AmstatNews

Subscribe to AmstatNews feed
Updated: 1 hour 42 min ago

People News August 2018

Wed, 08/01/2018 - 7:00am
Mike West

The second Akaike Memorial Lecture Award will be presented to Mike West of Duke University during the plenary session of the Japanese Joint Statistical Meeting 2018, which will take place at the Korakuen Campus of Chuo University on September 10.

The award, jointly created by the Institute of Statistical Mathematics (ISM) and Japan Statistical Society (JSS), aims to encourage the education of talented young researchers by recognizing those who have achieved outstanding accomplishments that contribute to the field of statistical sciences. It celebrates the outstanding achievements of the late Hirotugu Akaike.

West’s contributions to Bayesian statistics include seminal work in dynamic modeling and the implementation of nonparametric models that paved the way for practical data analyses via the first realization of large-scale simulation-based methods. West has also worked at the frontier of various research fields to which Bayesian statistics can be applied and contributed to the creation of data-driven sciences. For example, he established a new approach for biomarker discovery using gene expression data, thus creating a novel trend in -omics biology based on data analysis.

Read more about West’s work and the Akaike award.

Noel Cressie

Noel Cressie, distinguished professor at the University of Wollongong, was recently named a Fellow of the Australian Academy of Science. He was inducted into the academy May 22 in Canberra, Australia.

The academy’s citation associated with his induction reads: 

“Noel Cressie is a world leader in statistical methodology for analyzing spatial and spatio-temporal data, and its applications to environmental science. His fundamental contributions changed the basic paradigm for analyzing observations in space and space-time. Cressie has also contributed to research on pollution monitoring, climate prediction, ocean health, soil chemistry, and glacier movement and is a NASA science team member for the Orbiting Carbon Observatory-2 mission. Responding to the huge volumes of complex data in environmental research, Cressie has made ground-breaking innovations for ‘big data analytics’ for remote sensing and climate change.”

The American Statistician Highlights: August Issue Spans Methodology, Applications

Wed, 08/01/2018 - 7:00am

Daniel Jeske, The American Statistician Executive Editor

The August 2018 issue of The American Statistician features eight articles and two letters to the editor that span a wide range of interesting methodology and application areas. As usual, there is something for everyone in this issue.

The General section begins with an article about the construction of minimum volume confidence sets for the parameters of a shifted exponential distribution (also known as the two-parameter exponential distribution). A second article proposes a new test for detecting general forms of serial temporal dependence.

We have three articles in the Statistical Practice section. First, an article presents a structural equation modeling approach for analyzing a spatial regression model for situations in which there is spatial influence on both the response and covariate variables. The second article in this section presents a semiparametric Bayesian model for analyzing homerun production of Major League Baseball players. The final article details a random graph model for studying citation patterns within the causal inference literature.

We have one article in the Teacher’s Corner that focuses on a variety of thought-provoking insights on the Wilcoxon-Mann-Whitney test. In the Short Technical Note section, you will find a probabilistic proof of an interesting binomial coefficient identity. The Interdisciplinary section is represented by an in-depth analysis of if and how the proportion of gun-related suicides can estimate gun prevalence.

The issue concludes with two letters to the editor that provide comments about a recent article that investigated the use of interpolated nonparametric confidence intervals for population quantiles.

Stats4Good: STATtr@k and Data for Good: A Perfect Combo

Wed, 08/01/2018 - 7:00am
This column is written for those interested in learning about the world of Data for Good, where statistical analysis is dedicated to good causes that benefit our lives, our communities, and our world. If you would like to know more or have ideas for articles, contact David Corliss.

With a PhD in statistical astrophysics, David Corliss works in analytics architecture at Ford Motor Company while continuing astrophysics research on the side. He serves on the steering committee for the Conference on Statistical Practice and is president-elect of the Detroit Chapter. He is the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods to issue-driven advocacy in poverty, education, and social justice.

STATtr@k 411
– Includes sections for awards and scholarships, getting in touch with ASA chapters, career support, and resources

– Updated monthly

– Search function, including archives

Submit articles

Membership in the American Statistical Association offers many benefits: chapter membership, education and collaboration opportunities, great magazines, and much more. One of the most important resources for students and people just getting started in a career in statistics is STATtr@k. Describing itself as “a website produced by the American Statistical Association for individuals who are in a statistics program, recently graduated from a statistics program, or who recently entered the job world,” it is much more than a website. Under the care and guidance of ASA staff, STATtr@k provides access to an extensive collection of resources for the early-career statistician.

Content and Resources

From mentoring programs to articles with valuable career advice, to information about applying for scholarships and fellowships, to educational opportunities and students in the news, STATtr@k is a hub for early-career development information. You can find out about hackathons and learn from others’ experiences to improve your performance; get the skinny on conferences and what they offer for early-career development; and learn about the work of local chapters, ASA sections, and other organizations such as the National Institute of Statistical Sciences (NISS). With so much information and resources available, STATtr@k’s search function might be the most important part.

One of the most valuable aspects of STATtr@k is the opportunity to gain writing experience and exposure by submitting articles. You can share your story, experiences, opportunities, and useful information for students and early-career statisticians. For example, I had the opportunity to interview Megan Price from the Human Rights Data Analysis Group for the upcoming September issue of Amstat News, which focuses on careers in statistics. Because our talk focused on her career as a human rights statistician, this interview will appear on STATtr@k, as well.

STATtr@k’s support for people early in their career makes it a resource for the entire statistical community, so everyone can participate. It’s a perfect channel for senior statisticians and leaders in the statistical community to share experiences, resources, and opportunities with others just getting started.

STATtr@k and Data for Good

Looking to get started in Data for Good projects? STATtr@k is a great place to research opportunities, learn about what others are doing, and connect with the project that meets your interests and develops your skills. It’s also a great place to let people know about your Data for Good projects and recruit newcomers to your important work. Using the search function (it’s in the upper right corner of every page), look for the subjects and opportunities that interest you most.

For example, a search on “social good” will turn up information about scholarships, student fellowships, new undergraduate programs in data science, and an article about how internships helped four students make a difference. A search on “justice” will connect you with volunteer opportunities, the ASA’s Research Experience for Undergraduates (REU) program, best practices for developing an open data portal, and feature articles like “The Local ASA Chapter Is My Justice League”, in which Scott McClintock described his ASA chapter—in Philadelphia—as his “Justice League,” where collaborators use their “statistical super-heroism for the greater good.” The list of resources and possibilities for good work goes on and on. If you don’t find information about a program, it means you can write about it and let STATtr@k staff know so others can benefit from your experience.

The mission of the ASA can be summed up as doing good statistics, doing good for statistics, and doing good with statistics. The wealth of resources STATtr@k offers early-career statisticians, used to support Data for Good projects, meets these objectives at once. My highest hope for Data for Good, in this column and elsewhere, is to see it become normative—a natural, ordinary part of a career in statistics. That starts from the beginning, and so naturally connects with STATtr@k’s mission to provide resources for early-career statisticians.

We all have a role to play in developing the statistics—and the statisticians—of the future. That means we all can be users, even contributors, to STATtr@k’s work to support students, recent graduates, and others just starting out. Help make Data for Good an important part of their statistical career. Visit STATtr@k and find your place in moving statistical science forward as a powerful means for doing good in our society, communities, and world.

Symposium on Data Science and Statistics: A Remarkable Success

Wed, 08/01/2018 - 7:00am
ASA Photo/Meg Ruyle Barry Nussbaum, 2017 ASA President, gives the banquet talk, “I Never Met a Datum I Didn’t Like.” eposter2 Attendees look at and discuss e-posters at SDSS 2018. Photo courtesy of Laura Normoyle Photography Keynote speaker Emery Brown discusses the mechanisms of general anesthesia. SDSS Opening Mixer ASA Photo Mian Arif Shams Adnan of Indiana University talks to Marcel Camara of XLSTAT and Rutgers University student Olena Yasinchuk at SDSS 2018. ASA Photo Andrea Roberson presents her poster at SDSS 2018. ASA Photo Attendees look at posters at SDSS 2018. ASA Photo Attendees chat at the SDSS 2018 Opening Mixer. ASA Photo/Meg Ruyle Martha Christino from TC Williams High School presents her poster on Hurricane Irma to Peter Craigmile, a professor at The Ohio State University. Photo courtesy of Laura Normoyle Photography From left to right: Barry D. Nussbaum, 2017 ASA President, Ross McKitrick from University of Guelph in Canada, Emery N. Brown from MIT, Harvard Medical School, Massachusetts General Hospital, and Yasmin H. Said, SDSS 2018 Program Chair and George Mason University, at SDSS 2018. Photo courtesy of Laura Normoyle Photography SDSS 2018 attendees enjoy a session at the conference. Photo courtesy of Laura Normoyle Photography From left to right: Katherine Ensor from Rice University, Alyson Wilson from North Carolina State University, Dave Higdon from Virginia Tech, and Sallie Keller, 2006 ASA President.
    Yasmin H. Said, 2018 SDSS Program Chair

      More than 500 people attended the sold-out 2018 Symposium on Data Science and Statistics in Reston, Virginia, May 16–19.

      The program for this first ASA symposium featured a strong program offering short courses, concurrent sessions, and electronic poster sessions. There also was an exhibit hall and many opportunities for networking. Emery N. Brown gave the keynote address “Uncovering the Mechanisms of General Anesthesia: Where Neuroscience Meets Statistics,” while David Scott, Adalbert Wilhelm, and Jerome Friedman each gave a plenary talk.

      Keynote and Plenary Speakers
      Emery N. Brown is a renowned scholar and member of the National Academy of Medicine, National Academy of Sciences, and National Academy of Engineering. He is an anesthesiologist-statistician whose experimental research has made important contributions to understanding how anesthetics act in the brain. In his statistics research, he has developed signal processing algorithms to study dynamic processes in neuroscience.

      David Scott is the Noah Harding Professor of Statistics at Rice University in Houston, Texas. He was a founding member of the department of statistics in 1987 and its chair. Scott’s talk focused on Edward Wegman’s influence on the profession and his work in computational statistics and density estimation.

      Adalbert Wilhelm holds a professorship in statistics and is the vice dean of the Bremen International Graduate School of Social Sciences at Jacobs University in Bremen, Germany. His talk focused on statistical graphics in data science. He bridged the different visualization aspects from computer science, statistics, and application domains and discussed recent trends.

      Jerome Friedman is a renowned scholar and member of the National Academy of Sciences. He is a professor of statistics at Stanford University and one of the world’s leading researchers in statistics and data mining. Friedman’s talk was titled, “Omnibus Regression: Predicting Probability Distributions with Imperfect Data.”

      Within the invited program were sessions on data science, data visualization, machine learning, computational statistics, computing science, and applications—some standing room only. The short courses, which took place May 16, also were full.

      One of the most popular invited sessions was “Interactive Statistical Graphics: Where Are We Now?” It featured talks by Wayne Oldford (“Exploratory Visualization via Extendible Interactive Graphics”), Catherine Hurley (“Model Exploration via Conditional Visualization”), and Heike Hofmann (“Interactive Web-Graphics Using R”).

      Some other talks garnering packed rooms included the following:

      Daniele Struppa: “Social Networks and Simplicial Complexes”

      Menas C. Kafatos: “Laws of the Universe, Information, and Mind in the Quantum Universe”

      Kirk Borne: “Exploring and Exploiting Interestingness in Data Science”

      Leland Wilkinson: “Automatic Visualization”

      David Banks: “Cherry-Picking Techniques for Complex Data Sets”

      Edward George: “Bayesian Penalty Mixing with the Spike and Slab Lasso”

      To read about all the sessions and talks, visit the online program.

      The banquet talk, “I Never Met a Datum I Didn’t Like,” was given by Barry D. Nussbaum, the 112th president of the American Statistical Association and chief statistician for the US Environmental Protection Agency. At the banquet, the Interface Foundation of North America and ASA awarded a lifetime achievement award to Edward J. Wegman for his seminal contributions to computational statistics, data visualization, and data science. In 1987, he incorporated the Interface Foundation and has been the treasurer for 31 years.

      This symposium is a continuation of the Interface Symposium on Computing Science and Statistics. The first Interface Symposium was held in Reston, Virginia, in 1988.

      Turing Award Winner, Longtime ASA Member Publishes The Book of Why

      Wed, 08/01/2018 - 7:00am

      Judea Pearl, a longtime ASA member, was interviewed in November of 2012 after receiving the Turing Award from the Association of Computing Machinery. He has recently published a book, The Book of Why: The New Science of Cause and Effect (with Dana MacKenzie), that aims to familiarize the general, nontechnical public with recent advances in causal inference. ASA Executive Director Ron Wasserstein interviews him again here to find out what message he thinks his new book sends to Amstat News readers.

      Judea Pearl

      The Book of Why is making a splash in statistics, as well as in machine learning and other data-intensive sciences. I would like to start with a question that you have probably heard many times: What brought you to write the book?

      I have official and unofficial answers to this question.

      The official answers: First, I have found it both timely and exciting to lay before the public the amazing story of a science that has changed the way we understand scientific claims and yet has remained below the radar to the general public. As we enter the era of big data and machine learning, it is important to share with the public our current understanding of how this new science is likely to affect our lives in the 21st century.

      Second, as a part-time philosopher, I have found it intriguing to narrate the history of statistics as viewed from the special lens of its orphaned sister: causation. The story of this “forbidden love” was never told before and, believe me, it is full of mystery, intrigue, personalities, dogmatic orthodoxy, and heroic champions of truth and conviction.

      Finally, my unofficial reason is to incite a rebellious spirit among rank-and-file statisticians, so the excitement that currently fuels causality research in academia percolates down to education and to practice. In other words, I am impatient with the slow pace at which the tools of causal inference are becoming an organic part of statistical thinking.

      You expressed a similar impatience in our interview six years ago. And you have initiated the ASA Causality in Statistical Education Award to close the growing gap between research and education. Hasn’t this initiative met your expectations?

      It has. But, with age, my impatience grew stronger and less forgiving. Of course, the availability of instructional material made it easier for instructors to introduce aspects of causal inference in graduate courses, but it was not sufficient to change the curriculum of undergraduate classes. Nor was it sufficient to reshape the minds of practicing statisticians or high-profile academics who are too busy to sort out what all the causal inference “hype” is about.

      What The Book of Why is doing can be described as “the democratization of causal inference.” It awakens the untrained students to the realization that “it’s easy and who needs the ‘experts’ and all their quibbles?” As a result, the book is accomplishing what I have failed to achieve in the past 30 years through hard labor and scholarly discussion with the leading statisticians of our time—a mass uprising of common sense.

      I have read that some statisticians find your claims to be “hard to swallow,” especially your characterization of causal inference as “The Causal Revolution” and your depiction of statisticians as antagonistic to causal thinking. Can you comment on these sentiments?

      These are not only sentiments but natural complaints voiced by practicing statisticians who are genuinely surprised by how the history of statistics is viewed from the causal lens.

      Take for instance the mantra “correlation does not imply causation,” which every statistics student has learned to chant, demonstrate, and internalize.

      The Book of Why dissects this mantra to far-reaching conclusions that seem indeed “hard to swallow,” even to seasoned statisticians.

      First, it can be strengthened to assert that no causal conclusion can ever be obtained without some causal assumptions (or experiments) to support the conclusion. This is hard to swallow because it sounds circular, and because if you look at the statistical literature from 1832 to 1974, you will find many ideas about what is needed to substantiate causal conclusions (e.g., Yule, Fisher, Neyman, Hill, Cox, Cochran), but not one causal assumption—at least not formally.

      This raises an interesting question: Why could not these giants of statistics come up with a simple principle, telling us what assumptions are needed for establishing a given conclusion, and let us judge—for any given situation—whether it is plausible to make those assumptions? And here comes the second surprise that is even harder for people to swallow: Even if they knew the needed assumptions, statisticians could not have articulated them mathematically—they simply did not have the language to do so.

      Readers refuse to accept this linguistic deficiency until I ask them to write down a mathematical expression for the sentence, “The rooster crow does not cause the sun to rise.” Failing this elementary exercise drives people to realize a totally new notational system is needed; the beautiful and powerful language of probability theory and its many extensions cannot make up for this deficiency.

      The needed notation first came into being in 1920, when the geneticist Sewall Wright put down on paper a new mathematical object: a causal diagram. Thus, statistics was separated from causality, not by antagonism or disdain, but by a language barrier—the toughest barrier for humans to acknowledge and to cross. Now that the barrier is behind us, it is only natural we should call the crossing a “Causal Revolution.”

      These are interesting theoretical points, but I wonder if they are likely to have significant impacts on the practice of statistics or on statistical education.

      The most significant practical impact of the Causal Revolution would probably be a continuous erosion of the supremacy of randomized clinical trials (RCT) in the development and evaluation of drugs, therapeutical procedures, and social and educational policies. Last year, for example, the editors of one of the two leading medical journals in America stated that authors should not talk about causation unless they have conducted a randomized clinical trial.

      Miguel Hernan of Harvard and several other specialists in public health vigorously protested this restriction, and Hernan wrote, “The biggest disservice of statistics to science has been to make ‘causal’ into a dirty word, the C-word that researchers have learned to avoid.”

      Indeed, considering the practical difficulties of conducting an ideal RCT and its inherent sensitivity to sample selection bias, observational studies have a definite advantage: They interrogate the target populations at their natural habitats, not in artificial environments choreographed by experimental protocols.

      The development of a new toolkit that allows scientists to estimate causal effects from observational studies now opens a wide variety of applications—from medicine to social science to ecology—free from problems of ethics, costs, and external validity that plague randomized clinical trials.

      True, observational studies are necessarily sensitive to modeling assumptions that must be defended on scientific grounds. However, the transparency with which those conceptual assumptions are displayed, coupled with the ability of testing them against data, now make observational studies serious contenders to RCTs.

      I would like to go back to education and ask what you believe would induce a typical statistics instructor to introduce aspects of causal inference in a standard statistics class.

      Curious students who read The Book of Why will make it impossible for statistics instructors to skip such aspects.

      Take for instance Simpson’s paradox, a phenomenon discussed in every statistics class, usually for the purpose of demonstrating that “correlation is not causation.” The discussion usually ends with a song of praise to statistical tables for showing us that the reversal can indeed occur in the data, hence the paradox does not exist. Done. Some instructors go a bit further and praise the table for protecting us from naïve beliefs in miracle drugs that are good for men, good for women, and bad for the population.

      Now imagine an inquisitive student raising his/her hand and asking the very obvious question: So, what do we do if we find Simpson’s reversal in the data? Shall we believe the aggregated data or the disaggregated data? I do not believe any instructor would in good faith be able to evade this question, suspecting the student knows the answer; it takes a few lines to describe. In other words, instructors would not be able to skip the causal implications of Simpson’s paradox, as their professors did to them.

      The same applies to Lord’s paradox, spurious correlations, instrumental variables, confounders, and other causal concepts that were used to embarrass statistics instructors in the past.

      The graphical approach you advocate in the book is but one of several approaches currently used in causal inference. Would a reader versed in potential outcome analysis feel comfortable with your methodology?

      Not only comfortable, but enlightened and liberated. Researchers entrenched in potential outcome analysis will discover, to their amazement, that the following three notorious weaknesses of potential outcomes can easily be overcome:

      • Assumptions of “conditional ignorability,” which currently underlie every potential outcome study, can be made not because they facilitate available statistical routines, but when they are truly believed to hold in the world. They are, in fact, vividly displayed in our model of the world (i.e., the causal diagram), where they can be scrutinized for plausibility, completeness, and consistency.
      • When assumptions of “conditional ignorability” do not hold, it is not the end of the world; the analysis can continue, and causal questions answered using other types of assumptions the model may license.
      • Modeling assumptions need not remain opaque or data-blind; they can be tested for compatibility with the available data, and the model tells us how.

      Making these three bullets available to researchers from the potential outcome camp will break through a wall of cultural isolation and enable them to communicate with the rest of the research community in a common, unified language.

      To summarize, the democratization of causal inference is bringing about a globalization of common sense and a breakdown of cultural barriers. I am gratified to see The Book of Why contributing to this process.

      Modeling Contest Adds Problem, Addresses Energy Use

      Wed, 08/01/2018 - 7:00am
      Stacey Hancock

        Starting in 2016, the Consortium for Mathematics and its Applications’ (COMAP) annual Mathematical Contest in Modeling (MCM) added a data insights problem, Problem C. In this new modeling challenge, teams are presented with a modeling problem and data set. While not a big data challenge, data sets often have interesting characteristics and naturally occurring complicating factors such as missing data, cross-discipline sources, correlated observations, and blends of data types.

        This year’s Problem C addressed energy usage of four contiguous US states: California, Arizona, New Mexico, and Texas. Teams were charged with seeking mathematical models that could assist with policy changes for forming a realistic new energy compact focused on increased use of cleaner, renewable energy sources. Using a data set representing 50 years of energy use with more than 500 variables, teams first developed and described an energy profile for each state, used this energy profile model to assess which state was best, and then used the model to predict the future energy profile in years 2025 and 2050 in each state. Using these results, teams determined renewable energy usage targets and action items for each state and summarized their results in a one-page memo to the state governors.

        More than 4,000 teams participated in Problem C this year: 4,589 from China; 136 from the United States; and several from Canada, Hong Kong, the United Kingdom, Indonesia, India, Macau, Mexico, South Korea, and Taiwan. Six of these teams were designated as Outstanding Winners. Problem author and judge commentaries on team submissions, plus a selection from the Outstanding Winner solution papers, will appear in The UMAP Journal.

        New in 2018, the American Statistical Association is designating one outstanding team as the winner of the ASA Data Insights Award. This year’s winning team is from Xi’an Jiaotong University, China, with adviser Fang Zhang. Winners include Running Hu, Shengkuan Yan, and Minghao Zhou.

        While the MCM has traditionally been aimed at mathematics students, students with statistical skills have a unique advantage on Problem C due to MCM’s data analysis focus. The MCM is open to both high-school students and college undergraduates.

        The 2019 MCM contest is set for January 24–28.

        Biopharmaceutical Section News for August 2018

        Wed, 08/01/2018 - 7:00am

        The ASA-BIOP Nonclinical Biostatistics Working Group (NCBWG) endorsed the creation of a new workstream co-chaired by Phillip Yates of Pfizer Inc. and Katja Remlinger of GSK. The goal of the workstream is to provide information, insight, and networking opportunities for students interested in careers in the nonclinical area and offer a forum for young professionals to more actively engage in ASA-BIOP activities.

        In addition to supporting the Nonclinical Biostatistics Conference, we are interested in pairing industry veterans with nearby academic partners. Contact Yates or Remlinger or visit the NCBWG website for more information or to volunteer.

        Section on Physical and Engineering Sciences News for August 2018

        Wed, 08/01/2018 - 7:00am

        The Section on Physical and Engineering Sciences (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 for up to $1,000 to cover the expenses of the speaker’s visit. The speaker provides information to students about the following:

        • What an applied statistician does
        • How an applied statistician solves 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.

        Detroit, Ann Arbor Chapters Recognize K–12 Poster Winners

        Wed, 08/01/2018 - 7:00am
        Karry Roberts, ASA Detroit Chapter Secretary

          From left: Bob Peterson, Karry Roberts, Kathy Peterson, Jaclyn Golaszewski, Mickala Winne, Samantha Dulz, Rob Kushler, and Violet Fiddes. The three poster winners won an honorable mention in the Grades 7-9 category of the competition for their poster titled, “What’s Up with Binge Watching?”

          From left: Nicholas Moloci, Paavani Tewari, Nicole Pike, Rob Kushler, Anamaria Kazanis, and Karry Roberts at Uriah Lawton Elementary School, Ann Arbor, Michigan. Third-grader Tewari won first place in the Michigan Statistics Poster Competition.

          Members of the ASA Detroit and Ann Arbor chapters recently presented awards for the ASA National Data Visualization Poster Competition and Michigan Statistics Poster Competition to students at their schools.

          For several years, Robert Kushler, a Detroit Chapter board member, has coordinated award presentations for students in southeast Michigan with sponsors of the Michigan event currently led by Dan Adrian at Grand Valley State University (GVSU). Offered as an incentive to sponsoring teachers, members of the Detroit Chapter travel to the schools for a ceremony, rather than just mailing the plaques and certificates. They also present the teachers with a statistical book in recognition of their contributions.

          This year, the chapter held two award presentations. A team of ninth-grade students from Macomb Academy of Arts and Sciences in Armada, Michigan, received an honorable mention at the national level. Violet Fiddes, a mathematics and computer science teacher there, encouraged her students to enter the competition.

          The poster by Samantha Dulz, Jaclyn Golaszewski, and Mickala Winne, titled “What’s Up with Binge Watching?” won an honorable mention award in the Grades 7–9 category of the National Data Visualization Poster Competition. They also won a second-place award at the state-level Michigan Statistical Poster Competition.

          Also, in Fiddes’ class, “Printed vs. Ebooks” by Kayla Whitney and Jessica Jarema received a third-place award and “Do You Know Crime” by Cameron Keller, Jacob Brown, and Brennan McClelland was a national qualifier at the Michigan Statistics Poster Competition.

          Representing the ASA Detroit Chapter Board for this event were Rob Kushler, Kathy Peterson, Bob Peterson, and Karry Roberts.

          During another recognition event at Uriah Lawton Elementary School in Ann Arbor, Anamaria Kazanis, council of chapters representative to the ASA Board; Nicholas Moloci, vice president of the Ann Arbor Chapter; and Kushler and Roberts from the Detroit Chapter presented a first-place Michigan Statistics Poster Competition award to third-grade student Paavani Tewari for “Exploring Public Library Summer Game Data.”

          Arizona Chapter Hosts Judging for ASA National Data Visualization Poster Competition for K–12

          Wed, 08/01/2018 - 7:00am

          Four ASU judges (from left): El-Ham Ismail (ASA student member and winner in recent ASA DataFest at ASU), Jonathan Kurka (ASU instructor), Jennifer Broatch (ASU West Campus assistant professor), and Mickey Mandencido (ASU West Campus assistant professor)

          The Arizona Chapter concluded its academic year of activities by supplying judges for the ASA’s Data Visualization Poster Competition for K–12 students. Hosted by Jennifer Broatch at Arizona State University West Campus, the final national round of judging included 134 posters submitted from the regional competitions. The judges came from both ASU faculty and statistics students, as well as local industry—18 altogether, including coordinator and chapter president Rodney Jee.

          The event not only served to determine the national winners for this annual competition, but also provided an opportunity for students, professors, and industry professionals to exchange views about graphics and statistical ideas.

          Using a recently revised rubric to award the posters on their application of data visualization, the judges often found posters with impressive work (occasionally beyond the AP curriculum) and/or amusing subjects.

          Judging for the nationals rotates throughout the ASA’s chapters. It is very much an “event in a box,” since the regional competitions mail the posters to the national judging site and about a dozen chapter members spend a full day judging before communicating the results to the ASA. The ASA provides the awards to the national winners, coordinates the regional sites with the national judging coordinator, and supports a lunch for the judges.

          People, Ideas Come Together at Risk Analysis Symposium

          Wed, 08/01/2018 - 7:00am
          Alexandra Kapatou
            Stan Sclove of the University of Illinois at Chicago tells old funny stories at the Bernard Harris Memorial Symposium, which honored the first chair of the ASA Section on Risk Analysis. The symposium was held in Raleigh, NC, in May. From left, front row: Edward Melnick, Stan Sclove, David Banks, and Susan Harris; back row: Richard Smith, Clarice Weinberg, Erin Charles, and John Wambaugh RA5_RichardSmith Richard Smith, left, discusses extreme weather events. Seated are Aleka Kapatou, Michael Pennell, and Clarice Weinberg. RA4_Clarise Weinberg_Banks Clarice Weinberg and David Banks discuss risk at the Bernard Harris Memorial Symposium.

              The idea to organize a symposium in honor of Bernard Harris, the first chair of the ASA Section on Risk Analysis, arose a couple of years ago when Susan Harris, Bernard’s widow, presented the section with a gift. The gift indicated it was meant to cover expenses of invited speakers from interdisciplinary areas, like Bernard. What is risk analysis, if not an interdisciplinary area, after all? We imagined an intimate, small conference, where a few invited speakers would have plenty of time to present their research and the audience would have enough time to ask questions and give feedback. It took place May 10–11 in Raleigh, North Carolina.

              The speakers, in order of presentation, included the following:

              • Michael Pennell of The Ohio State University and chair of the Risk Analysis Section gave the opening remarks.
              • Stan Sclove of the University of Illinois at Chicago told us about the life and work of Bernard Harris.
              • Edward Melnick from NYU Stern School of Business began the technical part of the symposium by presenting the foundations of risk assessment.
              • Richard Smith, from The University of North Carolina at Chapel Hill and associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), presented his research on risk of extreme weather events in a changing climate.
              • R. Dale Hall from the Society of Actuaries presented segmentation and decomposition techniques in actuarial risk analysis using predictive analytics.
              • John Wambaugh of the US Environmental Protection Agency presented informatics tools for chemical safety.

              Symposium Organizing Committee Members
              Michael Pennell, The Ohio State University
              Matthew Wheeler, National Institute of Occupational Safety and Health
              Susan Simmons, North Carolina State University
              Alexandra Kapatou, American University
              Maria Barouti, American University
              Qian Li, FDA Center for Tobacco Products
              Piaomu Liu, Bentley University
              Mary Louie, New York Life Insurance
              Edsel Peña, University of South Carolina
              Chris Sroka, New Mexico State University
              Wensong Wu, Florida International University

              The talks were followed with poster presentations by graduate students Taeho Kim and Shiwen Shen from the University of South Carolina and Jun Shepard from Duke University. The first day of the symposium closed with a mixer.

              On the second day, Clarice Weinberg from the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, answered the question: “Can we develop a way to find the multi-SNP contributors to disease risk?”

              David Banks, from Duke University and director of SAMSI, presented methods of adversarial risk analysis. Ilyan Iliev of the University of Southern Mississippi discussed terrorism and social media. Rita Fuller from New York Life Insurance Company gave a talk for the benefit of students on preparing for a job interview in data science. Finally, Matthew Wheeler, from the National Institute for Occupational Safety and Health and past chair of the Risk Analysis Section, gave closing remarks by presenting inspired ideas that put together different areas of risk analysis.

              Alabama Chapter Hosts Miniconference in Mississippi

              Sun, 07/01/2018 - 7:00am

              Chapter officers (from left) Hailin Sang, Jon Woody, Jeff Syzchowski, and Bob Oster with ASA Past President Barry Nussbaum

               

              Student award winners (from left): Justin Leach, Mojtaba Khanzadeh, Ralph Vital, and Zavia Epps

                The Alabama Chapter hosted a miniconference April 6 at Mississippi State University. Approximately 40 current and prospective chapter members attended from Alabama and Mississippi who were affiliated with Mississippi State University, Jackson State University, the University of Mississippi, and the University of Alabama at Birmingham.

                The keynote speaker was Barry Nussbaum, ASA past president. The title of his talk was “The Only Thing We Have to Fear Are the Data Themselves … And That Is Not a Very Big Fear.” The purpose of the presentation was to demonstrate what to do—and what not to do—in terms of the ability of statisticians to succinctly explain results so decision-makers may correctly integrate analyses into their actions. The role of examining the opportunities and pitfalls of big data was a major focus of the presentation. Nussbaum delivered his talk with a mix of seriousness and humor, and his talk was well received by those in attendance.

                Student presenters and their topics included the following:

                • Justin Leach of the University of Alabama at Birmingham, “Penalized Smoothing Splines in Adolescent Growth Studies”
                • Ralph Vital of Mississippi State University, “Goodness-of-fit Test for the Hazard Rate”
                • Mojtaba Khanzadeh of Mississippi State University, “In-Situ Monitoring of Melt Pool Images for Porosity Prediction in Directed Energy Deposition Processes”
                • Zavia Epps of Jackson State University, “Monte Carlo Methodology Using Bootstrapping to Investigate Weight in Motion Data on Bridge Load Rating Factor”

                The chapter also held a business meeting that covered the newly written Alabama Chapter constitution and bylaws, chapter activities, a possible name change for the chapter to recognize that members are from both Alabama and Mississippi, and JSM activities.

                In addition, the chapter held elections for its officers. Jon Woody of Mississippi State University was elected president and Bob Oster of the University of Alabama at Birmingham was re-elected treasurer. Hailin Sang of the University of Mississippi continues his term as secretary. Former President Jeff Szychowski will assume the role of chapter representative. A new vice president will be selected later. Elections for vice president and secretary will be held in 2019.

                Bureau of Labor Statistics’ Wendy Martinez Elected ASA President

                Sun, 07/01/2018 - 7:00am

                Wendy Martinez
                Photo/Studio B

                The membership has voted, and it elected Wendy Martinez, director of the Mathematical Statistics Research Center at the Bureau of Labor Statistics, as the 115th president of the American Statistical Association. Before she takes office as president on January 1, 2020, she will serve as president-elect, beginning January 1, 2019.

                 

                 

                Richard De Veaux

                 

                Members also elected Richard De Veaux, professor of statistics at Williams College, as vice president. De Veaux’s term also begins January 1, 2019.

                 

                 

                Additionally, the ASA membership elected the following:

                • Anamaria Kazanis of ASKSTATS Consulting as the Council of Chapters Governing Board Representative to the ASA Board
                • Mark Glickman of Harvard University as the Council of Sections Governing Board Representative to the ASA Board
                • Mary Kwasny of Northwestern University as chair-elect of the Council of Chapters Governing Board
                • Ofer Harel of the University of Connecticut as chair-elect of the Council of Sections Governing Board

                Following is a list of the entire slate of election results, including officers for each of the ASA’s sections:

                Board of Directors

                President-Elect 2019
                Wendy Martinez

                ASA Vice President 2019–2021
                Richard De Veaux

                Council of Chapters Representative to the ASA Board of Directors
                Anamaria Kazanis

                Council of Sections Representative to the ASA Board of Directors
                Mark Glickman

                Council of Sections Governing Board
                Chair-Elect
                Ofer Harel

                Vice Chair
                Alyson Wilson

                Council of Chapters Governing Board
                Chair-Elect
                Mary Kwasny

                Vice Chair District 4, Region 2
                Jo Wick

                Vice Chair District 3, Region 2
                Ruth Cassidy

                Section on Bayesian Statistical Science

                Chair-Elect 2019
                Marina Vannucci

                Program Chair-Elect 2019
                Surya Tokdar

                Secretary/Treasurer 2019–2020
                Yanxun Yu

                Council of Sections Representative 2019–2021
                Ying Yuan

                Biometrics Section

                Chair-Elect 2019
                Sherri Rose

                Secretary/Treasurer
                Elizabeth Ogburn

                Council of Sections Representative 2019–2021
                Page Moore

                Biopharmaceutical Section

                Chair-Elect 2019
                Bruce Binkowitz

                Program Chair-Elect 2019
                Stephine Keeton

                Publications Officer 2019–2021
                Yongming Qu

                Council of Sections Representative 2019–2021
                Veronica Powell

                Business and Economic Statistics Section

                Chair-Elect 2019
                Adriana Kugler

                Program Chair-Elect 2019
                Mariana Saenz-Ayala

                Secretary/Treasurer 2019–2020
                Maggie Jones

                Section on Statistical Computing

                Chair-Elect 2019
                John Castelloe

                Program Chair-Elect 2019
                Kary Myers

                Council of Sections Representative 2019–2021
                Lucy D’Agostino McGowan

                Section on Statistical Consulting

                Chair-Elect 2019
                Manisha Desai

                Publications Officer 2019–2020
                Joseph Rigdon

                Executive Committee-at-Large 2019–2021
                Robyn Ball

                Council of Sections Representative 2019–2021
                Dean Johnson

                Section on Statistical Education

                Chair-Elect 2019
                Michael Posner

                Secretary/Treasurer 2019–2021
                Jennifer Broatch

                Executive Committee-at-Large 2019–2021
                Jessica Chapman

                Executive Committee-at-Large 2019–2021
                Beverly Wood

                Section on Statistics and the Environment

                Chair-Elect 2019
                Amanda Hering

                Program Chair-Elect 2019
                Ephraim Hanks

                Treasurer 2019
                Henry Scharf

                Section on Statistics in Epidemiology

                Chair-Elect 2019
                Jing Cheng

                Program Chair-Elect 2019
                Alisa Stephens-Shields

                Secretary/Treasurer 2019–2021
                Nicole Bohme Carnegie

                Section on Government Statistics

                Chair-Elect 2019
                Katherine Thompson

                Program Chair-Elect 2019
                Michael Yang

                Publications Officer 2019–2020
                Jenny Guarino

                Secretary/Treasurer 2019–2020
                Tara Murphy

                Council of Sections Representative 2019–2021
                Jennifer Parker

                Section on Statistical Graphics

                Chair-Elect 2019
                Isabella Ghement

                Program Chair-Elect 2019
                Shailaja Surywanshi

                Secretary/Treasurer 2019–2020
                Stefano Castruccio

                Council of Sections Representative 2019–2021
                Ritwik Mitra

                Health Policy Statistics Section

                Chair-Elect 2019
                Laura Hatfield

                Section on Statistics in Marketing

                Chair-Elect 2019
                Lan Luo

                Program Chair-Elect 2019
                Daniel McCarthy

                Secretary/Publications Officer 2019–2020
                Seshadri Tirunillai

                Section on Physical and Engineering Sciences

                Chair-Elect 2019
                Robert Gramacy

                Program Chair-Elect 2019
                Mary Frances Dorn

                Council of Sections Representative 2019–2021
                Kimberly Kaufeld

                Section on Quality and Productivity

                Chair-Elect 2019
                Chris Gotwalt

                Program Chair-Elect 2019
                Terri Henderson

                Section on Risk Analysis

                Chair-Elect 2019
                Elena Rantou

                Program Chair-Elect 2019
                Qian Li

                Social Statistics Section

                Chair-Elect 2019
                Eileen O’Brien

                Program Chair-Elect 2019
                Antje Kirchner

                Publications Officer 2019–2020
                Liana Fox

                Council of Sections Representative 2019–2021
                John Finamore

                Section on Statistics in Sports

                Chair-Elect 2019
                Phil Yates

                Program Chair-Elect 2019
                Sarah Morris

                Survey Research Methods Section

                Chair-Elect 2019
                Morgan Earp

                Program Chair-Elect 2019
                Yang Chang

                Treasurer 2019–2020
                Darcy Steeg Morris

                Publications Officer 2019–2020
                Steven Pedlow

                Council of Sections Representative 2019–2021
                Julia Soulakova

                Education Officer 2019–2020
                James Wagner

                Section on Teaching of Statistics in the Health Sciences

                Chair-Elect 2019
                Laila Poisson

                Council of Sections Representative 2019–2021
                Jose-Miguel Yamal

                Section on Nonparametric Statistics

                Chair-Elect 2019
                Runze Li

                Program Chair-Elect 2019
                Yanyuan Ma

                Treasurer 2019
                Jelena Bradic

                Council of Sections Representative 2019–2021
                Aurore Delaigle

                Statistics in Defense and National Security

                Chair-Elect 2019
                Douglas Ray

                Program Chair-Elect 2019
                Karl Pazdernik

                Publications Officer 2019–2020
                Kelly Townsend

                Council of Sections Representative 2019–2021
                Joe Warfield

                Section for Statistical Programmers and Analysts

                Chair-Elect 2019
                Vipin Arora

                Program Chair-Elect 2019
                Navneet Hakhu

                Council of Sections Representative 2019–2021
                Ben Joseph Barnard

                Section on Statistical Learning and Data Science

                Chair-Elect 2019
                Helen Zhang

                Program Chair-Elect 2019
                Adam Rothman

                Council of Sections Representative 2019–2021
                Xiao Wang

                Section on Statistics in Imaging

                Chair-Elect 2019
                Anuj Srivastava

                Imaging Program Chair-Elect 2019
                Linglong Kong

                Mental Health Statistics Section

                Chair-Elect 2019
                Wes Thompson

                Program Chair-Elect 2019
                Knashawn Morales

                Council of Sections Representative 2019–2021
                Christine Mauro

                Section on Medical Devices and Diagnostics

                Chair-Elect 2019
                Matyin Ho

                Program Chair-Elect 2019
                Tyson Rogers

                Section on Statistics in Genomics and Genetics

                Chair-Elect 2019
                Saonli Basu

                Program Chair-Elect 2019
                Qionshi Lu

                NIST Sponsors Data Science Competition

                Sun, 07/01/2018 - 7:00am

                The National Institute of Standards and Technology is sponsoring a data science competition with a prize purse of $190,000 called The Unlinkable Data Challenge: Advancing Methods in Differential Privacy.

                The goal of this series of competitions is to propose a new or improved mechanism to enable the protection of personally identifiable information while maintaining the usefulness of data sets to be used by researchers for positive purposes and outcomes.

                Excitement, Sleeplessness, Relief: ASA Members Express How It Feels to Win NSF Graduate Research Fellowship

                Sun, 07/01/2018 - 7:00am

                “I was super elated,” says Brian Kwan, upon hearing he had been awarded a National Science Foundation Graduate Research Fellowship—so much so that he couldn’t sleep that night. Receiving the fellowship—one of 16 awarded to statistics students out of 2,000 recipients—was the culmination of years of anticipation, during which he resisted the temptation to rush an application while an undergraduate and, instead, deepened his knowledge of statistics by working on projects with faculty members at the University of California, Irvine, University of Pittsburgh, and his current home, University of California, San Diego.

                Quoting the line from John Tukey that “The best thing about being a statistician is you get to play in everyone’s backyard,” Kwan says collaborating with different investigators gave him a chance to learn about the statistical literature in each scientific field and to reason which statistical methods could apply best to the statistical analysis needed. Working with Loki Natarajaran on research to develop novel statistical approaches to predicting future kidney function decline among type 2 diabetics deepened Kwan’s interest in prediction modeling and gave him the confidence, he says, to apply for the fellowship.

                Maria Jahja heard from a friend she had been selected, and after checking the email, she immediately texted her parents. “It was all typos because I was so excited,” she says. “I know so many great students who applied in my field, I didn’t think I really had a chance.” As an undergraduate research assistant in the lab of Eric Laber at North Carolina State University, Jahja began building artificial intelligence agents for video games. “I would code fun games, then implement learning algorithms for sequential decision-making under uncertainty.” This led to her research proposal on using statistically rigorous uncertainty measures to inform decision-making, which she will pursue at Carnegie Mellon University.

                “Computers are astonishingly efficient at solving formal problems,” she says, “but building an intelligent system for uncertain situations is much more difficult. Human intuition and reasoning are hard for a machine to reproduce, especially in complex environments where even a human expert might be unsure what the best choice is. It might even be that there is no ‘optimal’ choice, as it depends on individual preference. But if we could make some hybrid data-driven system capturing the strength of both—an expert and algorithm-driven decision-making system—I think that has immense value for society.”

                Derek Hansen came to his research proposal, in part, through working at the Federal Reserve Board as a research assistant, where he used state-space techniques to estimate models in economic and financial applications and wrote lots of code in R and Julia to tackle the technical problems encountered (he will present the results of this research at JSM in a presentation titled “Randomized Missing Data Approach to Robust Filtering with Applications to Economics and Finance”). His NSF proposal will look at whether similar techniques can be used to improve model selection.

                “To be honest, I wasn’t expecting at all to win the NSF fellowship,” says Hansen. “In fact, I found out because a fellow research assistant at the Federal Reserve saw my name on the website and congratulated me. I was absorbed in [a] Bayesian particle filtering project, so I hadn’t even checked my non-work email in a few days.” Hansen says the news was both a pleasant surprise and reassuring. “It’s a little scary working on a research proposal and sending it off to be evaluated—I also am relieved that experts in the field of statistics don’t think my ideas are completely crazy.” Hansen will begin a PhD program in statistics at the University of Michigan this fall.

                The NSF fellowships provide financial support for three years across a five-year period.

                The Commutative Property Is Neither Necessary nor Sufficient: It’s Time to Consider Implementing Distance PhD Programs

                Sun, 07/01/2018 - 7:00am

                Seth T. Lirette is an assistant professor in the department of data science at the University of Mississippi Medical Center. He supports biomedical research across the entire university, while focusing on radiological and aging research. He is also president of the private analytics and statistical consulting firm Blackshear & Lirette. He holds an MS in statistics from Mississippi State University and a PhD in biostatistics from the University of Alabama at Birmingham.

                  With the data revolution firmly upon us, it has never been more imperative to have highly skilled data practitioners building models and conducting analyses. Many of those doing data science–type work have no formal training, and this often results in questionable analyses.

                  One potential way to rectify this situation is to provide easier access to those people who wish to pursue advanced training in analytics-related disciplines, but—for various reasons—are unwilling to take residential status at or commute to campus. While there is a litany of master’s degrees offered completely online, I am unaware of any programs for distance PhDs in statistics, biostatistics, data science, or any other analytics field. I propose it is time we loosen the reigns on the seemingly ubiquitous residential requirement for admission to PhD programs.

                  Disproving the sufficiency of commuting to a campus for obtaining a PhD is trivial, as we know lots of people who drive onto campuses for 4+ years and never receive doctorates. Disproving the necessity of commutation (or residentiality) is a bit more challenging. Although anecdotal, my personal experience gives a good counterexample. This essay seeks to document my experience in a distance (although officially residential) PhD program, explaining the positives and negatives alongside what worked and what needs to be improved upon.

                  Background

                  I began my stint at the University of Mississippi Medical Center (UMMC) as an unpaid intern during the summer between the two years of my master’s degree. During this internship, I was offered a full-time biostatistician position once I completed my MS degree. I accepted this position in May 2012. Then, in the spring of 2013, I was approached by who would eventually be the head of my department with an interesting proposition. He had explained that everyone was impressed with my work, and that he and the other faculty members would eventually like me to join the faculty. At the risk of sounding obvious, I would need a PhD for this to take place.

                  UMMC had no program related to statistics or biostatistics (we now have PhD and master’s programs in biostatistics and data science). Therefore, I needed to take the degree from somewhere afar. But I had no desire to uproot my family, nor did the department want to relinquish me. This is where the interesting proposition enters: UMMC had worked out an unofficial agreement with the department of biostatistics at the University of Alabama at Birmingham (UAB) in which I could take my PhD from UAB, but stay employed full time at UMMC. I would complete every degree requirement necessary (complete 60 hours of coursework, pass comprehensive exams, complete 24 research credit hours with a dissertation, and other requirements). The only difference between me and my student-peers would be that they were full-time graduate students living in Birmingham, Alabama, and I was a full-time biostatistician living in Jackson, Mississippi.

                  My Experience

                  From my anecdotal evidence, it seems to be somewhat common for a student to complete his or her dissertation while not living in the same locale as his or her adviser. However, I could not find a documented instance of a person doing an entire statistics PhD from a distance. So, this was going to be an exercise in faith and completely an (N=1) experiment. There were some growing pains, but for the most part, things went rather smoothly.

                  It was first agreed upon that I should show up for classes, at least initially. So, for the entirety of the fall semester of 2013, I made the four-hour one-way commute from Jackson to Birmingham to attend my Tuesday/Thursday classes in person and made the trek back, twice per week. This was obviously less than ideal, but everyone involved wanted to make sure I would not be falling behind on my coursework. In retrospect, this was unnecessary, as we will later see.

                  During the second semester, spring of 2014, I spoke with my professors, and we agreed that, given my success in the first semester, I could cut the commute back to once per week. At the end of the first year, I had logged many, many miles on I-20.

                  From that point on, and since my grades were exemplary, I would make it standard practice to meet face-to-face on the first day of class and then only be physically present if needed (i.e., to take an exam or to present a formal presentation).

                  Every professor I encountered was more than willing to be helpful in any way. Any questions I had about assignments or methods were answered over telephone calls or email exchange. I never felt as though I did not have adequate access to my instructors.

                  One caveat to this does need mentioning. Because this was not an official distance program, most of the classes consisted only of traditional face-to-face lectures. I took two official online classes (Writing for Research and Introduction to Epidemiology), but other than these, there was not an official platform for disseminating lectures other than face-to-face. As a result, I sometimes felt like I was not “getting my money’s worth” from some classes. I cannot fault them for this, as it was not an official distance program, but I love hearing people lecture on subjects they know intricately, and this is one thing I wish I had more of. That being said, by the time of my last course (Advanced Clinical Trials), we had a very nice virtual classroom where I was able to fully participate from four hours away via webcam. If a school chooses to fully implement a distance program, this would be vitally necessary.

                  Another downside to the distance model is that many people meet lifelong friends in graduate school, as I did during my master’s degree. Not living in the same city or seeing them often obviously limited the amount of interaction I had with my classmates. I knew a few through email and casual conversations, but I would have liked the chance to get to know them better.

                  I, however, was able to take solace in the fact that working full time provides a much better salary than that typically received through assistantship and fellowship stipends. And if I am being completely honest, my family of five needed a full-time salary more than I needed new friends.

                  One of the biggest advantages of working full time while pursuing the degree is that I was constantly exposed to real-world issues of data analysis. It is no secret that the curated data sets provided in classroom settings are usually nothing like data in the wild. The latter is what I was exposed to on a daily basis and would only serve to sharpen my skills. Also, with almost every new technique I learned in the classroom, I could envision somewhere at UMMC to apply it. These two worlds synergized very well. In fact, the topic of my dissertation directly resulted from one of my collaborations with the department of radiology at UMMC.

                  By the time it came to write my dissertation, everyone knew about my situation and I had assembled a wonderful committee willing to provide me with anything I needed to succeed.

                  Given my entrance into the program in August of 2013, we were initially aiming for a graduation date of May 2018. In fact, I earned my degree in August of 2017. This was, in no small part, due to both the helpfulness of the entire department of biostatistics at UAB and the generosity of what would become the department of data science at UMMC.

                  While I realize this option is not for everyone seeking a PhD, I hope I have made the argument that implementing a distance PhD should now be a viable option for most departments. Adding more highly trained statisticians, biostatisticians, and data scientists to the academic, governmental, social, industrial, and technological workforce can only improve some of the issues we currently face.

                  With more educational options, we can go a step further in adding more well-qualified practitioners of the data sciences, and we can hopefully make progress into taming what sometimes currently looks like a Wild West of analytics.

                  NC Chapter Sees Speed-Mentoring Success

                  Sun, 07/01/2018 - 7:00am

                  Gabe Farkas (right) chats with two students during the speed mentoring program.

                   

                  Breda Munoz and Rishi Chakraborty answer students’ questions during the speed mentoring program.

                    The North Carolina Chapter hosted a speed mentoring event in April during which students and young professionals had the opportunity to meet with established career professionals in a small-group setting. Mentors and mentees discussed networking, goal setting, and career development with targeted questions and activities.

                    Mentors included Abie Ekangaki of UCB, Kirsten Foley of the Environmental Protection Agency, Gabe Farkas of the San Antonio Spurs, Breda Munoz of RTI, and Rishi Chakraborty of DCRI.

                    Mentees found the format efficient and liked that there were opportunities to talk to or hear from everyone while going more in-depth with the mentor they were assigned to.

                    The chapter’s next mentoring event will be a full-day workshop on November 30. Mentees will have the opportunity to attend the chapter’s fall dinner that evening to mingle with the larger North Carolina Chapter community and have a small group meeting with ASA President Lisa LaVange. See more photos on the chapter’s website.

                    What Does Samanthi Konarasinghe Like to Do When She Is Not Being a Statistician?

                    Sun, 07/01/2018 - 7:00am

                    Konarasinghe is made up before a theater performance.

                      Konarasinghe’s painting, “My Dream”

                        Who are you, and what is your statistics position?

                        I am Samanthi Konarasinghe, statistician and director of the Institute of Mathematics and Management in Sri Lanka. I am a senior lecturer in mathematics, statistics, and financial management at universities and tertiary education institutions.

                        Konarasinghe

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

                          When I am being a statistician or not, I am an artist. I am an actress, painter, violinist, and writer. I have produced several stage plays and won the best actress award, as well. I have conducted a solo art exhibition, named “My Dream.” I am a nature lover. I walk miles and miles of roads full of trees and flowers. I enjoy feeding and talking to animals. I love parties and dancing.

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

                          As everyone says, I was born an artist. From my small days, I was equally interested in arts and mathematics. For me, painting, singing, dancing … are not hobbies; they are part of my life. My scientific and artistic lives are intertwined.

                          Session Proposals Sought for JSM 2019

                          Sun, 07/01/2018 - 7:00am
                          Rich Levine, JSM 2019 Program Chair

                            Rich Levine

                            You read that right. JSM 2019 program planning has begun. This call always takes me aback. Here we each are in the throes of preparing for JSM 2018 Vancouver—organizing our alumni gatherings, salivating over the slam-packed conference program, locating the best eateries, stressing because we should have planned the pre-conference trip to Banff earlier—and then some highly organized, energetic, new program chair springs 2019 on us.

                            I know the dog days of summer are busy, but don’t let this opportunity pass you by. 2019 ASA President Karen Kafadar has set the JSM 2019 theme as “Statistics: Making an Impact.” Take a morning to organize your thoughts on one of the five session types highlighted below. The proposal process is easy—the deadline is September 6. Aim to make an impact in Denver!

                            Invited Paper and Panel Sessions

                            The classic invited session format consists of a session of talks, and perhaps commentary, by two to six speakers and discussants or a panel of three to six expert discussants. We are seeking sessions that address a hot topic of broad audience interest by a slate of engaging speakers/panelists. In fact, the most stimulating invited sessions present diverse viewpoints and strategies on a common topic, with speakers coming from different institutions and taking different approaches toward similar problems.

                            To organize a session, first set a theme and contact potential participants. Once these are determined, the proposal consists of the session title, a brief description/rationale, the list of participants, and tentative titles for the talks (these may be modified later). Proposals are submitted through the JSM online system, which opens July 18. The deadline for submission is September 6 at 11:59 p.m. ET.

                            As part of the submission process, you will select up to three sponsors (partner society, ASA section, etc.) in rank order for your proposed session. Once you submit a proposal through the online system, campaign for a sponsor! Most of the 209 invited sessions are allocated to partner societies and ASA sections. Furthermore, the ASA sections will select up to two proposals to enter into a competition for the remaining slots in the invited session program. You may find the contact for your desired sponsor on the program committee roster, which you can find on the JSM website when it goes up at the end of this month. Enlisting a proposal champion at the program committee table is a desired strategy. You must submit your proposal through the online system before contacting a program committee member sponsor representative. Given a meeting of this size and the number of balls the program committee juggles, the online submission ensures your proposal does not fall through the cracks.

                            As you are probably gathering, given the annual growth of JSM, the competition for an invited session slot is now fierce. Take the time to identify a fresh and important topic and provide an inspiring session description. You may also revise your proposal following a discussion with the program committee sponsor representative until the September 6 deadline. The goal is to make your proposal appealing to other committee members in the event it enters the selection competition. Finally, make sure your session participants follow the strict rules for participation.

                            Decisions about the invited program will be made by the end of September. Due to the limited number of sessions and increasing attendance, many strong invited session proposals will not be selected. Do not lose hope—you may submit your proposal later as a topic-contributed session.

                            Memorial Sessions

                            We will slot five memorial sessions at JSM 2019. Proposals must be submitted through the online invited session system, choosing “memorial session” as the sponsor. To maximize the odds of being selected for inclusion in the invited program, I recommend submitting memorial sessions by September 6 to enter the competition for an invited session. If the session is not selected by a partner organization or ASA section, the session proposal will be entered into the competition for one of the five memorial session slots. That said, I encourage you to contact me if you are planning to submit a memorial session. Final decisions will be made in the fall.

                            Invited Poster Sessions

                            We will continue the new tradition of an invited poster session of up to 30 electronic posters during the Opening Mixer. This session is an excellent opportunity for presenters to interact one-on-one with JSM attendees. Email your ideas to the JSM 2019 poster chair.

                            Introductory Overview Lectures

                            We are shooting for four Introductory Overview Lectures (IOLs) addressing state-of-the-art, important statistical topics of broad interest to JSM attendees. An IOL may be presented by an individual or a team. That said, I am looking for engaging, experienced lecturers with a facility for imparting potentially subtle ideas and deep concepts to a large audience. Do note that IOL speakers may also present an invited or contributed paper, panel, or poster at JSM. Contact me with suggestions for topics and/or speakers.

                            Statistics: Making an Impact

                            On behalf of all program committee members, I thank you in advance for helping JSM 2019 make an impact worthy of the majestic Rocky Mountains that will greet us each day in Denver July 27 to August 1, 2019!

                            Universities, Industry Collaborate to Benefit All

                            Sun, 07/01/2018 - 7:00am

                            Members of the ASA Committee on Funded Research (CFR), whose charge includes facilitating communication and interaction between the statistical community and funding organizations, sought to learn about how university departments engage industry in research collaborations. They found many departments willing to share their experiences by answering the following questions. CRF members hope this information furthers industry collaborations by the statistical community.

                            IOWA STATE UNIVERSITY

                            Max Morris is professor and chair of the department of statistics at Iowa State University and a statistical consultant affiliated with Los Alamos National Laboratory.

                            Please describe your department’s industry and/or government engagements/collaborations.

                            We have a set of agreements with various companies that provide support and professional experience for some of our more advanced graduate students. In brief, we sign a contract with the company to provide statistical support for 20 hours per week (the typical research assistant load) for one or more semesters. The contract is signed by the company and university and is processed through our grants and contracts office. The company pays the university, and the funds are used to support a graduate assistantship for the student (tuition and stipend at our usual rate) for the prescribed length of time.

                            With this setup, the student is an employee of the university, rather than the company—an arrangement especially helpful for some international students. For the most part, department faculty are not directly involved. The work done by the student is generally what we would call “consulting” or “applied research” and most often not related to their dissertation research. The experience is comparable to that of many industrial summer internships, but may last more than one term and is such that the student can do the work from campus.

                            How did the engagement/collaborations come about? How are the collaborations sustained in a way that is mutually beneficial to students, the university, industry, and the community-at-large?

                            These agreements are usually initiated by the companies, with communication to our department asking about opportunities to work with our students. Beyond financial support, the benefit to the student is primarily in the form of professional experience. Although the immediate benefit to the company is the work done by the student, there is often an interest in getting an “early look” at a potential employee, much as with many summer internship programs.

                            What arrangements about the collaborations might be helpful to share?

                            The arrangements are formalized through a contract between the company and university. I (as department chair) handle setting these up and, along with the associate chair, identify students who are good candidates. In some cases, the company also participates by interviewing students we identify as candidates. Before it is finalized, a contract must be approved by the university grants and contracts office; invoicing and fund transfer is handled by our central administration. There is usually little direct faculty oversight of the students (and so they must be relatively mature and independent), but as the university contact for these arrangements, I receive feedback from the companies about the students’ performance. In most cases, the students involved in these arrangements participate with a company for multiple semesters.

                            What benefits for the department have you seen come out of these collaborations?

                            For the department, these arrangements supplement our state-funded teaching assistantships and grant-funded research assistantships, giving us a bit more flexibility in how we arrange support for our students.

                            SOUTHERN METHODIST UNIVERSITY

                            Lynne Stokes is chair of the department of statistical science at Southern Methodist University. Work on problems for the US Census Bureau, National Center for Education Statistics, National Oceanic and Atmospheric Administration, US Energy Information Administration, and other federal agencies has been some of the most interesting of her career.

                            Please describe your department’s industry and/or government engagements/collaborations.

                            We have several types of engagements with business/government that pay our graduate students. Besides the usual grants, we currently have the following two activities:

                            • Local business (management consulting) provides stipend/overhead for a PhD student (just completed second year of this) in exchange for his time. This is in lieu of a teaching or research assistantship in the department. This has been mutually beneficial, as company has extended full-time offer to student on graduation. Student has accepted. We have had similar arrangements with hospital systems in the past.
                            • We have a long-term consultancy with a federal agency. The funding goes through our department’s statistical consulting center. Three students have received funding (stipends, summer funding) for work on various projects as needed. Some projects are short term (a few weeks) and some are long term (several years).

                            Other kinds of relationships:

                            • We have relationships in the area with companies who come to our department to interview students for summer internships. We facilitate the interviewing process by setting up appointments and giving them space. This is for grad students at both the master’s and PhD levels.
                            • We provide a forum for local companies to discuss their company’s projects (i.e., give a work-related seminar). This is for our master’s students. This is mostly to facilitate their recruiting of our students, but also provides these students with information about what statistical methods are used in the workplace.
                            • Local companies provided people (a lot of them the same as mentioned in the previous paragraphs) to serve as judges at the DataFest contest we held this year.
                            • We provide free consulting services to companies/nonprofits willing to be clients for our graduate students in their required consulting class.
                            How did the engagement/collaborations come about? How are the collaborations sustained in a way that is mutually beneficial to students, the university, industry, and community-at-large?

                            Some are cold calls that companies make to our department looking for help either on consulting problems or for recruiting.

                            The federal agency relationship resulted from a National Academies panel I served on. I met people at the agency who were trying to implement changes. This relationship has now been ongoing for more than 10 years.

                            A fair number of these relationships were from local companies hiring our students and having them work out well. They are interested in a pipeline to students. Then, over time, our students continue the recruitment cycle.

                            Some of the relationships are from the local ASA chapter meetings. We hold them on our campus, and so we get to know industry people who come to the meetings.

                            What arrangements about the collaborations might be helpful to share?

                            The consulting center is a good vehicle for supporting nondomestic students in the summer. The immigration laws allow people holding student visas to work on campus, but it is more difficult for them to get authorization to work off campus. If a company is willing to give up “control” and let the student be supervised by us and work on our campus, this allows them access to scarce talent.

                            What benefits for the department have you seen come out of these collaborations?

                            We have had a variety of benefits, including the following:

                            • We have been able to place our master’s students well locally, which helps us in recruitment.
                            • We have been able to have more PhD students (larger number of stipends) and provide more of them support in the summer on a part-time (flexible) basis, so they can work on dissertations and still earn enough to survive.
                            • We have generated research topics. Two current dissertation topics are from the federal agency consultancy.
                            • We have not yet hired adjuncts from our industry contacts, but we have discussed this with our industry friends, and will use them if we find ourselves short-handed.
                            • We have had the opportunity to train our students in consulting.
                            What benefits for the broader community have you seen come out of these collaborations?

                            Companies have been very happy with our students as employees. We are now working with two local companies to facilitate their employees’ participation in our master’s program. To do this, we are moving some of our courses to the evening, allowing them to receive credit in some courses (e.g., consulting) from their work experience. This is providing a better workforce for the companies and good (tuition-paying) students for us.

                            As mentioned above, we do provide free consulting to companies willing to work with our students. This has been especially useful for nonprofits without funds to hire statistical help.

                            What are the challenges or barriers to such collaborations? What have you learned since your department began these collaborations to deal with these challenges?

                            Challenges are locating people within the industry to partner with. Then, even after you have a working relationship with someone in a company, they may move on and you lose your contact.

                            We have not found an effective way to locate people in industry with whom to partner. We rely on them finding us. Because we are in a large metropolitan area, this has worked for us, but I can see it would take a lot more resources than a faculty member/chair would have to cultivate relationships if you were in a smaller area.

                            Most problems we get from local companies do not rise to the level of research problems for PhD students. They are still beneficial to train students in consulting. However, occasionally, there is a reluctance on the part of the company to pay the full cost of consultation, thinking the problem is helping supply our PhD students with dissertation problems. This has led to some awkward conversations and wasted time in scoping potential problems.

                            VIRGINIA TECH

                            Jennifer H. Van Mullekom is the director of the Statistical Applications and Innovations Group (SAIG) at Virginia Tech. She has collaborated on both sides of the fence as an industrial statistician for more than 18 years and now as an associate professor of practice.
                             
                             
                             
                             
                            Anne Ryan Driscoll is an assistant collegiate professor at Virginia Tech. Her research interests include statistical process monitoring, design of experiments, and statistics education. She has also collaborated on projects for the Department of Defense and NASA.

                             

                            Please describe your department’s industry and/or government engagements/collaborations.

                            The Virginia Tech Department of Statistics collaboration projects span grants from various government agencies, partners in the Virginia Tech Corporate Research Center, department of statistics corporate partners, and private companies that contact the department independently of our other programs.

                            We currently have a large number of collaborations not related to large federal granting agencies such as the National Institutes of Health or National Science Foundation. Our longstanding Corporate Partners Program creates an opportunity to exchange ideas between corporate statistics leadership and our department. Corporate Partners creates a forum to receive input on our program, place our students in internships and permanent positions, and facilitate collaboration.

                            We now have a new way to collaborate on short-term and long-term projects through the Statistical Applications and Innovations Group (SAIG), which recently became an external service center. SAIG can provide quotes for design, analysis, and reporting, which also may lead to research collaborations.

                            How did the engagement/collaborations come about? How are the collaborations sustained in a way that is mutually beneficial to students, the university, industry, and the community-at-large?

                            Collaborations originate in a variety of ways. Many of our government and industry collaborations have come through alumni employed at various agencies. They often encounter research questions in their daily work that are “important, but not urgent.” Their organizations lack the personnel to devote to these projects, so they turn to the university for both expertise and cost-effective flexible staffing to devote to the projects.

                            Other collaborations result from master agreements negotiated at either the university or college level. When these projects are presented at conferences, we often create interest from other partners. And, of course, networking at national conferences through the American Statistical Association and other professional societies such as the American Society for Quality also lead to these relationships.

                            The relationships are sustained by a successful track record of meeting deliverables. Our faculty purposely seek opportunities that align with their research interests and the interests of their students. They also scope opportunities properly and negotiate appropriate compensation for supervision and the students’ funding.

                            The “important, but not urgent” distinction is also made so we set expectations appropriately. Students have course and degree requirements, so they are not capable of dropping everything to devote all their time to solving a highly time-sensitive problem. Regular program reviews and communications are essential to keeping the program on track.

                            Another, often overlooked, avenue for research is that of a research associate or research professor. These are degreed staff members and faculty within the university who can be devoted full time to a more urgent problem.

                            The Corporate Partners Program is another way our department fosters collaborations with outside entities. This program was established 19 years ago with the goal of solidifying the informal ties with industry and government built over many years. Most of the partnerships originate because of ties to alumni who are employed at various companies.

                            Currently, our department benefits from partnerships with five companies. These partnerships are sustained by providing value to both the department and the company. Benefits to the company include an annual symposium and advisory board meeting to learn about initiatives at the department and university, opportunities to recruit students for internships and full-time employment, and easy access to faculty to foster joint research projects. The department benefits from feedback from the partners about refining our curriculum to tune our program to the rapidly changing needs of users of statistics, funding for graduate student scholarships and awards, research projects for faculty and students, and also employment opportunities for students.

                            What arrangements about the collaborations might be helpful to share?

                            Our Office of Sponsored Programs and the Virginia Tech Business Engagement Center provide excellent support in working through the logistics of relationships with outside entities. Existing relationships and agreements between VT industry affiliates and Corporate Research Center partners facilitate development of specific collaborations.

                            A relationship can be initiated by any faculty member. With respect to business relationships, the faculty member then completes a request for appropriate legal agreements on behalf of the university. Once the appropriate documents have been negotiated and signed, the relationship progresses through the appropriate channels in the university depending on its nature. Smaller projects may be handled at the departmental level, while others are handled at the college level.

                            Collaborations have varied time frames. For example, a short-term analysis collaboration with SAIG could be initiated by me and last a few weeks to a few years. Many of our long-term research relationships with professors and research groups within the department have lasted five years or more.

                            What benefits for the department have you seen come out of these collaborations?

                            The publications resulting from our collaborations are quite numerous, including ones in journals such as Journal of Quality Technology and Quality Engineering. A collaboration between Yili Hong and DuPont resulted in multiple publications and a SPES [Section on Physical and Engineering Sciences] award from the American Statistical Association, whereas one between Geoff Vining and NASA resulted in a NASA Engineering Safety Council Engineering Excellence Award. Those collaborations have also led to student dissertation topics with members of the sponsoring organization serving on the student’s committee.

                            Anne Driscoll also works on the latter collaboration with NASA. Both Vining and Driscoll collaborate with the Department of Defense. An organically evolving collaboration between Baker Hughes, a GE Company, under Robert Gramacy has led to a line of research on computer model calibration of a seal used in oil and gas extraction that is the topic of a student’s dissertation. William (Bill) Woodall has worked with many external collaborators over the years, most recently with the Food and Drug Administration to provide input on their Quality Metrics Program. Additionally, Robertson Evia- has collaborated on K–12 STEM education studies to provide analysis resulting in Science Communication publications for the National Academy for the Advancement of Science.

                            We have given short courses and webinars for our corporate partners and are continuing to evolve that relationship. We also look forward to forging new collaborations with external companies through SAIG.

                            What benefits for the broader community have you seen come out of these collaborations?

                            Our collaborations have certainly established strategic national government and industry partnerships, but our department also focuses on serving the local community. Christian Lucero is working through SAIG with Virginia Cooperative Extension to develop an analysis tool for a multicenter Centers for Disease Control and Prevention–sponsored diabetes management and prevention program. Our work has also resulted in grant applications for the local school systems, analytics supporting the building of a new dormitory for the Virginia Tech Corp of Cadets, and analytics to support policy and staffing in county governments.

                            What are the challenges or barriers to such collaborations? What have you learned since your department began these collaborations that help with these challenges?

                            Two of the barriers to collaborations with universities include the speed of establishing the legal relationships and data security. Both these problems typically have pre-existing solutions at most large research universities, but it often takes perseverance to execute these aspects of the projects properly. All parties involved must be familiar with the legal process and who to contact to initiate and sign agreements. A basic understanding of intellectual property is also important.

                            Some of our statistics/data science relationships are more consulting in nature, whereas others may involve proprietary algorithm or software development. The boundaries of these relationships and IP [intellectual property] ownership must be established up front. Statisticians and data scientists also need to be able to educate their lawyers regarding the differences of applications versus IP-containing projects. Members of our profession also need to be aware when a project crosses the line into IP development and how to protect their contribution.

                            Government and corporations have varying IT [information technology] security requirements. Personal health information creates its own unique challenges. Academicians are not typically “raised in this environment.” There is typically much more freedom in sharing information in academia. There is a learning curve associated with both partner requirements and on-campus resources for creating a secure IT environment. Due diligence on this front ahead of time can pay off in expedited project start-up.

                            Any other comments?

                            Professional development courses and leadership training provided by the American Statistical Association are crucial to the development of collaborations. An understanding of vision, mission, project management, emotional intelligence, and execution are essential to building and sustaining collaborations.

                            ARIZONA STATE UNIVERSITY

                            Jennifer Broatch, an assistant professor and BS program lead at Arizona State University’s West Campus, assists junior faculty.
                             
                             
                             
                             
                             
                             
                            Michelle Mancenido works to establish and coordinate industry collaborations and projects for BS statistics majors.

                             
                             
                             

                            Please describe your department’s industry and/or government engagements/collaborations.

                            Our primary involvement with industry is a semester-long senior capstone project.

                            We have three industry partners for this: a health care provider, a professional sports organization, and a global manufacturing company.

                            The capstone sites present the student team with a sufficiently diverse range of statistical applications to choose from. Students are mentored by faculty from statistics and applied mathematics, but they are ultimately responsible for scoping their projects, collecting data, coordinating with sponsors, formulating statistical models, coding, and—most importantly—presenting the results of their study to their sponsors. Mentors (one per team) are only expected to provide students with advice and help overcome stumbling blocks in the course of the project.

                            How did the engagement/collaborations come about? How are the collaborations sustained in a way that is mutually beneficial to students, the university, industry, and the community-at-large?

                            The collaborations are based on faculty network connections. The arrangement is mutually beneficial. For ASU, students are able to work and deal with real-world data that can sometimes be massive and dirty. For the sponsors, the students come in with skills that help them solve business problems they do not have the manpower or resources to tackle. It has also provided a pipeline for qualified job applicants.

                            The business problem tackled by the student team is selected in collaboration with our industry partners. Students are not simply handed data to analyze. They are active participants in the selection of the research project.

                            What arrangements about the collaborations might be helpful to share?

                            Each project is a semester-long contract. The contract is in the form of a project charter, which includes opportunity or problem statements, objectives, scope and limitations, and expected deliverables. The students develop these charters as part of their project management training. The students are also responsible and accountable for ensuring the delivery of quality output and meeting sponsor requirements such as deadlines.

                            What benefits for the department have you seen come out of these collaborations?

                            Our partnerships have directly led to jobs and internships within the industry partner. We also use an industry partner as an adjunct faculty to ensure the most current job skills. The capstone offers experience for junior faculty in business problems and the application of statistics (similar to a consulting center).

                            What benefits for the broader community have you seen come out of these collaborations?

                            In addition to the capstone, we partner with a local nonprofit conservation society. They have a great need for quality analysis, and—in turn—our students can get experience and help the local community at the same time.

                            What are the challenges or barriers to such collaborations? What have you learned since your department began these collaborations to deal with these challenges?

                            Always team dynamics. The students have a very tight timeline (four months), so they are pressured to deliver, which boils over when the dynamics are shaky to begin with. Hence, we add a team-building and leadership component to the course.

                            Another is the scoping of the project. For most of the project sites, it is a part-time job, so the students sometimes find themselves overwhelmed with meetings, onsite data collection, etc. The faculty mentors are instrumental in ensuring the deliverables and project timeline are feasible.

                            Company logistics and human resources are another hurdle. For example, our health care partner requires at least two months to get a student started in their system (HIPPA training, vaccinations, etc.)

                            Finally, we have developed an excellent relationship with our sponsors; as long as the students keep delivering high-quality output, they are willing to accommodate the students year-in and year-out.

                            Any other comments?

                            I know these projects can often be a lot of work, but they have been overwhelmingly rewarding for our students.

                            Pages