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What Does Claire Kelling Like to Do When She Is Not Being a Statistician?

Sun, 04/01/2018 - 7:00am
This column focuses on what statisticians do when they are not being statisticians. If you would like to share your pastime with readers, please email Megan Murphy, Amstat News managing editor.

Claire Kelling began beading when she was 10 years old.

Who are you, and what is your statistics position?

I am a dual PhD candidate in statistics and social data analytics at Penn State.

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

When I am not being a statistician, I like to weave! I mostly create beaded patterns for family members as Christmas and birthday gifts. My most recent patterns have used more than 10,000 beads per design!

Claire Kelling’s largest project thus far, a gift for her mother, contains 10,712 beads.

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

I have been beading since I was quite young, starting when I was about 10 years old, I think. I never enjoyed or was particularly good at the typical artistic activities, like drawing or painting. I am a triplet, and my brother and sister were both quite talented artistically. Having hobbies was actively encouraged in my family, as we lived on 56 acres with little to no TV, internet, or games. Therefore, I took up this hobby as a way to express myself artistically. It started as weaving potholders and turned into weaving pretty elaborate designs on a bead loom!

Claire Kelling completed her first major beading project when she was in about the fourth grade and entered it into the 4-H fair. It won fourth place.

My next big step for weaving will likely involve a large fabric loom (about 5 x 5 x 5 ft). I enjoy this hobby because, as a statistician perhaps, I am drawn to patterns, and beading involves patterns quite obviously in the designs, but also in the execution of the craft. It is very methodical and careful, much like statistics.

I also enjoy creating something tangible through my hobby. My beadworks are almost always gifts, and I think a handmade gift is an excellent way to show your appreciation for someone. I think the fact that I have put 10,000+ beads individually onto a needle to weave into a design shows a lot about how much I care about someone!

BASS XXV Scheduled for Fall

Sun, 04/01/2018 - 7:00am

The 25th Biopharmaceutical Applied Statistics Symposium (BASS XXV) will be held October 15–19 at the Hotel Indigo Savannah Historic District in Savannah, Georgia. One-hour tutorials on diverse topics pertinent to the research, clinical development, and regulation of pharmaceuticals will be presented by speakers from academia, the pharmaceutical industry, and the US Food and Drug Administration. Two parallel, two-day short courses will be presented October 17–19. BASS will also offer a poster session.

BASS is a nonprofit entity established to support graduate studies in biostatistics. For further information, contact the BASS registrar or BASS chair Tony Segreti.

Master’s and Doctoral Programs in Data Science and Analytics

Sun, 04/01/2018 - 7:00am
Steve Pierson, ASA Director of Science Policy
    More and more universities are starting master’s and doctoral programs in data science and analytics—of which statistics is foundational—due to the increasing interest from students and employers. Amstat News reached out to those in the statistical community who are involved in such programs to find out more about them. Given their interdisciplinary nature, we identified programs involving faculty with expertise in different disciplines to jointly reply to our questions. We have profiled many universities in our April, June, and December 2017 issues and January 2018 issue; here are several more. University of Central Florida Liqiang Ni is associate professor of statistics in the department of statistics at the University of Central Florida. His research interests include dimension reduction, multivariate analysis, actuarial science, and business intelligence. He has served as the graduate coordinator since August 2017.

    Shunpu Zhang is a professor of statistics and chair of the department of statistics at the University of Central Florida. His research interests include bioinformatics, functional estimation, health informatics, large-scale hypothesis testing, and big data analytics. MS in Statistical Computing—Data Mining Track

    Year in which first students graduated/are expected to graduate: 2002
    Number of students currently enrolled: 60
    Program format: In-person, 36 credits, comprehensive exam required, either thesis or project-based, full time and part time, graduate assistantship offer on competitive basis

    There are largely two components for required courses in the curriculum. The first tilts to traditional statistics, including a two-semester course for theoretical statistics and one-semester course for regression analysis and logistic regression/GLM, respectively. The second component tilts to applications: two-semester course for data processing and preparation, including coding, and two-semester course for data mining. There is a variety of elective courses students can choose from.

    What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
    In the late 1990s, the statistics community began to realize the great potential in data mining and data science. UCF created one of the earliest data mining programs, in part inspired by SAS Company and with support from Disney, Florida Hospital, Blue Cross Blue Shield of Florida, Universal Studios, and many local business partners.

    The students responded with a good deal of enthusiasm. We have seen a growing need for an educated and talented workforce at the MS level and beyond that can contribute to industry, government, and academia through innovative applications of data analysis methodologies.

    How do you view the relationship between statistics and data science/analytics?
    We believe statistical science is an integral part of data science/analytics. A good data scientist/data analyst must have adequate training in statistics.

    What types of jobs are you preparing your graduates for?
    We are preparing MS graduates largely for industries. Every year, a few graduates continue their studies in PhD programs.

    What advice do you have for students considering a data science/analytics degree?
    We suggest students have a solid foundation in computer programming, mathematics, and statistics to be a good data analyst. They also should have a keen interest in the new developments in data science.

    Describe the employer demand for your graduates/students.
    Demand for our graduates has always exceeded the supply, especially in recent years.

    Do you have any advice for institutions considering the establishment of such a degree?
    We believe a data analytics/data science graduate program resides best in a statistics department with concentrations in computer programming and software development. Open-mindedness is the key to a successful interdisciplinary program.

    University of Michigan Michael Elliott is a professor of biostatistics and research professor of survey methodology. His research interests include survey methods, causal inference, missing data, and longitudinal data analysis with applications to social epidemiology, cancer trials, women’s health, pediatrics, and injury.

    H. V. Jagadish is Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science. His research has spanned many aspects of big data, including data usability when they come from multiple heterogeneous sources, and has undergone many manipulations.

    XuanLong Nguyen is associate professor and director of master’s programs in statistics. His research interests include Bayesian nonparametrics, hierarchical models, and machine learning.

    Elizabeth Yakel is associate dean for academic affairs and professor in the school of information. Her research focuses on data reuse, teaching with primary sources, and the development of standardized metrics to enhance repository processes and the user experience.

    Ji Zhu is a professor and director of the data science master’s program in statistics. His research interests include statistical learning; network analysis; and statistical modeling in finance, marketing, and biosciences. Data Science Master’s Program

    Year in which first students graduated/are expected to graduate: 2019–2020
    Partnering departments: Biostatistics, Electrical Engineering and Computer Science, School of Information, Statistics (administrative unit)
    Program format: Full time, on campus; requires at least 25 credit hours in core areas including databases, data and web applications, regression, and statistical learning

    The program requires the students to have demonstrated competence in a basic computing sequence and a basic statistics sequence. By taking graduate-level courses, the students need to demonstrate expertise in data management and manipulations, as well as statistical techniques relevant to data science. The students need to take at least one advanced elective from each of the following buckets: principle of data science; data analysis; and data science computation. The students will also have an integrative capstone experience through an approved project.

    Students with an undergraduate degree in data science would already have obtained a reasonable level of training toward the core skills and may finish the master’s degree in one year. Students with an undergraduate degree in mathematics or physics, statistics or biostatistics, computer science, and other quantitative disciplines should be able to complete all requirements within two years.

    What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
    The data science explosion is fueled organically by new data generated from diverse sources, devices, web services, mobile communication, scientific studies, and social media. Data scientists require a versatile and unique set of skills to manage, process, and extract data from these complex information streams, and then interrogate, analyze, visualize, and interpret the information. Nationally, there is a pressing need for data scientists, and, in fact, for people with every level of data science training. The successful launch of our data science major, which has attracted almost 200 students across campus in its first two years, made it clear that our students want to be part of data science. The collaborative approach we take across departments and colleges enables us to pool resources and offer the best our university has for a truly cross-cutting program.

    How do you view the relationship between statistics and data science/analytics?
    Statistics is undoubtedly a major part of data science. The advancement of statistics has always been driven by new data that arise in science or society, whether they are from agriculture measurements or the industrial revolution or the internet. While data science requires tools from multiple disciplines (e.g., mathematics, computer science) and must work with specific domains of applications (e.g., business or health care analytics), statistics and data science are inseparable. From design of experiments to probabilistic modeling, from data exploration to confirmatory testing, and from estimation to prediction, statistics has been the core to data analysis. Statistics without data science will not thrive, and data science without statistics is certainly unsound.

    What types of jobs are you preparing your graduates for?
    This is a new program, but we provide the training the students need to work as data scientists in a wide range of industries, from financial services to health care, from marketing to social networking. We invite companies to our career fair for the students, and we encourage students to take internships to help them understand what they need to prepare for in school.

    What advice do you have for students considering a data science/analytics degree?
    We offer two master’s degrees, one in applied statistics and the other in data science. At the present time, the applied statistics degree focuses more on modeling and inference, and the data science degree focuses more on data handling and data mining. Some of the students from the applied statistics degree pursue a doctoral degree in statistics, biostatistics, economics, and other quantitative fields. We expect the data science students to be versed in data management and programming. However, there is an increasing overlap between the two programs, as we offer more computing courses to applied statistics students and more statistics courses to data science students.

    Describe the employer demand for your graduates/students.
    We do not have data on our graduates from the data science program, but the vast majority of our graduates from the applied statistics program was employed or went to PhD programs within six months of their graduation.

    Do you have any advice for institutions considering the establishment of such a degree?
    Data science programs by nature cross traditional boundaries, but the department of statistics is a natural and ideal home for such programs. To make such programs successful, the statistics departments must be willing to modernize their existing curriculum to embrace data science and reach out to work with the faculty from other programs. At Michigan, different programs offer complementary courses in data science and, together, we believe we can attract and accommodate students from diverse backgrounds.

    The Johns Hopkins University James Spall has four appointments at The Johns Hopkins University: principal professional staff at JHU/APL; chair of the applied and computational math program; co-chair of the data science program; and research professor in the department of applied math and statistics. Spall has published extensively in the fields of control systems and statistics. Master of Science in Data Science and Post-Master’s Certificate (PMC) in Data Science

    Year in which first students graduated/are expected to graduate: Late 2018
    Number of students currently enrolled: More than 120 fully matriculated students in the MS degree and 0 students in the PMC program. There are additional students who have been given a provisional admission status (additional evaluation and/or coursework required) for both the MS and PMC.
    Partnering departments: Applied and computational mathematics and computer science
    Student type: Nontraditional/part time/continuing education, although there are a few students pursuing the degree full time
    Program format: Online/in-person/combination; 30 credit hours required in five years for the MS; 18 credit hours required in three years for the PMC

    The program is a combination of selected offerings in two existing rigorous graduate degree programs in applied and computational mathematics (ACM) and computer science (CS). On the ACM side, students will take a foundational course in statistical methods and data analysis, followed by required courses in optimization, statistical models and regression, and computational statistics. On the CS side, students will take a foundational course in algorithms, followed by required courses in databases, visualization, and data science. All students are also required to take one upper-level ACM elective (e.g., data mining, queuing theory, or stochastic optimization) and one upper-level CS elective (e.g., machine learning or big data processing using Hadoop). Qualified students will need to have taken three semesters of calculus (through multivariate), discrete mathematics, Java, and data structures.

    What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
    The motivation for starting the program is clear to anybody even slightly paying attention to broad trends in society toward greater quantitative analysis in decision-making and the need for processing and interpreting massive data sets in many diverse fields. JHU had a well-received non-credit sequence in data science through Coursera and the school of public health for several years, and the need for a graduate credit program was fairly clear. In response, the JHU Whiting School of Engineering, through its engineering for professionals division, took on the challenge of creating a rigorous, credit data science program based in both applied math and computer science. Relative to the number of applicants, the data science program has had an overwhelming response since the program was rolled out in fall 2016. The cumulative number of applications grew from 0 to more than 2,000 in less than two years.

    How do you view the relationship between statistics and data science/analytics?
    While there is a wide variety of data science programs, all seem to have a substantial basis in statistics. That connection is not surprising when you consider statistics is defined as the field devoted to “the practice or science of collecting and analyzing data”!

    While we will not proclaim to know “the” relationship between statistics and data science, the JHU program in data science is deeply connected to advanced methods in mathematical statistics, modeling, and computational statistics. As such, the prerequisites for the data science program involve mathematics through multivariate calculus (Calculus III), as well as a course in discrete mathematics and exposure to linear algebra and matrix theory.

    What advice do you have for students considering a data science/analytics degree?
    A prospective student needs to be strong in math and adept at programming. Someone considering the program who has not taken mathematics or programming courses in several years prior to starting the program might consider taking a refresher to “hit the ground running.” Also, for the key demographic of students who are working full time or near full time, it is recommended that students initially take only one course at a time. This allows a person to re-acclimate to academic life.

    What types of jobs are you preparing your graduates for? Describe the employer demand for your graduates/students.
    The range of jobs associated with data science, broadly defined, is almost limitless. It seems many large and small employers have people doing data science in some capacity, but without having that label in the job title. Given that most of our students are part time and are partially or fully employer funded, the students are expected to continue with their current employer. For the minority of students not employer funded, we currently have little data regarding employer demand because the program is a new offering. That being said, given the strong demand for the program, there is little doubt that those students in the job market will be able to find relevant positions.

    University of Vermont James P. Bagrow is an assistant professor in mathematics and statistics at the University of Vermont and a member of the Vermont Complex Systems Center. He has degrees in liberal arts (AS) and physics (BS, MS, and PhD).

    Jeffrey S. Buzas is professor and chair of mathematics and statistics and director of the statistics program. He has degrees in mathematics (BS) and statistics (MS and PhD).

    Margaret J. Eppstein is professor and chair of computer science at the University of Vermont and the founding director of the Vermont Complex Systems Center. She has a BS in zoology, MS in computer science, and PhD in environmental engineering.

    Peter Sheridan Dodds is a professor in mathematics and statistics at the University of Vermont, where he is also the director of the Vermont Complex Systems Center and co-director of the Computational Story Lab. PhD in Complex Systems and Data Science

    Year in which first students graduated/are expected to graduate: 2021
    Partnering departments: Vermont Complex Systems Center (lead), Mathematics and Statistics, Computer Science
    Program format: In-person (online being developed), thesis/project or coursework, 30 credit hours, traditional/non-traditional/full-time/part-time/continuing education

    We provide students with broad training in computational and theoretical techniques for describing and understanding complex natural and sociotechnical systems, enabling them to then—as possible—predict, control, manage, and create such systems.

    Our PhD is a natural addition to our educational platform, which already consists of an MS in complex systems and data science and a five-course graduate certificate in complex systems. UVM also now has an undergraduate major in data science.

    The major skill sets we aim to train include the following:

    • Data wrangling: Methods of data acquisition, storage, manipulation, and curation
    • Visualization techniques, with potential for building high-quality web-based applications
    • Uncovering complex patterns and correlations in systems through data-fueled machine learning and genetic programming
    • Powerful ways of identifying and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques

    Students must have prior coursework or be able to establish competency in the following:

    • Calculus
    • Coding (Python/R ideal, but not necessary)
    • Data structures
    • Linear algebra
    • Probability and statistics

    What was your primary motivation(s) for developing a master’s (or doctoral) data science/analytics program? What’s been the reaction from students so far?
    The basic motivation was that we live in a renaissance time with so many fields moving from data-scarce to data-rich. Students need a suite of skills to be able to contend with the kinds of broad problem solving they will face in the real world, very likely as parts of teams. These students should not be cogs with narrow training. Student response has been extremely positive.

    How do you view the relationship between statistics and data science/analytics?
    Our PhD and master’s incorporate training in computer science, statistics, mathematics, physics (mechanisms), and complex systems.

    What types of jobs are you preparing your grads for? (If you have had graduates, please summarize the types of jobs they took and in what sector.)
    Data science positions at corporations and in governments positions. Students with training that will be formally framed by our PhD have gone on to work for companies, as well as into careers in education.

    What advice do you have for students considering a data science/analytics degree?
    Students should look for data science programs that are truly interdisciplinary. They should be able to develop skills that enable them to explain patterns, and not just reproduce them or generate novel ones. While explanation is fundamental to science, it is also crucial in real-world venues to be able to understand and defend, for example, decisions proffered by algorithms for maintenance of ethical, legal, and assurance standards.

    Describe the employer demand for your grads/students.
    Very strong. We have increasingly received interest in PhD students with a deeper training.

    Do you have any advice for institutions considering the establishment of such a degree?
    Just do it. The world has changed, and it is our responsibility to adapt. We have to frame education so students will have a clear path to becoming data scientists. Many essential courses will already exist, but the development of hybrid core courses on data science will likely also be necessary.

    Section on Physical and Engineering Sciences News for April 2018

    Sun, 04/01/2018 - 7:00am
    Joanne Wendelberger, Joint Research Conference Chair

      Make your plans now to head to Santa Fe, New Mexico, for the 2018 Joint Research Conference (JRC) on Statistics in Industry and Technology, which will be hosted by the Los Alamos National Laboratory at the Drury Plaza Hotel June 11–14. JRC2018 is a joint meeting of the SPES/Institute of Mathematical Statistics Spring Research Conference on Statistics in Industry and Technology and the Quality and Productivity Section’s Quality and Productivity Research Conference.

      A short course titled “Bridging Statistics and Data Science” will be taught by Ming Li from Amazon and Hui Lin from DowDuPont. Conference activities include a tour and reception at the Los Alamos National Laboratory Bradbury Science Museum. There will also be an opportunity to experience the Meow Wolf interactive exploration of art and technology.

      The conference program committee, co-chaired by Xinwei Deng and Brian Weaver, has arranged a stellar lineup of invited sessions. Plenary speakers include this year’s conference honoree, Max Morris from Iowa State University; Scott Vander Wiel from Los Alamos National Laboratory; and Derek Bingham from Simon Fraser University. Special invited luncheon speakers include ASA President-elect Karen Kafadar of the University of Virginia, who will give a talk titled “The Critical Role of Statistics in Development and Validation of Forensic Methods,” and Francesca Samse of The University of Texas at Austin, who will discuss work color perception and scientific visualization.

      Invited Sessions

      The Technometrics invited session will feature Mickael Binois with “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments,” Joseph Guinness with “Permutation and Grouping Methods for Sharpening Gaussian Process Approximations,” and Matthias Tan with “Gaussian Process Modeling of a Functional Output with Information from Boundary and Initial Conditions and Analytical Approximations.”

      The Journal of Quality Technology (JQT) invited session will include Doug Montgomery, who will speak about 50 years of JQT; Michael Hamada, who will discuss estimation of a service-life distribution based on production counts and a failure database; and John R. Lewis, who will talk about selecting an informative/discriminating multivariate response for inverse prediction.

      Lessons Learned from Data Challenges and Challenging Data

      • Anne Hansen, “Overcoming Data Obstacles and Driving a Data Culture”
      • David Osthus, “When Flu Forecasting Isn’t About the Flu: What I’ve Learned Participating in the CDC’s Influenza Forecasting Challenge”
      • Christine Anderson-Cook, “Data Competition Hosting: Getting More Than Just a Winner Through Strategic Design and Analysis”

      Data Science in New Mexico

      • Lauren Hund, “Strategies for Calibrating Inexact Computer Models to Estimate Physical Parameters”
      • Oleg Makhnin, “gibbSeq: A Bayesian Multiple Testing Method for Genetics Applications”
      • James Degnan, “Using Approximate Bayesian Computation to Infer Evolutionary Trees”

      Test Planning for Reliability

      • Laura Freeman, “Challenges and New Methods for Designing Reliability Experiments”
      • Lu Lu, “New Developments on Demonstration Test Plans”
      • Isaac Michaud, “Using Mutual Information for Designing Sensitivity Tests”

      Astrostatistics Interest Group

      • Luis Campos, “Disentangling Astronomical Sources with Spatial, Spectral, and Temporal X-Ray Data”
      • Gwendolyn Eadie, “Estimating the Mass to Light Ratio of the Milky Way’s Nuclear Star Cluster and Its Central Black Hole”

      Design for Computer Experiments

      • Robert Gramacy, “Replication or Exploration? Sequential Design for Stochastic Simulation Experiments”
      • Matthew Plumlee, “Calibration with Frequentists Coverage and Consistency”
      • Daniel W. Apley, “Input Mapping for Calibration of High/Low Fidelity Simulation Models with Mismatched Inputs”

      Design for Physical Experiments

      • Jeff Wu, “Analysis of Marginal Tail Means: A Robust Method for Parameter Design Optimization”
      • Xun Huan, “Value of Feedback and Forward-Looking in Bayesian Sequential Optimal Experimental Design”
      • Ryan Lekivetz, “Restricted Screening Designs”

      Statistical Machine Learning

      • Tom Loughin, “Adaptively Pruned Random Forests for Modeling Means and Variances Simultaneously”
      • Nicholas Henderson
      • Rob McCulloch

      Uncertainty Quantification

      • Michael Grosskopf
      • Peter Marcy, “Bayesian Gaussian Process Models for Dimension Reduction Uncertainties”
      • Jared Huling, “Neural Networks for Flexible and Fast Emulation of Computer Experiments”

      Invited sessions on statistical process control, physics applications, and functional data are also being planned.

      Biometrics Section News for April 2018

      Sun, 04/01/2018 - 7:00am

      The Biometrics Section will sponsor seven continuing education (CE) courses at the 2018 Joint Statistical Meetings in Vancouver. Here, we highlight four of them:

      Prediction in Event-Based Clinical Trials

      Instructors: Daniel Heitjan and Gui-Shuang Ying

      Did you ever wish you could use the accumulating data from your event-based clinical trial to reliably predict its future course? Well, now you can! Give these instructors a half day at JSM 2018 and they will teach you how using their Bayesian simulation methods coded in straightforward R.

      Participants will learn about flexible parametric and nonparametric prediction models for simulating future enrollment and event histories. The instructors will describe applications to real trials, showing how you can predict the timing of future interim analyses, identify efficient enrollment strategies informed by current data, and give DSMBs the best possible information on the likelihood of trial success.

      Bring your own computer and data and give their methods a try!

      Health Care Analytics in the Presence of Big Data

      Instructor: Evan Carey

      The phrase “big data” has become widespread, but what does it mean for the practicing health care analyst? Come to this course to learn more!

      In this course, participants will gain hands-on experience using cutting-edge software tools for the analysis of large administrative health care data sets, with a focus on Python and Apache Spark. Serial and parallel optimizations techniques using frequentist statistical frameworks and machine learning frameworks will be demonstrated.

      This course will focus on methods and software, rather than the clinical context, but numerous real-world examples will be discussed that will offer a broad perspective. Students will be provided with a copy of a functioning “virtual machine” with all software and course materials pre-installed.

      Regression Modeling Strategies

      Instructor: Frank Harrell

      When was the last time you had a “statistical modeling tune-up”? How do you keep up to date with methods for developing and validating predictive models, dealing with common analytical challenges, and graphically interpreting regression models? This course is the answer!

      Here is an enlightening and extremely popular course (that’s why we offer it nearly every year) that covers multivariable regression modeling strategies, relaxing linearity assumptions, interaction surfaces, differences with machine learning, classification vs. prediction, quantifying predictive accuracy, detailed case studies using R, and more.

      Introduction to Bayesian Nonparametric Methods for Causal Inference

      Instructors: Jason Roy and Michael Daniels

      Have you ever thought about trying more innovative approaches to causal inference, but you didn’t know how to begin? Bayesian nonparametric methods (BNP) could be exactly what you are looking for!

      In this short course, expert instructors will review BNP methods and illustrate their use for causal inference in the setting of point treatments, dynamic (longitudinal) treatments, and mediation.

      The BNP approach to causal inference has several possible advantages over popular semiparametric methods, including efficiency gains, the ease of causal inference on any functionals of the distribution of potential outcomes, the use of prior information, and capturing uncertainty about causal assumption via informative prior distributions. You’ll learn even more from their wealth of examples, supported by detailed instructions for software implementation using R.

      Data Is My Job: Four ASA Members Share Career Insights

      Sun, 04/01/2018 - 7:00am
      Career opportunities are limitless with a degree in statistics or data science. To celebrate Mathematics and Statistics Awareness Month (#mathaware), we highlight four ASA members who have used their statistics skills and imaginations to snag sweet jobs. Who Are These People?
      Janet McDougall is the founder and president of McDougall Scientific consulting firm.

      Rob Santos is the chief methodologist and director of the Statistical Methods Group at the Urban Institute.

      Yihui Xie is a data scientist and software engineer at RStudio, Inc.

      Nancy Potok is the chief statistician of the United States. What do you during a typical day at work?

      McDougall: Like most professionals/managers, there is not really a typical day, but there are tasks you regularly perform. I balance managing the company with keeping up as a statistician and working with clients.

      Email. Updates on ongoing projects (sometimes resolving issues); answering clients’ questions; reaching out to potential clients or collaborators; setting up meetings; keeping up on industry changes, regulations, and statistics by being on mailing lists and reviewing the content

      Meetings. Both internal—project management, product development, finance, HR, marketing—and external—going to meet with clients offsite or having teleconferences with them

      Research. New statistical methods, therapeutic areas, regulations—usually as part of a project or a work-up for bidding on the project

      Training. Finding, organizing, and attending webinars in statistics, data management, etc.; also having statistical discussions with staff about design and analysis issues

      The one big omission is programming. I don’t meet our standards for a programmer—because of all the other distractions—and I do miss that part of the job.

      Santos: On any given day, I will be overseeing policy research projects in diverse topics like housing discrimination, refugee resettlement, urban community attachment, food insecurity, driving travel behavior, client feedback loops, and firefighter safety and risk assessment, as well as attending to internal administrative projects like IRB reviews, advancing diversity/inclusion and community engagement research methods, and chairing institutional awards committee deliberations (this is but a portion of my current portfolio).

      Xie: I answer software questions from various channels (StackOverflow, GitHub, emails, etc.) and write code, documentation, and books. I have been trying to publish one book a year since 2015, and—this year—I’m working on my fourth book.

      Potok: There is no typical day—every day is different. A lot of my job involves external relationships with stakeholders, data users, and the US statistical agencies. As a result, I may be out of my office for the greater part of the day meeting with people, speaking at conferences or other events, attending workshops, or strategizing with the statistical agency heads on our priority work areas. On other days, I am mostly in my office meeting with my small staff and guiding their activities. Some days, I brief the policy officials at the Office of Management and Budget on high-priority decisions that need to be made or documents that need to be cleared. If I am really lucky, I get to catch up on things I should be reading once in a while.

      Share with us!
      We want to see you sharing your cool data jobs with future statisticians and data scientists. Snap a selfie with our #CelebrateStatistics or #CelebrateDataScience printable posters, post them on social media, and tag @AmstatNews. Print the main poster too! How did you end up in your current position?

      McDougall: By chance. I started freelance work in the pharmaceutical industry after working at one company for about four years. Through contacts in the industry, the workload grew and I brought others onboard, then had to move the business out of my home and into a leased office. Taking on leases, it seemed appropriate to incorporate—so I did and, as the owner, became the de facto president.

      For the next couple of years, I kept my title as statistician, as that was what I was proud to be. When a future client asked if this was my father’s business, I recognized I had to take the business—and my image—more seriously and adopted the title of president. I kept the title and have grown into the position, learning to be more of a strategic thinker and planner, making important and tough decisions, mentoring, delegating—even my beloved programming—to the growing staff, dealing with all the financial administrative tasks.

      I was lucky to find good business coaches along the way who not only guided me, but also two other staff members (also statisticians) to form a management team. My role as senior statistician, where I keep abreast of the developing statistical trends and advise on design and analysis, is still what gives me the greatest pleasure.

      Santos: Through a journey-pursuing opportunity. I spent much of my career in leadership positions trying to promote the most rigorous research in university-based survey research centers at Temple University, University of Michigan, and The University of Chicago. I finally realized I wanted to be closer to the action of putting research results to use for the betterment of society and landed at the Urban Institute, a public policy research think tank.

      I then was lured back to my home state of Texas (Austin) to co-own a social science research firm, but we sold the firm after six years of amazing growth and interesting projects.

      And then I returned to Urban Institute to resume my passion for conducting research for the public good. And this is where I have been the past 11 years.

      I enjoy most the ability to play in everyone’s tent, be it justice policy, immigration, housing, hunger issues, health, education, infrastructure, program evaluation, you name it. The past decade has been the most rewarding and fulfilling period career-wise in my life.

      Xie: I wrote an R package named “knitr” in late 2011, which caught the attention of our current CEO. I met him in 2012 for the first time and collaborated on a few talks, including a keynote at the useR! conference in 2012. We had a brief phone call in 2013, when I was about to look for a job. He basically said “Yes,” and that’s it.

      I’m a statistician by training, but I love programming more than statistics. Sorry! I probably should not have said that here. Anyway, I write software primarily for statisticians and data analysts.

      Potok: The short, technically correct answer is that I applied for it on and was selected through the federal government’s merit selection process. Of course, that was after a long and varied career that spanned two tours at the US Census Bureau (first as the principal associate director and CFO and the second as the deputy director and COO); working in the private sector doing social science research and consulting; and—at various times—holding other federal positions, including deputy undersecretary for economic affairs at the Department of Commerce. I discovered I had a real passion for strategically managing data and the people who create and disseminate data to inform major policy questions and provide high-quality information that could change peoples’ lives for the better.

      What did you want to be when you were 12?

      McDougall: A scientist. I got my first chemistry set when I was around eight or nine years old and loved the sense of wonder and discovery.

      Santos: I totally wanted to be a math professor. I loved everything there was about mathematics and majored in math for my BA. But when it came time to think about graduate school, my counseling professor insisted I consider a more applied area—statistics—so I could always have a great job. Time has shown he certainly steered me well.

      Xie: I just wanted to study super hard and obtain the highest possible educational degree, which was a “postdoc” according to what people in my village told me when I was a boy. I probably also wanted to be a scientist. Now some people call me “data scientist,” which is not what I intended to be when I was 12; I had no clue about statistics until I went to college. To a child, a scientist who plays with colorful chemicals looks like more fun. Playing with data is also fun, although it is a little more abstract.

      Potok: My career goal at age 12 was to be a librarian. I loved reading, doing research, and uncovering new and interesting information. One of my favorite activities at that age was to pick up a Funk & Wagnalls encyclopedia, open it to a random page, and just start reading. Of course, at age 13, I discovered boys and my career ambition shifted quickly to wanting to be a go-go dancer for a rock band. It’s been an interesting ride since then.

      What career advice would you give your 20-year-old self?

      McDougall: Keep learning—and not just in your narrow discipline. I still over-prepare for meetings and discussions about protocol development and analyses—therapeutic areas, publications for the disease or the design—also just general trends in our society. By reading widely, the doors to serendipity open more frequently and you get wonderful insights into a problem or an opportunity that you might miss otherwise. You ‘see’ things other people miss, because your mind has been opened to different possibilities.

      Santos: I’d say pursue your passions, keep your options open, have fun, and always challenge yourself beyond your self-perceived limitations. Life’s all about the journey, not the destination. A great career and a good, fulfilling life can be had by heeding those few words.

      Xie: Try more often to do things you don’t like but are important at the same time. It is easy to do things you like, but in the real world, there is no guarantee you will like all tasks assigned to you. Don’t be afraid of the pain from challenges. If you don’t feel the pain in tackling a challenge, it basically means you are losing the chance to learn more things and grow up.

      Potok: Conquer your fear of taking big risks to follow your dreams—at 20 years old, there is plenty of time to learn from your mistakes and discover both what you are great at and what makes you happy.

      Symposium on Data Science and Statistics Promises Solid Program, Networking

      Sun, 04/01/2018 - 7:00am

      Yasmin H. Said is the 2018 SDSS Program Chair. She holds a PhD in computational statistics. Based on her research on ecological alcohol systems, she was awarded patent 7,800,616, Policy Analysis and Action Decision Tool. She was a visiting fellow at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge in England. She was a founding co-editor-in-chief of WIRES: Computational Statistics, a Wiley journal. She is an elected member of both the Research Society on Alcoholism and International Statistical Institute.

      The ASA Symposium on Data Science and Statistics (SDSS) is designed for data scientists, computer scientists, and statisticians who analyze and visualize complex data. The 2018 symposium returns to Reston, Virginia, the site of the original 1988 symposium held under the newly incorporated Interface Foundation of North America (IFNA). The SDSS series, beginning this May, is a joint collaboration of the ASA, assuming responsibility for administrative management, and the Interface Foundation, retaining responsibility for the direction and intellectual focus. 

      The SDSS program offers short courses, concurrent sessions, electronic poster sessions, exhibits, and many opportunities for networking. Emery N. Brown, a well-known scholar with a medical focus on anesthesiology and neuroscience, will give the keynote address: “Uncovering the Mechanisms of General Anesthesia: Where Neuroscience Meets Statistics.”

      The plenary talks will feature David Scott from Rice University, David Brillinger from the University of California at Berkeley, Jerome Friedman from Stanford University, and Adalbert Wilhelm from Jacobs University in Germany.

      The invited program includes session tracks on data science, data visualization, machine learning, computational statistics, computing science, and applications and features well-known scholars such as Leland Wilkinson, Roy Welch, Wayne Oldford, Edward George, William Cleveland, David Banks, Michael Trosset, Menas Kafatos, Nozer Singpurwalla, Lynne Billard, Carey Priebe, Douglas Nychka, Kirk Borne, and Claudio Cioffi-Revilla. In addition, there will be a number of contributed and electronic poster sessions. In total, there will be approximately 300 presentations split nearly equally between invited and contributed talks, as well as poster sessions spanning an array of topics.

      A key feature of SDSS is a collection of short courses. These short courses will focus on the latest software tools, technologies, and methodologies for data science—including the Hadoop, R, and Spark ecosystems—and give participants hands-on experience. A number of high-profile technology companies will present these short courses, as well as invited talks, including Cloudera, Databricks, Domino,, IBM, Microsoft, RStudio, and SAS.

      There will be many opportunities for networking and social interaction with ample breaks, continental breakfasts on Thursday through Saturday, an opening mixer on Wednesday, and a symposium banquet on Thursday. Barry Nussbaum, 2017 ASA president, will be the banquet speaker, giving a light-hearted talk titled, “I Never Met a Datum I Didn’t Like.”

       The 2018 SDSS is being held in honor of Edward Wegman, the founder and a key person in IFNA, serving as treasurer for some 30 years. He was the founding chair of the statistics department at George Mason University and developed both the MS in statistical science and PhD in computational science and informatics there. He has been dissertation director for 44 doctoral students, with seven additional students in candidacy. After a 32-year career at George Mason, Wegman will retire at the end of May as professor emeritus. He earned his PhD in May 1968 from the University of Iowa. Several sessions in this first SDSS are dedicated to his contributions to the profession. 


      Longtime Members Offer Wisdom

      Sun, 04/01/2018 - 7:00am
      We interviewed a few longtime members to find out why they remain members of the ASA and how they have benefited from being a member. Here is what they had to say:

      Janet Myhre

      Member for more than 50 years

      Being a mathematical applied statistician has allowed me to have a career in which I am continuously learning, always involved in problem solving, and never bored. My first interest in statistics occurred as a graduate student at the University of Washington. Z. W. Birnbaum was instrumental in founding my interest in probability and mathematical statistics.

      One of my careers has been as a professor of mathematics at the Claremont Colleges and University. My teaching involved instructing courses in probability, theoretical and applied statistics, problem solving, data analysis, and statistical thinking for engineers. Other activities included founding the Reed Institute for Applied Statistics. This institute funded summer research for undergraduates and facilitated the process of obtaining funding for courses in applied statistics. In these courses, problems were elicited from government and business, which involved data and could be solved using applied statistics. A course in applied statistics was designed to analyze one of these problems during a semester course. Students received course credit and provided the client with written and oral reports.

      My second career, concurrent with my first career and still active, has been as a statistical consultant to the US Navy. This career has been extremely rewarding. The problems to be solved are often complex and require additional theory. The solutions are made possible by teamwork with exceptionally well-informed and dedicated naval officers, engineers, and scientists.

      How has your professional and/or personal life been affected by being a member of the ASA?

      The ASA has helped my professional life by providing statistical problem solving information both online and in professional meetings. My years as an associate editor of Technometrics and later as chair of the Committee for Careers in Statistics were both informative and rewarding.

      Is there anything you wish someone had told you when you embarked upon your career that you would like to tell others now? 

      One wants to find a career where the work is so enjoyable you never want to stop, where being involved in your work is one of the most rewarding things you do.

      Betty Skipper

      Member since 1963

      I majored in mathematics at Oberlin College, and I was the first in my family to graduate from college.

      During my senior year in college, I saw an announcement on a bulletin board about a biostatistics graduate program at Western Reserve University (WRU). A professor from that program came to campus that afternoon to talk to students. I had never heard of biostatistics. It was explained as an opportunity to combine mathematics and science. The coursework consisted of two years of statistics at Iowa State University (ISU) and the first year of medical school at WRU. I applied and received a pre-doctoral fellowship.

      As I was finishing my dissertation, a professor at WRU told me he was moving to a new medical school at the University of New Mexico (UNM) and would be hiring biostatisticians. I applied, intending to move back to the Midwest in two years. Forty-eight years later, I retired as a tenured professor from UNM and now work part time. I met my late husband in Albuquerque and have two grown children and four grandchildren.

      I have spent my career as an applied biostatistician, collaborating with students and health professional colleagues, as well as teaching biostatistics courses. I am particularly interested in mentoring students and junior faculty and teaching statistical concepts to health professionals who are not statisticians. My combined training in statistics and medical sciences has been an important asset. Over the years, I have seen major changes in statistical practice—from mechanical calculators and computer punch cards to modern computers and statistical software.

      Although I didn’t start with specific plans for this career, it has been a rewarding career. I have been privileged to work with many students and faculty colleagues at the University of New Mexico.

      How has your professional and/or personal life been affected by being a member of the ASA?

      Continuing education through local chapter meetings, publications, and short courses.

      What or who inspired you to become a statistician?

      There was really no one who inspired me to become a statistician. As I mentioned in the career summary, I saw an announcement on a college bulletin board and decided to apply without really knowing much about it.

      Dennis Boos

      Member since 1974

      How has your professional and/or personal life been affected by being a member of the ASA?

      The ASA has been the central professional organization in my career. I have attended most JSMs over the last 40 years. In fact, we often made the JSM a family vacation. (My children loved the hotels when they were young!) I have been involved in a number of committees and have appreciated the organizational professionalism of the ASA. Of course, JASA and TAS have been important journals for me, and I still get hard copies.

      What or who inspired you to become a statistician?

      In the summer of 1973, I visited the department of statistics at Florida State looking for a career change. My undergraduate degree was in physics, but I had become disillusioned with the heavy dependence of physics on the defense industry for funding. I first talked to a math professor friend about applied math, but he suggested walking down the hall and talking to someone in statistics. (He saw the future!) So, the associate head, Fred Leysieffer, told me about statistics—I had no clue about the field—and I applied to graduate school soon after.

      Is there anything you wish someone had told you when you embarked upon your career that you would like to tell others now?

      I guess it would have been nice for me to understand the entrepreneurial nature of an academic career. As a junior, I wasn’t proactive enough in making connections with scientists and other statisticians. Fortunately, North Carolina State is a warm and nurturing environment for young faculty.

      Calvin Zippin

      Member since 1947

      In 1947, upon graduation from SUNY-Albany with majors in biology and mathematics and a course in statistics, I was hired by the Sterling-Winthrop Research Institute—the research arm of a large pharmaceutical firm—to do a variety of chores, including statistical analysis of laboratory and clinical data. That same year, I joined the Albany Chapter of the ASA. Three years later, with a full tuition scholarship, I began graduate work in biostatistics at Johns Hopkins under the revered William G. Cochran.

      With my ScD degree in hand, I accepted a faculty position in the school of public health at the University of California, Berkeley in 1953 and transferred in 1955 to the school of medicine in San Francisco (UCSF) with appointments in the Cancer Research Institute and the department of preventive medicine. In addition to teaching and research, I served as the campus’ only consulting biostatistician. This resulted in involvement in a great variety of fascinating research areas. I continued to be active with the San Francisco Bay Area Chapter of the ASA.

      As head of the UCSF cancer registry and with ties to state and national cancer data systems, my work became focused on the biometry and epidemiology of cancer. Long-term continuing support from the National Cancer Institute fueled my research and led to travel throughout the world and service on national and international committees.

      Some of the ways in which I was further rewarded was election to fellowship in the ASA, service as president of the Western North American Region of the Biometric Society, membership in COPSS, and receipt of a lifetime achievement and leadership award from the National Cancer Institute. I am currently professor emeritus of epidemiology in the department of epidemiology and biostatistics at UCSF.

      How has your professional and/or personal life been affected by being a member of the ASA?

      Attending ASA meetings in the early days brought me in contact with persons with similar and diverse interests within statistics, and subject matter talks helped me try to keep current on developments within the field.

      Membership in the ASA provided much of the basic grounding for my career. Rather than cite an individual experience, I will list several that stand out in my memory:

      • Four trips to the Soviet Union during the Cold War for an international cancer congress and as part of a US-USSR collaborative project on breast cancer
      • Participation in an international World Health Organization meeting on the importance of teaching statistics to medical students held in the unusual location of Karachi, Pakistan
      • Meeting with staff of the Atom Bomb Casualty Commission in Hiroshima, Japan, while doing research on late effects of radiation

      At the request of the American Association for the Advancement of Science, I interviewed in 1978 in Buenos Aires mothers of missing abductees (some scientists) during the so-called “dirty war” in Argentina. 

      What or who inspired you to become a statistician?

      I credit Lloyd C. Miller, my boss at the Sterling-Winthrop Research Institute, with inspiring me to go into biostatistics. Although he was primarily a pharmacologist, he had worked with Chester Bliss of Yale on bioassay and became aware of the critical importance of statistical methodology in research. He went on to become director of revision of the United States Pharmacopoeia for 20 years. His encouragement led to my going on to graduate work in biostatistics.

      Is there anything you wish someone had told you when you embarked upon your career that you would like to tell others now? 

      For anyone interested in concentrating on an applied area of statistics, I would emphasize the importance of learning as much about the subject matter of that field as well as that of statistics.

      I was interested in biology and the health sciences. At the time of my graduate work at Johns Hopkins, we were required to take courses comprising most of the first two years of the medical curriculum in addition to work in biostatistics. For me, this was a blessing.

      Also, I would say a career in statistics can open itself to the most exciting and unexpected avenues of fulfillment. Keep your sights high. Expect the unexpected!

      Steve Ascher

      Member since 1974

      I earned my PhD in 1978 from SUNY at Buffalo. My first job was as an assistant professor at Temple University (1978–1983). At Temple, I revived the undergraduate major in statistics, which had been dormant for many years.

      In 1983, I decided to go into industry and joined McNeil Pharmaceutical as a statistician. It was there that I learned about the complexities of drug development and how incredibly complex it is to get a new drug on the market. From 1993–1999, I worked at IBRD and Covance (two contract research organizations (CROs)). I received my first managerial experience at both organizations as I headed two small statistics groups.

      Working on both the client side and the contractor side gave me a better understanding of how to build beneficial relationships between clients and CROs. I returned to Johnson & Johnson (Janssen) in 2000 to build a phase 4 statistics/programming/data management group.

      My CRO experience was helpful, as our business model at Janssen was to use CROs. While at Janssen, my group supported numerous world-wide neuroscience trials. We also assisted in writing posters, abstracts, and manuscripts. In addition, I co-founded a mentoring program for J&J statisticians and programmers. During my J&J tenure, I became involved with the ASA New Jersey Chapter and served as president for four years and vice president for two years. I am currently secretary. We sponsor workshops, webinars, and career days for statistic graduate students and high-school students.

      I retired in May of 2016 and still keep active with the ASA New Jersey Chapter and review papers for two veterinary journals and one sports in statistics journal. In addition, I do horse show announcing and keep busy with my sports memorabilia collection.

      How has your professional and/or personal life been affected by being a member of the ASA?

      Being a member has allowed me to keep up with the latest advances in statistics through subscribing to journals, belonging to various chapters and sections, and attending events for knowledge and networking. I have recommended membership to students and colleagues throughout my career.

      Will you share an experience that stands out to you regarding your ASA membership?

      What stands out for me is getting more involved in the ASA New Jersey Chapter. I have been president (four years), vice president (two years), and am currently secretary. I have gotten to work with my wonderful and dedicated officers and have always been very appreciative that all of the speakers we have had at various events do this for no financial gain. The volunteer spirit and giving back to the statistics community is alive and thriving! Through the New Jersey Chapter, I have also gotten involved in career days for both statistics graduate students and high-school students. The future of our profession lies with them.

      What or who inspired you to become a statistician?

      I was good at math growing up and, as a baseball fan, wondered how baseball statistics were computed. Whereas many of my friends wanted to be doctors, lawyers, etc., I wanted to be the statistician for the New York Mets. When I started my undergraduate studies at SUNY at Buffalo in 1970, I was a math major. A friend thought I might like to take a statistics course as a way to apply math. I did and earned my BA in math/stat in 1974, my MS in statistics in 1976, and my PhD in statistics in 1978—all from SUNY at Buffalo.

      Is there anything you wish someone had told you when you embarked upon your career that you would like to tell others now? 

      The importance of written and oral communications. I stress this when I give presentations at career days. Today, communications all too often are in tweets, emails, Instagram, etc., where people “talk” in shorthand (e.g., LOL, UR, etc.). The art of face-to-face communication seems to be fading. As I learned during my career, one needs to be able to present findings to people in a clear and concise manner. Statisticians do not just compute p-values!

      The Value of CSP 2018

      Sun, 04/01/2018 - 7:00am
      Photo by Sara Davidson/ASA CSP 2018 attendees introduce themselves to one another at the ice-breaker during the keynote address on February 16. Photo by Sara Davidson/ASA ASA President Lisa LaVange gives the keynote address at CSP 2018 February 16. ASA photo Attendees chat at the CSP 2018 Opening Mixer. ASA Photo Refreshment breaks offer another opportunity to meet other attendees. ASA Photo Tania Patrao presents a poster at CSP 2018. Photo by Meg Ruyle/ASA Theresa Henle, Brianna Heggeseth, and Christina Knudson get acquainted at the CSP 2018 Opening Mixer. Photo by Sara Davidson/ASA Attendees converse during a short course at CSP 2018.
      Conference focuses on people and interactions, not just talks and presentations Jean V. Adams, CSP 2018 Steering Committee Chair

      The 7th annual Conference on Statistical Practice was held in Portland, Oregon, Feb 15–17. There were talks, short courses, posters, and blah, blah, blah. You’ve been to conferences. You know what they’re about. Watching and listening to presentations. Right?


      Consider the TED talks. They are all available online for free. Yet people spend thousands, even tens of thousands, of dollars to attend the TED conference in person. Why?

      The people.

      A conference is all about the people. The word conference comes from the Latin word conferre, which means “to bring together.” To bring people together, you need to have some enticement. The Conference on Statistical Practice does this by inviting abstracts from potential speakers and instructors and selecting only the best for inclusion in the program. But, putting people in the same place at the same time is just the first hurdle. What’s the second?

      Connecting them.

      The value is in the interactions. To encourage people to interact with each other, you need to create a safe space and remove barriers to communication. The Conference on Statistical Practice focuses on this second hurdle to ensure attendees get the most out of the conference. We limit attendance to keep the conference small. We keep the meeting rooms close to the shared space to encourage mixing. We include an ice-breaker at the keynote. We encourage folks to gather together for meals. We connect mentors and mentees.

      Our focus on people and interactions was effective. Just listen to what attendees had to say about the 2018 Conference on Statistical Practice:

      “I was surprised by how much people are using this meeting to connect with each other, even beyond the scheduled events. A lot of small professional networks (formal and informal) were taking advantage of the opportunity.”

      “I met a woman who started graduate school to get a degree in biostatistics. She was an opera singer before this! I think she wants to bring her creativity to statistics.”

      “It was really interesting to meet people with such diverse backgrounds and at different stages in their careers and studies.”

      Many thanks to ASA President Lisa LaVange, who kicked off the meeting with her keynote address, “Reflections on Career Opportunities and Leadership in Statistics.” LaVange also led a panel session on her #LeadWithStatistics initiative to establish a leadership institute at the ASA.

      Interact at next year’s Conference on Statistical Practice in New Orleans, February 14–16.

      Climate Science Day Participants See Change of Tone on Capitol Hill

      Sun, 04/01/2018 - 7:00am

      Photo by Steve Pierson/ASA From left: The ASA’s 2018 Climate Science Day participants—Peter Bloomfield, Bo Li, Dorit Hammerling, and Leonard Smith—gather in front of the American Association for the Advancement of Science building. Photo courtesy of Carissa Bunge Bo Li with teammate Rachel Kirpes outside the Capitol Photo by Steve Pierson/ASA Leonard Smith meets with his representative, John Rutherford (R-FL). Photo courtesy of Julia Marsh Dorit Hammerling and her teammate, Matthew Hurteau, outside the office of Sen. Tom Udall (D-NM)

      The ASA Advisory Committee on Climate Change Policy and the Section on Statistics and the Environment sent four statisticians to the eighth annual Climate Science Day (CSD) on Capitol Hill at the end of January. Peter Bloomfield, Dorit Hammerling, and Leonard Smith were return CSD participants, while Bo Li participated for the first time.

      CSD’s purpose is to connect scientists with congressional lawmakers and their staffs to discuss climate science, a purpose that is quite broad considering the wide range of views and interests across the 50 states and 435 congressional districts. The scientists attended a series of briefings earlier in the day to prepare them for this range. The briefings included a discussion with a panel of Hill staffers, tips for Hill visits, strategies for communicating about climate science, a keynote speaker, and time for each team—two scientists of different disciplines and a science society staff person—to prepare specifically for their meetings. This year’s keynote speaker was Laura Helmuth, the health, science, and environment editor at The Washington Post.

      With a goal for productive, engaging discussion, the request made to congressional offices in the past was to consult CSD participants or the sponsoring organizations when they had questions regarding climate science. This year, however, CSD participants were encouraged to focus on connecting the mainstream scientific view with current and potential future impacts of climate change in their districts for offices that have not yet acknowledged the scientific community’s view of climate change as, for example, was stated in the 2016 letter signed by 31 scientific organizations, including the ASA.

      For offices whose views were more congruent with the mainstream scientific view, the discussions were about being a resource and the risks specific to the district/state.

      The 19 2018 CSD participants—sponsored by 10 science associations—collectively had 70 meetings. As reported last year, participants again noted the changing tone of climate change discussions in many offices. Where once there was polite, but short and nonengaging discussions, there was more open discussion about the impacts of climate change in the district or state and even the political challenges.

      Hammerling, who participated in her second CSD, also mentioned she enjoyed the interaction with other CSD scientists and the preparatory briefings. Li commented on the importance—as part of a scientist’s responsibility to society—of regular communication with policymakers and providing updates from the research community about new discoveries and developments.

      Statisticians Leading with Justice for All

      Sun, 04/01/2018 - 7:00am

      In the past two issues of Amstat News, I have focused on building the ASA Leadership Institute. This month, I want to highlight another 2018 presidential initiative—expert witness training. The idea for such a program came from our membership.

      Early in 2017, Executive Director Ron Wasserstein heard from several members about whether the ASA could help prepare consulting statisticians for service as expert witnesses in a trial or deposition. Around the same time, I had occasion to talk with a former University of North Carolina biostatistics student, Naomi Brownstein, and she described her interest in being an expert witness. Now a statistics faculty member at Florida State University, Naomi had been approached about serving in this capacity. So, Ron and I put our heads together to assess the need for this kind of training and came up with a proposal.

      There are areas of the law that involve quantitative expertise. Ensuring there are qualified statistical professionals in the courtroom or otherwise involved with the legal process in those areas would improve the quality of the legal process and increase recognition of the important contributions of statistics and statisticians.

      There are many leaders in our field who regularly step up to serve the courts on a variety of important topics, and our sister fields—such as mathematics—are also stepping up to contribute. Gerrymandering of legislative districts, for instance, is one topic that has drawn attention of late. Gerrymandering struck a chord with me, living in two states (Maryland and North Carolina) that have been accused of extreme partisan gerrymandering—but in opposite political directions. Other topics include investigations of Medicaid fraud by medical providers and using “risk-limiting audits” to detect problems with elections. These topics often end up in court, and statisticians should be prepared to chip in.

      One aspect of the FDA’s mission to protect and promote the public health that I did not fully appreciate until working there was the need to ensure product claims made in labeling and advertising were accurate and did not mislead the public. Evaluating the evidence to determine whether product statements are misleading is the subject of FDA guidance documents, but these determinations often end up in court, where a company’s first amendment rights in making claims about a product are weighed against the agency’s need to ensure such claims do not mislead the public.

      A recent example is the Federal Trade Commission’s suit against Quincy Bioscience that questioned the use of secondary analysis to support a claim about a treatment for memory loss after the trial failed on its primary endpoints. The FTC lost the claim, and no statisticians were involved as expert witnesses due to the nature of the suit, but statistical theory about inference in general and multiplicity in particular formed the basis of the FTC’s argument.

      Given the interest in training among ASA members, requests for ASA’s assistance as expert witnesses from other parties, and my own interest in this topic, Ron and I presented our proposal for an expert witness training program to the board, and thus was born a presidential initiative.

      Our vision is to develop a program that provides the general skills and knowledge a statistician should have to be an expert witness, as well as prepare participants to speak as an expert on at least one subject matter area. To this end, we formed a working group made up of leading statisticians Joseph L. Gastwirth, Mary W. Gray, Nicholas P. Jewell, and Rochelle E. Tractenberg and asked Kathy Ensor of Rice University to be the chair. The assembled team includes a lawyer and statisticians with courtroom experience.

      I asked Kathy, as chair, to report on the deliberations of the working group to date, and here is what she had to say:

      Leaders in our field have often provided expertise to the courts and Congress, many learning by experience. The objective of this training program for statistical expert witnesses is to help our community, especially those new to our profession, expedite the learning curve on how to best serve the courts as an expert statistician.

      There were exciting suggestions for what should and should not be included in a training workshop. Although our experiences varied, several common themes emerged, including the following:

      • What it means to be an expert witness
      • What it means to be an expert statistical witness
      • Voicing a clear unbiased statistical opinion at all stages of the legal process
      • Ethical considerations as practicing professional statisticians
      • Common mistakes and pitfalls to avoid

      The role our profession has played in the courts throughout history is laudable. The role of the expert statistician emerged strongly in the 1980s. As a young statistician, I recall reading with great enthusiasm the text “Statistics and the Law” by [Morris] DeGroot, [Stephen] Fienberg, and [Joseph] Kadane and then later Jay Kadane’s 2008 book, “Statistics in the Law: A Practitioner’s Guide, Cases, and Materials.” I guess this speaks to my love of statistics and its application, as I read the books with the deep immersion great novels require.

      The vast array of areas in which statisticians interact with the courts simply boggles the mind—areas such as employment discrimination, DNA, medical practice, environmental issues, patent challenges, economic risk, and financial fraud.

      The recent creation of the National Institute of Standards and Technology–supported Center for Statistics and Applications in Forensic Evidence (CSAFE) recognizes the important role statisticians play in forensic science and hence the legal system. The expert witness working group also noted emerging areas that include statistical and machine learning algorithms potentially guiding decisions of the courts and the criminal justice system. Equally as broad as the societal issues statisticians are asked to address are the areas of statistics in which statisticians serve as experts. And given the demand for our expertise, we as a committee were reminded that one important component to serving as an expert is knowing when to decline a request.

      We are developing a training program with the following key goals in mind:

      • Quality – Develop a program that provides excellent training and meets, or even exceeds, the needs of our members
      • Impact – Develop a program that can reach a substantial number of people over time
      • Sustainability – Develop a program that can be offered regularly and support itself financially

      An RFP for training program development will be announced, pending approval by the ASA Board. The general goal is to begin the program either in the fall of 2018 or spring of 2019. Once developed, this program will become part of the ASA Leadership Institute, offered at a frequency deemed helpful to our community.

      My thanks go to Kathy and her team for the work accomplished so far, and I look forward to seeing this program roll out in the coming months. I believe the program will be a valuable resource to ASA members, and we have certainly heard from several who are anxiously awaiting its inception. This is yet another example of an area in which strong statistical leadership can have an impact that extends far beyond our membership.

      So, here’s to statisticians leading with justice for all!

      Kimberly F. Sellers

      Thu, 03/01/2018 - 6:00am

      Department of Mathematics and Statistics, Georgetown University

      Educational Background
      The George Washington University, DC: PhD, Statistics (2001)
      University of Maryland, College Park: MA, Mathematics (1998)
      University of Maryland, College Park: BS, Mathematics (1994)

      About Kimberly
      Kimberly Sellers is a statistician and associate professor of mathematics and statistics specializing in statistics at Georgetown University in Washington, DC, and a principal researcher with the Center for Statistical Research and Methodology Division of the US Census Bureau. A DC-area native, she completed her BS and MA degrees in mathematics at the University of Maryland College Park. During her graduate studies in mathematics at Maryland, she became inspired to study statistics, thanks to instruction by Professor Piotr Mikulski. After completing her MA degree, she earned her PhD in mathematical statistics at The George Washington University, partly through support as a Gates Millennium Scholar (one of the inaugural cohort recipients).

      Sellers held faculty positions at Carnegie Mellon University as a visiting assistant professor of statistics and the University of Pennsylvania School of Medicine as an assistant professor of biostatistics. She was also a senior scholar at the Center for Clinical Epidemiology and Biostatistics before her return to the DC area.

      Sellers’ research areas of interest and expertise are in generalized statistical methods involving count data that contain data dispersion and image analysis techniques, particularly low-level analyses including preprocessing, normalization, feature detection, and alignment. Her primary research centers on the Conway-Maxwell-Poisson distribution. Sellers is the leading expert on this distribution, having developed various statistical methods associated with distribution theory, generalized regression models, control chart theory, multivariate distributions and analysis, and stochastic processes for count data expressing data dispersion. Her single-authored and collaborative works have been published in prominent journals, and she is a nationally and internationally invited speaker.

      Sellers is likewise recognized both nationally and internationally for her service activities. She is an associate editor for the Journal of Computational and Graphical Statistics and The American Statistician. She is also an active contributor to efforts to diversify the fields of mathematical and statistical sciences, both with respect to gender and race/ethnicity. Last but not least, she is the 2017–2018 chair for the American Statistical Association’s Committee on Women in Statistics and an advisory board member for the Black Doctoral Network.

      What Does Claire Bowen Like to Do When She Is Not Being a Statistician?

      Thu, 03/01/2018 - 6:00am
      This column focuses on what statisticians do when they are not being statisticians. If you would like to share your pastime with readers, please email Megan Murphy, Amstat News managing editor.


      Bowen, after a day of training for the Ironman, hulks in front of the White House.



      Who are you, and what is your statistics position?

      My name is Claire Bowen, and I am a statistics PhD candidate in the applied and computational mathematics and statistics department at the University of Notre Dame.

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

      When I’m not being a statistician, I participate in endurance races such as marathons and triathlons.

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

      During high school, I was overweight and could barely run a mile. Originally, I joined the cross country team as the student manager and started running to keep up with the team better. Through the encouragement of the team and coach, I changed to a runner by the end of the season.

      Bowen, officially an Ironman, finishes the Santa Rosa Ironman race in 2017.

      Since then, I discovered I enjoy long-distance races. I completed my first marathon when I was 19 and my first half-Ironman when I was 24. My goal is to complete an Ironman before I am 30.

      I continue this hobby to help maintain a healthy lifestyle and for the overall positivity I’ve experienced from running and triathlon communities. Being overweight before, my training keeps my physical and mental health in check more easily. I love that I can do other physical activities like hiking and snowboarding without being exhausted afterward. My stress has gone down considerably, and training breaks up my day when I’m stuck on a research problem (or sometimes I figure out the problem during a run).

      I joined the local biking and triathlon clubs, where I have the opportunity to meet people in different social groups. In these endurance race communities, everyone is encouraging and positive about the sport they’re in, because it doesn’t matter how you perform in the races. What matters is that you are swimming/biking/running and you are doing it for you, becoming the best you can be.

      Bonnie K. Ray

      Thu, 03/01/2018 - 6:00am

      Vice President, Data Science, Talkspace

      Educational Background
      Columbia University (Office of Naval Research Graduate Fellow): PhD, Statistics (1991)
      Baylor University: BSc, Mathematics (1985)

      About Bonnie
      Bonnie Ray currently leads data science activities at Talkspace, a NYC-based startup that enables improved mental health for all by providing an affordable, accessible, and secure platform for messaging-based psychotherapy. Previously, she led the data science team at Arena, a Baltimore-based startup focused on analytics-driven hiring assessments. From 2001–2015, she held positions of increasing responsibility at IBM Research, where her role immediately prior to moving to the start-up world was director of cognitive algorithms and she led teams charged with developing machine learning methods for next-generation AI applications.

      A graduate of Columbia University and Baylor University, Bonnie started her career in academia as a post-doctoral fellow at the Naval Postgraduate School working with the late Professor Peter Lewis on his study of sea surface temperatures. She went on to hold assistant and associate professor faculty positions in the applied mathematics department at the New Jersey Institute of Technology, during which time she received three National Science Foundation awards, including a CAREER grant awarded to promising young scientists that funded her educational and research activities related to environmental time series analysis.

      Bonnie was born and raised in the Deep South, living first in Mississippi and then in northern Louisiana before attending college in the heart of Texas. She always had a love of mathematics, but was introduced to statistics as a junior in college and knew almost immediately that a continuing study of pure mathematics was not to be. Summer internships at Texas Instruments after her junior and senior years helped her appreciate the importance of mathematics to address business challenges and the power of computing to obtain efficient solutions, both of which continue to serve as career touchstones.

      Bonnie has published more than 60 refereed papers, holds 12+ patents, and is an elected Fellow of the American Statistical Association. In her spare time, she loves to swim, watch independent films, and relax with a good book.

      Wendy L. Martinez

      Thu, 03/01/2018 - 6:00am

      Photo by Barbi Barnum from
      Studio B Photography

      Director, Mathematical Statistics Research Center, Office of Survey Methods Research, Bureau of Labor Statistics

      Educational Background
      Joint Program in Survey Methodology Certificate in Survey Statistics (2015)
      George Mason University: PhD, Computational Sciences and Informatics (Computational Statistics area) (1995)
      The George Washington University: MS, NASA Langley Research Center, Aerospace Engineering (1991)
      Cameron University: BS, Physics and Mathematics (1989)

      About Wendy
      Wendy Martinez was born and raised in Detroit, Michigan. After high school, she served as an active-duty member of the US Army Signal Corps, where she had the opportunity to be stationed in Germany for several years.

      Martinez has been serving as the director of the Mathematical Statistics Research Center at the Bureau of Labor Statistics (BLS) for six years. Prior to this, she worked in several research positions throughout the Department of Defense. She held the position of science and technology program officer at the Office of Naval Research, where she established a research portfolio comprised of academia and industry performers developing data science products for the future Navy and Marine Corps.

      Her areas of interest include computational statistics, exploratory data analysis, and text data mining. She is the lead author of three books on MATLAB and statistics. These books cover topics ranging from classical approaches in statistics to computationally intensive methods and exploratory data analysis. She became interested in data science when pursuing her PhD under Edward Wegman (GMU), who had founded a new curriculum in computational statistics that included most of the courses in what is now considered data science.

      Martinez was elected a Fellow of the American Statistical Association in 2006 and is an elected member of the International Statistical Institute. She was recently honored by the American Statistical Association when she received the ASA Founders Award at the 2017 Joint Statistical Meetings.

      Kristian Lum

      Thu, 03/01/2018 - 6:00am

      Human Rights Data Analysis Group

      Educational Background
      Rice University: BA, Statistics and Mathematics (2006)
      Duke University: PhD, Statistical Science (2010)

      About Kristian
      Kristian Lum is the lead statistician at the Human Rights Data Analysis Group (HRDAG). Previously, she worked as a data scientist at a small technology startup and was a research assistant professor in the Virginia Bioinformatics Institute at Virginia Tech.

      Lum’s research primarily focuses on examining the uses of machine learning in the criminal justice system, developing new statistical methods that explicitly incorporate fairness considerations, and advancing HRDAG’s core statistical methodology—record-linkage and capture-recapture methods for estimating the number of undocumented conflict casualties. Although statistics and other quantitative disciplines are not the typical path to social impact, Lum is drawn to applications in which she can use her knowledge of statistics to address important societal problems and amplify the voices of marginalized groups and individuals.

      In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.

      Lum’s work on record linkage and capture-recapture focuses on solving problems that arise in HRDAG’s conflict casualty estimation work. In capture-recapture, she has proposed methods to obtain reliable casualty estimates, even when some victims are unlikely to be recorded. She is also the primary developer of the DGA package for R, which implements a popular Bayesian method for capture-recapture.

      Lum is originally from Auburn, California—a small town in the foothills of the Sierra Nevada mountains. For as long as she can remember, she has enjoyed math and logic puzzles. Her interest in math took off in high school when she took calculus from an excellent professor at the local community college, Sierra College. For the first time, she felt she was learning why math worked, rather than how to manually implement an algorithm to solve an equation. In college, she took a few statistics courses as electives for her math degree and grew interested in discovering patterns in data that explain real-world phenomena. This led her to graduate school in statistics, where she first became involved with HRDAG after “cold emailing” the organization’s founder. She has been focused on applying statistics to important problems in human rights ever since.

      ASA, AMS Issue Joint Statement on Drawing of Voting Districts, Partisan Gerrymandering

      Thu, 03/01/2018 - 6:00am

      The American Statistical Association and Council of the American Mathematical Society (AMS) issued a joint statement to inform discussions and planning around the drawing of voting districts as we approach the 2020 census. This marks the first time in recent history the two organizations have issued a joint statement of broad interest to the American public.

      The statement is organized around the following three facts:

      • Existing requirements for districts generally do not prevent partisan gerrymandering.
      • It has become easier to design district plans that strongly favor a particular partisan outcome.
      • Modern mathematical, statistical, and computing methods can be used to identify district plans that give one of the parties an unfair advantage in elections.

      “While these points may be common knowledge in some circles, it’s important they be stated by objective and respected authorities like the AMS and the ASA and for them to be more widely known in the redistricting discussions around the 2020 Census,” noted 2018 ASA President Lisa LaVange.

      AMS President Ken Ribet said, “Our community is poised to play a central role in ongoing discussions about methods for creating voting districts and the evaluation of existing and proposed district maps. It has been a pleasure for me to observe the recent explosion in interest in this topic among colleagues and students in mathematics and statistics. I anticipate that the new statement by the ASA and AMS Council will lead to increasing transparency in the evaluation of districting methods.”

      “Statistical and mathematical standards and methods can be very helpful to inform decision-makers and the public about partisan gerrymandering,” remarked the statement’s main architect, Jerry Reiter, chair of the ASA Scientific and Public Affairs Advisory Committee. “The statement acknowledges the value of partisan asymmetry as a standard, and it highlights some methods for measuring partisan asymmetry. The statement does not endorse any one method, as ultimately this issue is determined by policymakers and the courts.”

      In issuing the statement, the two societies also offer to connect decision-makers and policymakers with mathematical and statistical experts.

      Celebrating Women in Statistics and Data Science

      Thu, 03/01/2018 - 6:00am
      In honor of Women’s History Month, we are celebrating several ASA women who work in statistics and data science. These accomplished women were chosen because they inspired and influenced other women in their field. Read their biographies to learn why they chose statistics, who influenced them, and what all they have accomplished.

      Emma Benn

      Alicia Carriquiry

      Mine Çetinkaya-Rundel

      Beth Chance

      Marie Davidian

      Rebecca Doerge

      Francesca Dominici

      Michelle Dunn

      Montse Fuentes

      Rachel M. Harter

      Amy Herring

      Monica Jackson

      Frauke Kreuter

      Sharon Lohr

      Kristian Lum

      Wendy Martinez

      Sally Morton

      Bhramar Mukherjee

      Susan Murphy

      Bonnie Ray

      Rachel Schutt

      Kimberly Sellers

      Dalene Stangl

      Jessica Utts

      Daniela Witten

      Dawn Woodard

      Xihong Lin

      Linda Young

      Bin Yu

      Hao Helen Zhang

      Tian Zheng

      Kelly Zou

      12th ICHPS Takes Place in Charleston, Breaks Records

      Thu, 03/01/2018 - 6:00am

      Glynis S. Ewing discusses her poster, “Alright Ladies, Buckle Up: The Effects of Seating Position, Gender, and Other Factors on Seat Belt Usage Rates and Data-Driven Policy Solutions.”

      More than 350 statisticians, methodologists, and health policy experts gathered January 10–12, 2018, at the Marriott Hotel in Charleston, South Carolina, for the 12th International Conference on Health Policy Statistics (ICHPS). This was a record-breaking year in terms of number of conference abstracts and attendance.

      ICHPS is held every two years, jointly sponsored by the ASA and Health Policy Statistics Section (HPSS). Conference co-chairs Laura Lee Johnson (US Food and Drug Administration) and Bonnie Ghosh-Dastidar (RAND Corporation) were supported by a 30-member organizing committee, including two student representatives and past conference chairs.

      The theme—Health <-> Statistical Science <-> Care, Policy, Outcomes—reflected the interactive relationship between health services and outcomes research and innovative statistical methodology to facilitate informed discussions regarding health reform and other efforts to improve health care in the United States.


      From left: Conference co-chair, Laura Lee Johnson, gets together with Arlene Ash, Sally Morton, and Kelly Zou during the International Conference on Health Policy Statistics.

      ICHPS 2018 was supported by grant number R13HS025884 from the Agency for Healthcare Research and Quality and by the Patient-Centered Outcomes Research Institute (PCORI) through a Eugene Washington Engagement Award. Project officers took an active role, challenging attendees to find effective mechanisms for disseminating results so they translate into actual policy and practice.

      Presentations and posters covered a range of topics, including fraud detection methods, causal inference and treatment heterogeneity, real-world evidence, pragmatic clinical trials, and comparative effectiveness. The Alan Alda Center for Communicating Science conducted a workshop using improvisational theater techniques developed to help people speak more vividly and expressively. Wednesday included a career panel and networking lunch for students. Thursday afternoon included town halls and roundtable discussions to allow for idea sharing and informal networking. Town halls were on global real-world data, the VA, engaging with community partners, Medicaid payment reform, and health care delivery system transformation.

      Networking dinners and meet-ups afforded opportunities to mingle in a relaxed environment while enjoying Charleston’s hospitality and delicious food during the local restaurant week.

      Workshops included sequences on causal inference, complex survey analysis, and patient-reported outcomes, plus workshops on social network analysis and an introduction to several data sets from the US government, including the Medical Expenditure Panel Survey, National Health and Nutrition Examination Survey, and Medicare Beneficiary Survey.

      Rousing addresses were delivered by Robert Califf, “Evidence Generation in the Era of Ubiquitous Information,” and Suchi Saria, “A Methodologist’s Quest to Improve Health Care.” Each offered advice on opportunities to redesign the health care delivery system in an evidence-driven way with incentives. Both emphasized high-risk, interdisciplinary problems of real interest requiring scalable interventions.

      Conference proceedings will be published in Health Services and Outcomes Research Methodology.

      HPSS presented its Long-Term Excellence Award to Sally C. Morton (Virginia Tech University) and Paul Rosenbaum (University of Pennsylvania). Anirban Basu (University of Washington) received the Mid-Career Award.

      ICHPS also provided 21 student travel awards, supported by grants, the ASA’s Biopharmaceutical Section, and the ASA’s Mental Health Statistics Section. Conference activities were supported by grants, awards, and multiple industry and institutional partners, including AbbVie, Amplexor, Pfizer, and Research Triangle Institute (RTI).

      ICHPS2020 is planned for January 6–8 in San Diego, California. For more information, contact conference chairs, Kate Crespi and Ofer Harel.

      Rebecca Doerge

      Thu, 03/01/2018 - 6:00am

      Dean, Mellon College of Science, Carnegie Mellon Univeristy

      Educational Background
      Cornell University: Post-Doctoral Scholar (1993–1995)
      North Carolina State University: PhD, Statistics (1993)
      University of Utah: MS, Mathematics (1988)
      University of Utah: BS, Mathematics (1986)

      About Rebecca
      Rebecca Doerge is dean of the Mellon College of Science at Carnegie Mellon University. Prior to joining both the department of statistics and department of biology at Carnegie Mellon , she was the Trent and Judith Anderson Distinguished Professor of Statistics at Purdue University. Doerge joined Purdue University in 1995 and held a joint appointment between the colleges of agriculture (department of agronomy) and science (department of statistics).

      Doerge’s research is focused on statistical bioinformatics, a component of bioinformatics that brings together many scientific disciplines to ask, answer, and disseminate biologically interesting information in the quest to understand the ultimate function of DNA and epigenomic associations.

      Doerge is the recipient of the Teaching for Tomorrow Award, Purdue University, 1996; University Scholar Award, Purdue University, 2001-06; and Provost’s Award for Outstanding Graduate Faculty Mentor, Purdue University, 2010. She is an elected Fellow of the American Statistical Association (2007), an elected Fellow of the American Association for the Advancement of Science (2007), and a Fellow of the Committee on Institutional Cooperation (2009). She has published more than 120 scientific articles and two books, as well as graduate 23 PhD students.

      Doerge was born and raised in upstate New York. As a first-generation student, she studied theoretical mathematics at the University of Utah. It was there she gained interest and experience in both computing and human genetics (Howard Hughes Medical Institute). She earned her PhD in statistics from North Carolina State University under the direction of Bruce Weir and was a postdoctoral fellow with Gary Churchill at Cornell University.

      Doerge is a member of the Board of Trustees for both the National Institute of Statistical Sciences and the Mathematical Biosciences Institute. She is a member of the Engineering External Review Committee at Lawrence Livermore National Laboratory and the Global Open-Source Breeding Informatics Initiative (GOBII) Advisory Board.