The second Akaike Memorial Lecture Award will be presented to Mike West of Duke University during the plenary session of the Japanese Joint Statistical Meeting 2018, which will take place at the Korakuen Campus of Chuo University on September 10.
The award, jointly created by the Institute of Statistical Mathematics (ISM) and Japan Statistical Society (JSS), aims to encourage the education of talented young researchers by recognizing those who have achieved outstanding accomplishments that contribute to the field of statistical sciences. It celebrates the outstanding achievements of the late Hirotugu Akaike.
West’s contributions to Bayesian statistics include seminal work in dynamic modeling and the implementation of nonparametric models that paved the way for practical data analyses via the first realization of large-scale simulation-based methods. West has also worked at the frontier of various research fields to which Bayesian statistics can be applied and contributed to the creation of data-driven sciences. For example, he established a new approach for biomarker discovery using gene expression data, thus creating a novel trend in -omics biology based on data analysis.
Read more about West’s work and the Akaike award.Noel Cressie
Noel Cressie, distinguished professor at the University of Wollongong, was recently named a Fellow of the Australian Academy of Science. He was inducted into the academy May 22 in Canberra, Australia.
The academy’s citation associated with his induction reads:
“Noel Cressie is a world leader in statistical methodology for analyzing spatial and spatio-temporal data, and its applications to environmental science. His fundamental contributions changed the basic paradigm for analyzing observations in space and space-time. Cressie has also contributed to research on pollution monitoring, climate prediction, ocean health, soil chemistry, and glacier movement and is a NASA science team member for the Orbiting Carbon Observatory-2 mission. Responding to the huge volumes of complex data in environmental research, Cressie has made ground-breaking innovations for ‘big data analytics’ for remote sensing and climate change.”
Daniel Jeske, The American Statistician Executive Editor
The August 2018 issue of The American Statistician features eight articles and two letters to the editor that span a wide range of interesting methodology and application areas. As usual, there is something for everyone in this issue.
The General section begins with an article about the construction of minimum volume confidence sets for the parameters of a shifted exponential distribution (also known as the two-parameter exponential distribution). A second article proposes a new test for detecting general forms of serial temporal dependence.
We have three articles in the Statistical Practice section. First, an article presents a structural equation modeling approach for analyzing a spatial regression model for situations in which there is spatial influence on both the response and covariate variables. The second article in this section presents a semiparametric Bayesian model for analyzing homerun production of Major League Baseball players. The final article details a random graph model for studying citation patterns within the causal inference literature.
We have one article in the Teacher’s Corner that focuses on a variety of thought-provoking insights on the Wilcoxon-Mann-Whitney test. In the Short Technical Note section, you will find a probabilistic proof of an interesting binomial coefficient identity. The Interdisciplinary section is represented by an in-depth analysis of if and how the proportion of gun-related suicides can estimate gun prevalence.
The issue concludes with two letters to the editor that provide comments about a recent article that investigated the use of interpolated nonparametric confidence intervals for population quantiles.
With a PhD in statistical astrophysics, David Corliss works in analytics architecture at Ford Motor Company while continuing astrophysics research on the side. He serves on the steering committee for the Conference on Statistical Practice and is president-elect of the Detroit Chapter. He is the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods to issue-driven advocacy in poverty, education, and social justice.STATtr@k 411
– Includes sections for awards and scholarships, getting in touch with ASA chapters, career support, and resources
– Updated monthly
– Search function, including archives
Membership in the American Statistical Association offers many benefits: chapter membership, education and collaboration opportunities, great magazines, and much more. One of the most important resources for students and people just getting started in a career in statistics is STATtr@k. Describing itself as “a website produced by the American Statistical Association for individuals who are in a statistics program, recently graduated from a statistics program, or who recently entered the job world,” it is much more than a website. Under the care and guidance of ASA staff, STATtr@k provides access to an extensive collection of resources for the early-career statistician.Content and Resources
From mentoring programs to articles with valuable career advice, to information about applying for scholarships and fellowships, to educational opportunities and students in the news, STATtr@k is a hub for early-career development information. You can find out about hackathons and learn from others’ experiences to improve your performance; get the skinny on conferences and what they offer for early-career development; and learn about the work of local chapters, ASA sections, and other organizations such as the National Institute of Statistical Sciences (NISS). With so much information and resources available, STATtr@k’s search function might be the most important part.
One of the most valuable aspects of STATtr@k is the opportunity to gain writing experience and exposure by submitting articles. You can share your story, experiences, opportunities, and useful information for students and early-career statisticians. For example, I had the opportunity to interview Megan Price from the Human Rights Data Analysis Group for the upcoming September issue of Amstat News, which focuses on careers in statistics. Because our talk focused on her career as a human rights statistician, this interview will appear on STATtr@k, as well.
STATtr@k’s support for people early in their career makes it a resource for the entire statistical community, so everyone can participate. It’s a perfect channel for senior statisticians and leaders in the statistical community to share experiences, resources, and opportunities with others just getting started.STATtr@k and Data for Good
Looking to get started in Data for Good projects? STATtr@k is a great place to research opportunities, learn about what others are doing, and connect with the project that meets your interests and develops your skills. It’s also a great place to let people know about your Data for Good projects and recruit newcomers to your important work. Using the search function (it’s in the upper right corner of every page), look for the subjects and opportunities that interest you most.
For example, a search on “social good” will turn up information about scholarships, student fellowships, new undergraduate programs in data science, and an article about how internships helped four students make a difference. A search on “justice” will connect you with volunteer opportunities, the ASA’s Research Experience for Undergraduates (REU) program, best practices for developing an open data portal, and feature articles like “The Local ASA Chapter Is My Justice League”, in which Scott McClintock described his ASA chapter—in Philadelphia—as his “Justice League,” where collaborators use their “statistical super-heroism for the greater good.” The list of resources and possibilities for good work goes on and on. If you don’t find information about a program, it means you can write about it and let STATtr@k staff know so others can benefit from your experience.
The mission of the ASA can be summed up as doing good statistics, doing good for statistics, and doing good with statistics. The wealth of resources STATtr@k offers early-career statisticians, used to support Data for Good projects, meets these objectives at once. My highest hope for Data for Good, in this column and elsewhere, is to see it become normative—a natural, ordinary part of a career in statistics. That starts from the beginning, and so naturally connects with STATtr@k’s mission to provide resources for early-career statisticians.
We all have a role to play in developing the statistics—and the statisticians—of the future. That means we all can be users, even contributors, to STATtr@k’s work to support students, recent graduates, and others just starting out. Help make Data for Good an important part of their statistical career. Visit STATtr@k and find your place in moving statistical science forward as a powerful means for doing good in our society, communities, and world.
More than 500 people attended the sold-out 2018 Symposium on Data Science and Statistics in Reston, Virginia, May 16–19.
The program for this first ASA symposium featured a strong program offering short courses, concurrent sessions, and electronic poster sessions. There also was an exhibit hall and many opportunities for networking. Emery N. Brown gave the keynote address “Uncovering the Mechanisms of General Anesthesia: Where Neuroscience Meets Statistics,” while David Scott, Adalbert Wilhelm, and Jerome Friedman each gave a plenary talk.Keynote and Plenary Speakers
Emery N. Brown is a renowned scholar and member of the National Academy of Medicine, National Academy of Sciences, and National Academy of Engineering. He is an anesthesiologist-statistician whose experimental research has made important contributions to understanding how anesthetics act in the brain. In his statistics research, he has developed signal processing algorithms to study dynamic processes in neuroscience.
David Scott is the Noah Harding Professor of Statistics at Rice University in Houston, Texas. He was a founding member of the department of statistics in 1987 and its chair. Scott’s talk focused on Edward Wegman’s influence on the profession and his work in computational statistics and density estimation.
Adalbert Wilhelm holds a professorship in statistics and is the vice dean of the Bremen International Graduate School of Social Sciences at Jacobs University in Bremen, Germany. His talk focused on statistical graphics in data science. He bridged the different visualization aspects from computer science, statistics, and application domains and discussed recent trends.
Jerome Friedman is a renowned scholar and member of the National Academy of Sciences. He is a professor of statistics at Stanford University and one of the world’s leading researchers in statistics and data mining. Friedman’s talk was titled, “Omnibus Regression: Predicting Probability Distributions with Imperfect Data.”
Within the invited program were sessions on data science, data visualization, machine learning, computational statistics, computing science, and applications—some standing room only. The short courses, which took place May 16, also were full.
One of the most popular invited sessions was “Interactive Statistical Graphics: Where Are We Now?” It featured talks by Wayne Oldford (“Exploratory Visualization via Extendible Interactive Graphics”), Catherine Hurley (“Model Exploration via Conditional Visualization”), and Heike Hofmann (“Interactive Web-Graphics Using R”).
Some other talks garnering packed rooms included the following:
Daniele Struppa: “Social Networks and Simplicial Complexes”
Menas C. Kafatos: “Laws of the Universe, Information, and Mind in the Quantum Universe”
Kirk Borne: “Exploring and Exploiting Interestingness in Data Science”
Leland Wilkinson: “Automatic Visualization”
David Banks: “Cherry-Picking Techniques for Complex Data Sets”
Edward George: “Bayesian Penalty Mixing with the Spike and Slab Lasso”
To read about all the sessions and talks, visit the online program.
The banquet talk, “I Never Met a Datum I Didn’t Like,” was given by Barry D. Nussbaum, the 112th president of the American Statistical Association and chief statistician for the US Environmental Protection Agency. At the banquet, the Interface Foundation of North America and ASA awarded a lifetime achievement award to Edward J. Wegman for his seminal contributions to computational statistics, data visualization, and data science. In 1987, he incorporated the Interface Foundation and has been the treasurer for 31 years.
This symposium is a continuation of the Interface Symposium on Computing Science and Statistics. The first Interface Symposium was held in Reston, Virginia, in 1988.
Judea Pearl, a longtime ASA member, was interviewed in November of 2012 after receiving the Turing Award from the Association of Computing Machinery. He has recently published a book, The Book of Why: The New Science of Cause and Effect (with Dana MacKenzie), that aims to familiarize the general, nontechnical public with recent advances in causal inference. ASA Executive Director Ron Wasserstein interviews him again here to find out what message he thinks his new book sends to Amstat News readers.
I have official and unofficial answers to this question.
The official answers: First, I have found it both timely and exciting to lay before the public the amazing story of a science that has changed the way we understand scientific claims and yet has remained below the radar to the general public. As we enter the era of big data and machine learning, it is important to share with the public our current understanding of how this new science is likely to affect our lives in the 21st century.
Second, as a part-time philosopher, I have found it intriguing to narrate the history of statistics as viewed from the special lens of its orphaned sister: causation. The story of this “forbidden love” was never told before and, believe me, it is full of mystery, intrigue, personalities, dogmatic orthodoxy, and heroic champions of truth and conviction.
Finally, my unofficial reason is to incite a rebellious spirit among rank-and-file statisticians, so the excitement that currently fuels causality research in academia percolates down to education and to practice. In other words, I am impatient with the slow pace at which the tools of causal inference are becoming an organic part of statistical thinking.
You expressed a similar impatience in our interview six years ago. And you have initiated the ASA Causality in Statistical Education Award to close the growing gap between research and education. Hasn’t this initiative met your expectations?
It has. But, with age, my impatience grew stronger and less forgiving. Of course, the availability of instructional material made it easier for instructors to introduce aspects of causal inference in graduate courses, but it was not sufficient to change the curriculum of undergraduate classes. Nor was it sufficient to reshape the minds of practicing statisticians or high-profile academics who are too busy to sort out what all the causal inference “hype” is about.
What The Book of Why is doing can be described as “the democratization of causal inference.” It awakens the untrained students to the realization that “it’s easy and who needs the ‘experts’ and all their quibbles?” As a result, the book is accomplishing what I have failed to achieve in the past 30 years through hard labor and scholarly discussion with the leading statisticians of our time—a mass uprising of common sense.
I have read that some statisticians find your claims to be “hard to swallow,” especially your characterization of causal inference as “The Causal Revolution” and your depiction of statisticians as antagonistic to causal thinking. Can you comment on these sentiments?
These are not only sentiments but natural complaints voiced by practicing statisticians who are genuinely surprised by how the history of statistics is viewed from the causal lens.
Take for instance the mantra “correlation does not imply causation,” which every statistics student has learned to chant, demonstrate, and internalize.
The Book of Why dissects this mantra to far-reaching conclusions that seem indeed “hard to swallow,” even to seasoned statisticians.
First, it can be strengthened to assert that no causal conclusion can ever be obtained without some causal assumptions (or experiments) to support the conclusion. This is hard to swallow because it sounds circular, and because if you look at the statistical literature from 1832 to 1974, you will find many ideas about what is needed to substantiate causal conclusions (e.g., Yule, Fisher, Neyman, Hill, Cox, Cochran), but not one causal assumption—at least not formally.
This raises an interesting question: Why could not these giants of statistics come up with a simple principle, telling us what assumptions are needed for establishing a given conclusion, and let us judge—for any given situation—whether it is plausible to make those assumptions? And here comes the second surprise that is even harder for people to swallow: Even if they knew the needed assumptions, statisticians could not have articulated them mathematically—they simply did not have the language to do so.
Readers refuse to accept this linguistic deficiency until I ask them to write down a mathematical expression for the sentence, “The rooster crow does not cause the sun to rise.” Failing this elementary exercise drives people to realize a totally new notational system is needed; the beautiful and powerful language of probability theory and its many extensions cannot make up for this deficiency.
The needed notation first came into being in 1920, when the geneticist Sewall Wright put down on paper a new mathematical object: a causal diagram. Thus, statistics was separated from causality, not by antagonism or disdain, but by a language barrier—the toughest barrier for humans to acknowledge and to cross. Now that the barrier is behind us, it is only natural we should call the crossing a “Causal Revolution.”
These are interesting theoretical points, but I wonder if they are likely to have significant impacts on the practice of statistics or on statistical education.
The most significant practical impact of the Causal Revolution would probably be a continuous erosion of the supremacy of randomized clinical trials (RCT) in the development and evaluation of drugs, therapeutical procedures, and social and educational policies. Last year, for example, the editors of one of the two leading medical journals in America stated that authors should not talk about causation unless they have conducted a randomized clinical trial.
Miguel Hernan of Harvard and several other specialists in public health vigorously protested this restriction, and Hernan wrote, “The biggest disservice of statistics to science has been to make ‘causal’ into a dirty word, the C-word that researchers have learned to avoid.”
Indeed, considering the practical difficulties of conducting an ideal RCT and its inherent sensitivity to sample selection bias, observational studies have a definite advantage: They interrogate the target populations at their natural habitats, not in artificial environments choreographed by experimental protocols.
The development of a new toolkit that allows scientists to estimate causal effects from observational studies now opens a wide variety of applications—from medicine to social science to ecology—free from problems of ethics, costs, and external validity that plague randomized clinical trials.
True, observational studies are necessarily sensitive to modeling assumptions that must be defended on scientific grounds. However, the transparency with which those conceptual assumptions are displayed, coupled with the ability of testing them against data, now make observational studies serious contenders to RCTs.
I would like to go back to education and ask what you believe would induce a typical statistics instructor to introduce aspects of causal inference in a standard statistics class.
Curious students who read The Book of Why will make it impossible for statistics instructors to skip such aspects.
Take for instance Simpson’s paradox, a phenomenon discussed in every statistics class, usually for the purpose of demonstrating that “correlation is not causation.” The discussion usually ends with a song of praise to statistical tables for showing us that the reversal can indeed occur in the data, hence the paradox does not exist. Done. Some instructors go a bit further and praise the table for protecting us from naïve beliefs in miracle drugs that are good for men, good for women, and bad for the population.
Now imagine an inquisitive student raising his/her hand and asking the very obvious question: So, what do we do if we find Simpson’s reversal in the data? Shall we believe the aggregated data or the disaggregated data? I do not believe any instructor would in good faith be able to evade this question, suspecting the student knows the answer; it takes a few lines to describe. In other words, instructors would not be able to skip the causal implications of Simpson’s paradox, as their professors did to them.
The same applies to Lord’s paradox, spurious correlations, instrumental variables, confounders, and other causal concepts that were used to embarrass statistics instructors in the past.
The graphical approach you advocate in the book is but one of several approaches currently used in causal inference. Would a reader versed in potential outcome analysis feel comfortable with your methodology?
Not only comfortable, but enlightened and liberated. Researchers entrenched in potential outcome analysis will discover, to their amazement, that the following three notorious weaknesses of potential outcomes can easily be overcome:
- Assumptions of “conditional ignorability,” which currently underlie every potential outcome study, can be made not because they facilitate available statistical routines, but when they are truly believed to hold in the world. They are, in fact, vividly displayed in our model of the world (i.e., the causal diagram), where they can be scrutinized for plausibility, completeness, and consistency.
- When assumptions of “conditional ignorability” do not hold, it is not the end of the world; the analysis can continue, and causal questions answered using other types of assumptions the model may license.
- Modeling assumptions need not remain opaque or data-blind; they can be tested for compatibility with the available data, and the model tells us how.
Making these three bullets available to researchers from the potential outcome camp will break through a wall of cultural isolation and enable them to communicate with the rest of the research community in a common, unified language.
To summarize, the democratization of causal inference is bringing about a globalization of common sense and a breakdown of cultural barriers. I am gratified to see The Book of Why contributing to this process.
Starting in 2016, the Consortium for Mathematics and its Applications’ (COMAP) annual Mathematical Contest in Modeling (MCM) added a data insights problem, Problem C. In this new modeling challenge, teams are presented with a modeling problem and data set. While not a big data challenge, data sets often have interesting characteristics and naturally occurring complicating factors such as missing data, cross-discipline sources, correlated observations, and blends of data types.
This year’s Problem C addressed energy usage of four contiguous US states: California, Arizona, New Mexico, and Texas. Teams were charged with seeking mathematical models that could assist with policy changes for forming a realistic new energy compact focused on increased use of cleaner, renewable energy sources. Using a data set representing 50 years of energy use with more than 500 variables, teams first developed and described an energy profile for each state, used this energy profile model to assess which state was best, and then used the model to predict the future energy profile in years 2025 and 2050 in each state. Using these results, teams determined renewable energy usage targets and action items for each state and summarized their results in a one-page memo to the state governors.
More than 4,000 teams participated in Problem C this year: 4,589 from China; 136 from the United States; and several from Canada, Hong Kong, the United Kingdom, Indonesia, India, Macau, Mexico, South Korea, and Taiwan. Six of these teams were designated as Outstanding Winners. Problem author and judge commentaries on team submissions, plus a selection from the Outstanding Winner solution papers, will appear in The UMAP Journal.
New in 2018, the American Statistical Association is designating one outstanding team as the winner of the ASA Data Insights Award. This year’s winning team is from Xi’an Jiaotong University, China, with adviser Fang Zhang. Winners include Running Hu, Shengkuan Yan, and Minghao Zhou.
While the MCM has traditionally been aimed at mathematics students, students with statistical skills have a unique advantage on Problem C due to MCM’s data analysis focus. The MCM is open to both high-school students and college undergraduates.
The 2019 MCM contest is set for January 24–28.
The ASA-BIOP Nonclinical Biostatistics Working Group (NCBWG) endorsed the creation of a new workstream co-chaired by Phillip Yates of Pfizer Inc. and Katja Remlinger of GSK. The goal of the workstream is to provide information, insight, and networking opportunities for students interested in careers in the nonclinical area and offer a forum for young professionals to more actively engage in ASA-BIOP activities.
In addition to supporting the Nonclinical Biostatistics Conference, we are interested in pairing industry veterans with nearby academic partners. Contact Yates or Remlinger or visit the NCBWG website for more information or to volunteer.
The Section on Physical and Engineering Sciences (SPES) Marquardt Memorial Speakers Program facilitates visits of experienced applied statisticians to colleges and universities to give a seminar and meet with students and professors. SPES reimburses the host institution for up to $1,000 to cover the expenses of the speaker’s visit. The speaker provides information to students about the following:
- What an applied statistician does
- How an applied statistician solves problems in science, engineering, technology, and business
- What nontechnical skills are required to be successful as an applied statistician
The Marquardt Industrial Speakers Program was established by SPES in the early 1990s to encourage careers in applied statistics. If you are an institution interested in having a speaker or a SPES member interested in being on the speakers list (or working directly with a local institution to set up a visit), contact Vaneeta Grover.
Members of the ASA Detroit and Ann Arbor chapters recently presented awards for the ASA National Data Visualization Poster Competition and Michigan Statistics Poster Competition to students at their schools.
For several years, Robert Kushler, a Detroit Chapter board member, has coordinated award presentations for students in southeast Michigan with sponsors of the Michigan event currently led by Dan Adrian at Grand Valley State University (GVSU). Offered as an incentive to sponsoring teachers, members of the Detroit Chapter travel to the schools for a ceremony, rather than just mailing the plaques and certificates. They also present the teachers with a statistical book in recognition of their contributions.
This year, the chapter held two award presentations. A team of ninth-grade students from Macomb Academy of Arts and Sciences in Armada, Michigan, received an honorable mention at the national level. Violet Fiddes, a mathematics and computer science teacher there, encouraged her students to enter the competition.
The poster by Samantha Dulz, Jaclyn Golaszewski, and Mickala Winne, titled “What’s Up with Binge Watching?” won an honorable mention award in the Grades 7–9 category of the National Data Visualization Poster Competition. They also won a second-place award at the state-level Michigan Statistical Poster Competition.
Also, in Fiddes’ class, “Printed vs. Ebooks” by Kayla Whitney and Jessica Jarema received a third-place award and “Do You Know Crime” by Cameron Keller, Jacob Brown, and Brennan McClelland was a national qualifier at the Michigan Statistics Poster Competition.
Representing the ASA Detroit Chapter Board for this event were Rob Kushler, Kathy Peterson, Bob Peterson, and Karry Roberts.
During another recognition event at Uriah Lawton Elementary School in Ann Arbor, Anamaria Kazanis, council of chapters representative to the ASA Board; Nicholas Moloci, vice president of the Ann Arbor Chapter; and Kushler and Roberts from the Detroit Chapter presented a first-place Michigan Statistics Poster Competition award to third-grade student Paavani Tewari for “Exploring Public Library Summer Game Data.”
The Arizona Chapter concluded its academic year of activities by supplying judges for the ASA’s Data Visualization Poster Competition for K–12 students. Hosted by Jennifer Broatch at Arizona State University West Campus, the final national round of judging included 134 posters submitted from the regional competitions. The judges came from both ASU faculty and statistics students, as well as local industry—18 altogether, including coordinator and chapter president Rodney Jee.
The event not only served to determine the national winners for this annual competition, but also provided an opportunity for students, professors, and industry professionals to exchange views about graphics and statistical ideas.
Using a recently revised rubric to award the posters on their application of data visualization, the judges often found posters with impressive work (occasionally beyond the AP curriculum) and/or amusing subjects.
Judging for the nationals rotates throughout the ASA’s chapters. It is very much an “event in a box,” since the regional competitions mail the posters to the national judging site and about a dozen chapter members spend a full day judging before communicating the results to the ASA. The ASA provides the awards to the national winners, coordinates the regional sites with the national judging coordinator, and supports a lunch for the judges.
The idea to organize a symposium in honor of Bernard Harris, the first chair of the ASA Section on Risk Analysis, arose a couple of years ago when Susan Harris, Bernard’s widow, presented the section with a gift. The gift indicated it was meant to cover expenses of invited speakers from interdisciplinary areas, like Bernard. What is risk analysis, if not an interdisciplinary area, after all? We imagined an intimate, small conference, where a few invited speakers would have plenty of time to present their research and the audience would have enough time to ask questions and give feedback. It took place May 10–11 in Raleigh, North Carolina.
The speakers, in order of presentation, included the following:
- Michael Pennell of The Ohio State University and chair of the Risk Analysis Section gave the opening remarks.
- Stan Sclove of the University of Illinois at Chicago told us about the life and work of Bernard Harris.
- Edward Melnick from NYU Stern School of Business began the technical part of the symposium by presenting the foundations of risk assessment.
- Richard Smith, from The University of North Carolina at Chapel Hill and associate director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), presented his research on risk of extreme weather events in a changing climate.
- R. Dale Hall from the Society of Actuaries presented segmentation and decomposition techniques in actuarial risk analysis using predictive analytics.
- John Wambaugh of the US Environmental Protection Agency presented informatics tools for chemical safety.
Symposium Organizing Committee Members
Michael Pennell, The Ohio State University
Matthew Wheeler, National Institute of Occupational Safety and Health
Susan Simmons, North Carolina State University
Alexandra Kapatou, American University
Maria Barouti, American University
Qian Li, FDA Center for Tobacco Products
Piaomu Liu, Bentley University
Mary Louie, New York Life Insurance
Edsel Peña, University of South Carolina
Chris Sroka, New Mexico State University
Wensong Wu, Florida International University
The talks were followed with poster presentations by graduate students Taeho Kim and Shiwen Shen from the University of South Carolina and Jun Shepard from Duke University. The first day of the symposium closed with a mixer.
On the second day, Clarice Weinberg from the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, answered the question: “Can we develop a way to find the multi-SNP contributors to disease risk?”
David Banks, from Duke University and director of SAMSI, presented methods of adversarial risk analysis. Ilyan Iliev of the University of Southern Mississippi discussed terrorism and social media. Rita Fuller from New York Life Insurance Company gave a talk for the benefit of students on preparing for a job interview in data science. Finally, Matthew Wheeler, from the National Institute for Occupational Safety and Health and past chair of the Risk Analysis Section, gave closing remarks by presenting inspired ideas that put together different areas of risk analysis.