  Strong demand. Since our program is for working professionals, we don’t track placement. But, we do receive a significant amount of inquiries with internship/job opportunities for our students that we share with them.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  Be unique. With more than 150 programs out there and more being created, start with this question: What are the elements that will make your program unique?

                  Build strong partnerships. Survey your corporate contacts who seek to employ graduates and who would collaborate with your department.

                  Keep it fresh. Find ways to keep material fresh in class, materials that keep students engaged with relevant coursework.


                  University of Colorado, Denver

                  Katerina Kechris is an associate professor in the department of biostatistics and informatics at the University of Colorado Anschutz Medical Campus. Her research focuses on the development and application of statistical methods for analyzing high-throughput omics data.


                  Farnoush Banaei-Kashani is an assistant professor in the department of computer science and engineering at the University of Colorado, Denver. His research focuses on developing novel machine learning, as well as data mining and management techniques that enable big data lifecycle.


                  Biostatistics MS Emphasis in Data Science Analytics Computer Science MS Track in Data Science in Biomedicine

                  Year in which first students graduated/expected to graduate: 2019

                  Number of students currently enrolled: 2–3/year under each track

                  Partnering departments: Biostatistics and Informatics, Computer Science and Engineering

                  Program format: The biostatistics MS is an in-person degree with 36 credit hours and a culminating thesis or publishable paper. Students are typically full time and participate in 1–2 years of a research assistantship. The emphasis in data science analytics is a focus area within the MS degree where students take three elective courses (total nine credits) from a list of courses related to data science, of which six credit hours will count toward their MS electives and the other three credit hours are required additional credit hours for the emphasis; hence, a total of 39 credit hours is required for graduation from the emphasis. Students in the emphasis will also write a thesis (or publishable paper) with a focus on data science.

                  The computer science MS is an in-person degree with 30 credit hours, including nine, 15, and six credit hours for core courses, elective courses, and thesis, respectively. Students are typically full time and participate in two years of a research assistantship. The data science in biomedicine track is a focus area within the computer science MS degree where students take three elective courses (total of nine credits) from a list of courses related to biomedicine, of which three credit hours will count toward their MS electives and the other six credit hours are required additional credit hours for the track; hence, a total of 36 credit hours is required for graduation from the data science in biomedicine track. The track graduates will write a thesis (or publishable paper) with a focus on data science in biomedicine.

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  We believe a challenge of interdisciplinary education is finding the right balance of “breadth versus depth.” That is, depth in any one topic may be sacrificed by covering multiple disciplines. On the other hand, the interdisciplinary nature of the program may be compromised if the curriculum is focused too much on one topic. We designed our programs to be parallel tracks within existing degrees so students have depth in one focus area (biostatistics or computer science), and then diversify their skills and expertise in complementary areas with additional classes and thesis research.

                  What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?

                  We developed biomedical-related data science tracks within the existing biostatistics MS and computer science MS degree programs in response to the changing landscape of biomedical research and technology, which is relying more on the generation, archiving, querying, analysis, and interpretation of large data sets. By implementing a new track, students pursuing this training within the respective MS program will have an official designation within their degree. This designation will help with employment and other opportunities such as internships and fellowships, where it would benefit students to show documented training and experience in data science. These tracks were developed in parallel to promote synergy between the departments of biostatistics and informatics and computer science and engineering.

                  How do you view the relationship between statistics and data science/analytics?

                  This relationship has been discussed in great detail on many blogs, editorials, and websites. Without getting into those details, we adapted a popular quote that is a pithy description of data scientists: “It’s the person who is better at statistics than any computer scientist and better at computer science than any statistician” (adapted quote from Josh Wills, director of data engineering at Slack.

                  What types of jobs are you preparing your graduates for?

                  This is the first year of our tracks, so we do not have job placement data yet. But the curricula are designed so graduates will be prepared to work in biomedical sectors (academic, public, and private) that require the analysis of large and diverse data sets from high-throughput omics, imaging, biomedical sensors, and electronic health records.

                  What advice do you have for students considering a data science/analytics degree?

                  We believe it is important to find a program that stresses interdisciplinary education, where students gain knowledge and skills in multiple domains from experts in the respective areas. However, a challenge of interdisciplinary education is finding that right balance of “breadth versus depth,” as discussed previously. When researching programs, students should ask faculty about their philosophy for striking that balance. This would give more information about how that interdisciplinary degree is different than pursuing related degrees such as computer science or (bio)statistics.

                  Describe the employer demand for your graduates/students.

                  In Colorado and nationwide, data science skills within the biomedical field are in high demand in industry (e.g., biotechnology and pharmaceuticals), the public sector (e.g., NIH), and academia.

                  Do you have any advice for institutions considering the establishment of such a degree?

                  Even if there is mutual interest among different departments, creating an interdisciplinary program has practical challenges. In our case, the two departments are on different campuses (~ 8 miles apart), have different tuition structures, and follow different academic calendars. Despite these differences—with support from department and school leadership and good communication between the programs—we were able to overcome these practical challenges.


                  As the ASA reached out to the statistical community for their involvement in starting master’s programs in data science and analytics, we learned about a few doctoral programs we wanted you to know about. We’re grateful to these universities for telling us about their innovative programs.


                  University of Wisconsin-Madison

                  Karl Broman, a professor in the department of biostatistics and medical informatics at the University of Wisconsin-Madison, is an applied statistician focusing on the genetic dissection of complex diseases in model organisms. He was named an ASA Fellow in 2016.


                  PhD Data Science Analytics, Biomedical Data Science PhD

                  Year in which first students graduated/expected to graduate (or year of first class for PhD program): 2018

                  Number of students enrolled: Expect 6–10 per year

                  Partnering departments: Biostatistics and Medical Informatics

                  Program format: In-person; 51 credits; thesis; traditional full time

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  Our biomedical data science PhD program integrates the two related disciplines of biostatistics and biomedical informatics. Students select three year-long sequences from a set of core topics, including at least one biostatistics sequence (for example, biostatistics methods) and at least one computer science/informatics sequence (for example, artificial intelligence/machine learning). Students will also complete at least six credits of biology coursework (such as genetics and genomics) and three semester-long research rotations concerning substantive problems in biomedical data science and advised by a computational faculty member in collaboration with a faculty member from the biological, biomedical, or population health sciences. Cohort cohesion is developed through an in-depth second-year reading course—in which a selection of seminal articles in (bio)statistics, computer sciences, and biology is deconstructed—and a third-year professional skills course. Prerequisites include courses in calculus, linear algebra, programming, and data structures, but students will be admitted with a portion of these and can complete the remainder during their first year in the program.

                  What was your primary motivation(s) for developing a doctoral data science/analytics program? What’s been the reaction from students so far?

                  The primary motivation for our program was to synthesize training in biostatistics and biomedical informatics (which our department has done at the faculty level for 20 years) to provide students with broad computational skills, along with knowledge in an area of biomedical science, so they can make sense of the complex, high-dimensional data that is now the norm in biology, biomedical research, and public health policy. We are recruiting our first class of PhD students, who will enroll in fall 2018.

                  How do you view the relationship between statistics and data science/analytics?

                  Statistics, as a field, is sometimes viewed as being restricted to the use of probability theory for formal inductive inference and the quantification of uncertainty in such inference. But applied statisticians’ work has always included a broad set of activities, including data management, data cleaning, data visualization, statistical computing, software development, and communication about data. In some ways, the term data science does a better job of capturing these activities, including other techniques such as machine learning, that have been developed by computer scientists. I would like statistics to be viewed more broadly, to include the full spectrum of problems that one must confront when seeking to make sense of data. However, it is important to both recognize and take advantage of the many important data science ideas that have arisen outside of statistics.

                  What types of jobs are you preparing your graduates for?

                  Students graduating from our program will be prepared for academic positions and positions in industry and government. Our program includes a professional skills seminar through which students will explore and prepare for the range of employment opportunities so they can make informed career choices and be ready to carry out a successful job search.

                  What advice do you have for students considering a data science/analytics degree?

                  Find some data—perhaps a friend’s data—and dig in! Focus first on data visualization, but always have specific questions you’re seeking to address. It’s easier to acquire and develop your computing and data analysis skills if you have specific challenges in mind.

                  Describe the employer demand for your graduates/students.

                  It’s clear there’s great demand for skilled data scientists. It also seems clear that employers focus not so much on skills in statistical theory as on communication, ability to work in an interdisciplinary team, and more general problem-solving abilities.


                  University of Central Florida

                  Liqiang Ni is an associate professor of statistics in the department of statistics at the University of Central Florida. His main research interests include dimension reduction, multivariate analysis, actuarial science, and business intelligence. He has served as the graduate coordinator since August 2017.


                  Shunpu Zhang is a professor of statistics and chair of the department of statistics at the University of Central Florida. Under his leadership, he and his colleagues created this new PhD program in big data analytics to spearhead UCF’s effort to meet the big data challenge in 2017.


                  PhD in Big Data Analytics

                  Year in which first students graduated/expected to graduate (or year of first class for PhD program): Fall 2018

                  Program format: In-person; 72 credit hours beyond a bachelor’s degree, with up to 30 hours transferable from a completed master’s program in statistics, computer sciences, or mathematics; dissertation; full-time students; graduate teaching/research assistantship available

                  Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.

                  We require the incoming students for our PhD program in big data analytics to have a bachelor’s degree in statistics, mathematics, and computer science with experience in at least one programming language. The curriculum includes advanced statistics, big data architecture such as distributed storage and processing, data mining, machine learning, and other recent developments in big data analytics. These courses will be taught (or jointly taught) by statisticians, computer scientists, and experts from the industry. We expect our students to possess the following core skills: data management; algorithm development; programming in R and Python; statistical inference; and communication and data visualization.

                  Traditional PhD programs in statistics aim to train students to analyze small to medium size, structured data. Our curriculum will focus on big data analytics. It is to train researchers with a statistics background to analyze massive structured or unstructured data to uncover hidden patterns; interesting, actionable associations; and other useful information for better decision-making. In addition to statistical inference and software, the new program has an interdisciplinary component that combines the strength of statistics and computer sciences.

                  What was your primary motivation(s) for developing a doctoral data science/analytics program? What’s been the reaction from students so far?

                  Started in 2001, the department’s data mining program (MS level) is the oldest such program in the United States. In recent years, we have seen a growing need for an educated and talented workforce of data scientists above the MS level who can contribute to industry, government, and academia through innovative applications of data analysis methodologies. The PhD program we are developing pursues high levels of community and business engagement. Our PhD Industry Advisory Board consists of member-participants from CFE Federal Credit Union, Sodexo, UCF Institute for Simulation and Training, the Walt Disney Company, Johnson & Johnson, CitiGroup Inc., iCube Consultancy Services Inc., UniKey Technologies, SAS, and Health First. The board continuously provides feedback on industry-driven competencies.

                  How do you view the relationship between statistics and data science/analytics?

                  We believe statistical science is an integral part of data science/analytics. A good data scientist/data analyst must be a good statistician, regardless of the label or title.

                  What types of jobs are you preparing your graduates for?

                  We are preparing graduates for academic/research institutions, industry, and government.

                  What advice do you have for students considering a data science/analytics degree?
                  Be open-minded and always ready to learn something new. You do not have to choose between hiking the mountain (statistics) and swimming the ocean (computer science, etc.). You can do both.

                  Describe the employer demand for your graduates/students.

                  Data scientists are in short supply, and the compensation for data scientists is very high. A McKinsey study predicts that, by 2018, the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Data scientists can earn a base pay of $116,840 (Glassdoor, 2016) and an average base salary of $113,436 (Forbes, 2016).

                  Do you have any advice for institutions considering the establishment of such a degree?

                  A big data analyst needs to have the ability to use existing methods or develop new methods to uncover true information from enormous amounts of data. To do so, a program in data science needs to provide students rigorous training in big data structure, programming skills, statistical methodologies, algorithm development, and interpreting and communicating the information discovered in the data. We believe such a goal can only be achieved by developing a cross-department joint program, instead of an “add-on” program that only requires students to take a few courses from other disciplines.

                  What Does Doug Splitstone Like to Do When He Is Not Being a Statistician?

                  Fri, 12/01/2017 - 6:00am

                  Doug Splitstone

                  Who are you, and what is your statistics position?

                  The framed certificate on the wall of my office recognizes Doug Splitstone’s 50th anniversary of membership in the ASA. This year marks 52 years.

                  I began my career as a professional statistician designing metallurgical and chemical experiments and performing subsequent data analyses at U. S. Steel (USS) Research. In late 1969 or early 1970, I was asked, “What number should U. S. Steel agree to in negotiating a US Army Corp of Engineers wastewater discharge permit?” The answer to that question started me on a career odyssey that continues today. It also fixes my career of dealing with problems of the environment as predating the US Environmental Protection Agency, founded in December 1970.

                  It was during my tenure with USS that I also learned the intricacies of atmospheric dispersion modeling and the monitoring of air quality. Dealing with water and air discharges necessitated teaching myself the techniques of time series analysis.

                  Also during that period (1974), I was fortunate enough to be a member of the founding group and first chair of the ASA’s ad hoc Committee on Environment Guidelines and Standards. That ad hoc committee later became the Continuing Committee on Statistics and the Environment and has now morphed into the Section on Statistics and the Environment.

                  Piper Warrior II (Photo courtesy of Doug)

                  With the downsizing of the steel industry in the early 1980s, I joined—in succession—two major environmental consulting firms. This required coming to grips with the statistical issues of dealing with hazardous waste and radioactive contamination. Estimation of the extent of contamination and volumes of contaminated material required becoming knowledgeable in geostatistical techniques, as well as the sampling theory of Pierre Gy.

                  Twenty-four years ago, I went into independent practice. My client base largely remains major industry, their environmental consulting firms, and/or their outside counsel. I have also had the pleasure of serving on several panels of the USEPA’s Science Advisory Board.

                  Doug flying—Doug’s son was his first passenger and took the photo.

                  Outside of the environmental arena, I have provided statistical support for the calorimetric calibration of a small nuclear reactor and completed several PhD dissertations in education and one in marriage counseling.

                  Those who have stuck with the above narrative might get the impression that my whole career has been one of learning about new things in order to help clients solve their problems. I hasten to add that my largest contribution is often getting a client to adequately define their problem.

                  Tell us about what you like to do for fun when you are not being a statistician.

                  I fly airplanes. Usually a single-engine Piper Warrior II (PA28-161).

                  What drew you to this hobby, and what keeps you interested?

                  The short answer is the urging of my son, who has been a licensed private pilot for many years. I took my first flight lesson at age 64 and earned my private pilot’s license at age 69. And it is just a neat thing to do.

                  Becoming a pilot requires continual learning and practice to maintain skills. In addition to continued education to become a better pilot, at 74 years young, I am pursuing an instrument rating so I can fly through clouds, as well as below and above them. Flying also provides the opportunity for a total escape. There is only one thing to think about when flying an airplane, and that is flying the airplane. Everything else has to wait until you are back on the ground and the plane is in the hangar.

                  What I Learned at the Women in Statistics and Data Science Conference

                  Fri, 12/01/2017 - 6:00am
                  Donna LaLonde, ASA Director of Strategic Initiatives and Outreach

                    “The Women in Statistics and Data Science (WSDS) Conference is a great venue for young, female statisticians like myself to build and strengthen connections.” ~ Comment from a graduate student who attended WSDS

                    Tag Yourself!

                    Throughout the conference, we asked attendees to describe in a word or two—or 12—what WSDS means to them. Visit the ASA’s Facebook page  to view all the photos, and please tag yourself!

                    Donna LaLonde

                    A young colleague who was not able to attend the Women in Statistics and Data Science conference (WSDS) asked me about it, which made me reflect on my experiences. In this recap, I want to share what I learned. Of course, I want to know what you learned, so please send your comments to me or add comments below.

                    The keynote address was given by Donna Jean Brogan, and the plenary talks were presented by Susmita Data, Jeri Mulrow, and Bonnie Ray. Susmita Datta presented the opening plenary and, through sharing her story, highlighted the themes of the conference—knowledge, influence, and community.

                    During her talk, Jeri Mulrow showed a slide indicating her career path had been nonlinear, which generated laughter and applause. She went on to encourage the audience to “know where you fit.”

                    At the celebration banquet, Donna Brogan described her life of learning and encouraged us to do the same. And as the conference’s closing plenary, Bonnie Ray’s talk, “Questions I Should Have Asked,” provided excellent advice for making career choices.

                    From the different paths pursued by these talented women, I learned that a productive career is a mix of successes, challenges, compromises, and definitely a willingness to take risks.

                    Members of the conference’s executive committee worked tirelessly to plan the conference program and coordinate the travel awards. Throughout the conference, these women continued to focus on ensuring a positive conference experience for all participants. I learned that my talented colleagues are exceptional multi-taskers, generous with their time and energy, and committed to celebrating women in statistics and data science.

                    We have video (this conference was our pilot for livestreaming talks) to celebrate WSDS 2017. Visit the ASA’s
                    (@AmstatNews) web page on Periscope to view the talks.

                    “I’m always more excited than nervous to give talks because I’m so interested in how well the material I prepare will be received.” This comment was shared with me from one of the 2017 Travel Award recipients. I suspect it captures the feelings of all the presenters, regardless of their career stage.

                    Again this year, the more than 400 conference participants had the opportunity to attend technical and professional development presentations, panel presentations, and speed sessions. Presentations included “Statistics in the Courts” by Alicia Laura Carriquiry, “Visualization of Large-Scale Simulations for Scientific Discovery” by Joanne R. Wendelberger, “Move Out of the Comfort Zone and TextM for Crowdsourcing” by Jiayang Sun, and “Navigating Minefields to Claim the Right to Exercise Creative Thinking” by Nancy Flournoy.

                    A range of issues were also explored during invigorating panels, including “Call Her Madam President: A Discussion with Women Leaders in the Statistics Community,” moderated by Kim Sellers with panelists Scarlett Bellamy, Anne Lindblad, and Nalini Ravishanker, and “‘Too Young’ to Lead … but Stepping Up Anyway: Shifting the Leadership Pendulum in (Bio)Statistics and Data Science,” moderated by Emma Benn with panelists Leslie Ain McClure, Merlise A. Clyde, Dionne L. Price, and Elizabeth A. Stuart. View the complete program online.


                    Dalene Stangl gives her concurrent session talk, “Power Dynamics in the Classroom: The Influence of Gender and Consequences for Promotion.”

                    Jeri Mulrow of the Bureau of Justice Statistics answers questions after her talk: “Statistical Leadership in the Federal Government.”

                    In addition to the formal program, participants had the option of participating in a pre-conference short course. This year, the courses were Calibrating Your ‘GPS’ (Growth in Professionalism – Strategies), taught by Rochelle Tractenberg, and Writing R Functions for Fun and Profit, taught by Jenny Bryan. From all the amazing presentations and the informal conversations, I learned a lot!

                    The first WSDS hackathon for service focused on the problem of human trafficking. Planning for the hackathon began shortly after WSDS 2016, with Samantha Chiu contributing many hours to its development. Of course, the sponsorship from Microsoft, including T-shirts, was great. The teams presented their work during the closing session, and the audience affirmed that the projects were amazing.

                    Lucy D’Agostino McGowan, Mine Dogucu, and Jacquelyn Neal scraped the web to present a preliminary analysis of the California legislature’s efforts related to human trafficking. The team of Nada Abdalla, Jingchen Hu, Yongmei Huang, Priya Kohi, and Yun You completed a preliminary analysis of trafficking trends by country and prosecutions by year.

                    Building on their backgrounds in econometrics, Jessica Dutra and Fulya Ozcan completed a preliminary analysis of human trafficking as repugnant markets.

                    The team from NC State harnessed Twitter data to inform anti-trafficking policy. Team members Katherine Allen, Iris Bennett, Jocelyn Cui, Lili Wu, and Joyce Yu developed a website as part of their project.

                    Aziza Salako, competing in her first hackathon, presented her project investigating the potential connection between child homelessness and trafficking.


                    Hackathon participants

                    Everyone agreed all the entries were “winning,” so each team will designate an organization and a donation will be made in its name. I learned first attempts can exceed expectations and set a high bar for future events.

                    Photos by ASA Graphic Designers Sara Davidson and Meg Ruyle

                    Applications Sought for ASA Science Policy Fellowships

                    Fri, 12/01/2017 - 6:00am

                    The ASA is accepting applications for its science policy fellowship, which will last for 1–2 years. The selected fellow will be based at the ASA headquarters in Alexandria, Virginia; however, they will spend the bulk of their time in Washington, DC, advocating for statistics and experiencing first-hand how federal science policy is formed.

                    Applications are due by December 31, but the ASA will consider high-quality applications until the position is filled.

                    The fellowship was created to elevate the profile of statistics in policymaking and advocate on behalf of the profession. Amy Nussbaum was the ASA’s inaugural science policy fellow. She represented the ASA at meetings from the National Academies to Capitol Hill and introduced her own member of Congress to climate scientists. Among the many projects she worked on were the documents “Guidance on Statistical Evidence in Legislation” and “Recommendations to Funding Agencies for Supporting Reproducible Research.”

                    Visit the ASA’s website for more information about this opportunity. Questions about this opportunity and application requirements may be directed to ASA Director of Science Policy Steve Pierson.

                    View a video about the ASA Science Policy Fellowship and learn more about Amy Nussbaum’s experience.