Past Events

Past Section Webinars

March 9, 2023
Speaker: Dr. Dimitris Rizopoulos

Dynamic Risk Predictions from Joint Models, with Applications in R

Abstract: This workshop focuses on data collected in follow-up studies. Outcomes from these studies typically include longitudinally measured responses (e.g., biomarkers) and the time until an event of interest occurs (e.g., death, dropout). The aim often is to utilize the longitudinal information to predict the risk of the event. An important attribute of these predictions is that they have a time-dynamic nature, i.e., they are updated each time new longitudinal measurements are recorded. In this workshop, we will introduce the framework of joint models for longitudinal and time-to-event data and explain how it can be used to estimate and evaluate such dynamic risk predictions. We will use the R package JMbayes2 to showcase the capabilities of these models.

September 29, 2021
Speaker: Professor Richard Smith

Climate Change, Extremes, and Risks
2021 has been the year that climate change finally became a subject everyone was talking about. A series of extreme climate events have covered the US and Canada, many parts of Europe, and other parts of the world. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) delivers dire warnings about what will happen if we fail to curb greenhouse gas emissions quickly. An international conference will take place in Britain in November where world leaders including President Biden will be expected to reveal their plans for action. So, where do statistics and data science fit into this picture?

It has often been stated that we cannot attribute a single event, such as the recent extreme heat conditions in the Pacific Northwest or the wildfires affecting many parts of the world, to climate change. What we can calculate is how the probability of such an event, or the size of the event given its occurrence, may change as a result of greenhouse gas emissions compared with the atmospheric conditions that existed 200 years ago. First, we need to define the event itself, for example, that the average temperature over a specific region of space and time exceeded a certain threshold level. Second, we can estimate the probability of such an event by studying historical records and comparing them with climate model output, in effect, computer simulations of climate under both present-day and historical conditions. Extreme value theory is the branch of statistics concerned with estimating probabilities of extreme events, and is widely used to characterize probabilities of extreme weather events. However, that theory itself raises many questions about the appropriate choice of distribution, method of estimating parameters, and how to account for uncertainty.

This talk will introduce these concepts to statisticians and data scientists not previously familiar with this field. No prior knowledge of climate science will be assumed, and only a basic graduate-level knowledge of statistics. The talk will introduce extreme value theory, show how these methods are applied in the climate context, discuss some of the pitfalls, and suggest directions for future research.

Richard Smith has been performing research in extreme value theory for several decades, has authored many papers on climatological statistics with research groups including the Statistical and Applied Mathematical Sciences Institute (SAMSI) and the National Center for Atmospheric Research (NCAR), and has interacted with numerous climate scientists in North America and worldwide. He has just been reappointed to the EPA’s Science Advisory Board. About the speaker, please visit or

June 17, 2021
Speaker: Professor David Banks


Title: Statistical Issues in Agent-Based Models for Risk Assessment.
Agent-based models (ABMs) are computational models used to simulate the actions and interactions of agents within a system. Usually, each agent has a relatively simple set of rules for how it responds to its environment and to other agents. These models are used to gain insight into the emergent behavior of complex systems with many agents, in which the emergent behavior depends upon the micro-level behavior of the individuals. ABMs are widely used in many fields, and this talk emphasizes the challenges that arise in the context various risk analyses (e.g., epidemics, invasive species, insurance). Relatively little work has been done on statistical theory for such models, this talk also points out some of those gaps and recent strategies to address them.

About the speaker, please visit

April 22, 2021
Speaker: Professor Nilanjan Chatterjee


Title: Polygenic Risk Prediction and Equitable Disease Prevention. 
Abstract: Recent discoveries from large scale genome-wide association studies (GWAS) have raised the prospect of using polygenic risk scores in routine health care setting for the prediction of future incidence of large variety of complex diseases. However, as GWAS studies to date have been heavily biased towards European origin populations, current polygenic risk scores often underperform in non-European populations and thus use of them can further exacerbate healthcare inequality. In this talk, I will review simple and advanced statistical methods for generating polygenic risk score using high-dimensional SNP data and describe theoretical characterizations of their expected performance, both in the population that underlies original studies and in a different population that is expected to have different distribution of allele frequencies and linkage disequilibrium (SNP-correlation). I will further describe novel Bayesian and machine learning based methods for building polygenic risk scores that can borrow information across GWAS studies of different ethnic groups, and thus makes best use of available data to generate more powerful polygenic risk scores across different ethnic groups. I will demonstrate potential utility for PRS in precision medicine using our recent studies on breast cancer.

About the speaker, please visit


Joint Webinar with the ASA's Transportation Statistics Interest Group

The Section and the Transportation Statistics Interest Group provided a free webinar from 3pm – 5pm on Thursday, Nov 4, 2021. Title of this webinar is Naturalistic Driving Studies with focus on Teenage Drivers: Research Challenges & Opportunities. Speakers of the webinar are: Dr. Johnathon Ehsani (JHU), Dr. Paul Albert (NCI), Dr. Feng Guo (Virginia Tech), and Dr. Subasish Das (TAMU). The webinar was well-attended and interactive. 

Section on Risk Analysis Career Webinar

On September 16, 2021, the Section co-sponsored a career webinar on risk analysis with Quality & Productivity, Physical & Engineering Statistics. The topic of the webinar is Careers in Risk Analysis in the Era of Data Science. Five experts in several areas in risk analysis from both industry and academia provided valuable insight into training and careers in risk analysis during the webinar. The speakers were Dill Grayson, MS, Senior Vice President & Client Quantitative Manager, Bank of America, Aparna Huzurbazar, PhD, R&D Engineer, Use Control Site Coordinator, Los Alamos National Laboratory, Aric LaBarr, PhD, Associate Professor of Analytics, North Carolina State University, William Myers, PhD, Principal Statistician, Data & Modeling Sciences, The Proctor and Gamble Company, and Tao Pang, PhD, CFA, FRM, Professor and Director, Financial Mathematics Program, North Carolina State University. Co-moderators of the webinar were Yue Jiang, Assistant Professor, Duke University and Piaomu Liu, Assistant Professor, Bentley University.

2021 Virtual JSM in August

Section on Risk Analysis organized three sessions and two roundtable discussions during 2021 JSM. Detailed information is below:

1. Invited session: Risk Analysis: New Data, New Approaches, and New Interfaces, 3:30pm - 5:20pm, Wednesday, 08/11/2021. 
2. Topic-contributed session: Recent Methodological Developments and Applications in Statistical and Machine Learning Approaches for Predictive Modeling Using Competing Risk Data, 10:00am - 11:50am, Monday, 08/09/2021. 
3. Contributed - speed session: RISK CSpeed 1 (including the 2021 Section on Risk Analysis Student Paper Award winners), 10:00am - 11:50am, Tuesday, 08/10/2021. 
Round-table Discussions:
1. Topic: Risk Analysis in Biomedical Research and Healthcare (317551). Discussion lead: Dr. Alexander Alekseyenko
2. Topic: Scaling Data Science Teams in Industry (317543). Discussion lead: Dr. Alexander Statnikov.

We celebrated virtually winners of this year's student paper award!
Winner Winner Honorable Mention
Student: Nicholas Hartman Yuming Sun Maria A. Sans-Fuentes
Affiliation: University of Michigan University of Michigan University of Arizona

Joint Webinar with the ASA's Transportation Statistics Interest Group

The Section and the Transportation Statistics Interest Group provided a free webinar from 3pm – 5pm on Thursday, Nov 4, 2021. Title of this webinar is Naturalistic Driving Studies with focus on Teenage Drivers: Research Challenges & Opportunities. Speakers of the webinar are: Dr. Johnathon Ehsani (JHU), Dr. Paul Albert (NCI), Dr. Feng Guo (Virginia Tech), and Dr. Subasish Das (TAMU). The webinar was well-attended and interactive. 

JSM 2018 Vancouver, British Columbia, Canada: July 28-August 2, 2018.

JSM 2018 Vancouver, British Columbia, Canada: July 28-August 2, 2018.

Vancouver Convention Centre

JSM (the Joint Statistical Meetings) is the largest gathering of statisticians and data scientists held in North America. It is held jointly with the:

  • *American Statistical Association
  • *International Biometric Society (ENAR and WNAR)
  • *Institute of Mathematical Statistics
  • *Statistical Society of Canada
  • International Chinese Statistical Association
  • International Indian Statistical Association
  • Korean International Statistical Society
  • International Society for Bayesian Analysis
  • Royal Statistical Society
  • International Statistical Institute

It is also one of the broadest, with topics ranging from statistical applications to methodology and theory to the expanding boundaries of statistics, such as analytics and data science.

JSM also offers a unique opportunity for statisticians in academiaindustry, and government to exchange ideas and explore opportunities for collaboration. Beginning statisticians (including current students) are able to learn from and interact with senior members of the profession.

*Indicates the founding societies of JSM

ASA Section on Risk Analysis
Bernard Harris Memorial Symposium: Risk in the 21st Century

May 10 & 11, 2018
North Carolina State University, Raleigh, NC

The 21st century has seen a revolution in big data analytics. Though there have been methodological advances analyzing these rich data sets, risk assessment science has often lagged behind these advances.

To bring together the top minds within statistics and risk analysis communities and to cross pollinate ideas between practitioners in different areas, the American Statistical Association’s Section on Risk Analysis is having a Symposium at the Department of Analytics at North Carolina State University May 10th and 11th, 2018. At this two day symposium, participants will be challenged by leaders on diverse topics such as toxico/environmental-, economic-, terrorism/defense-, climate-, and genetic disease-risk. These speakers will present on current challenges and stimulate discussion amongst participants. Additionally, focused breakout sessions will be scheduled allowing collaboration with other scientists.

To promote interest in risk analysis, student discounts will be given and a contributed poster session will allow all researchers to present their work while discussing challenges. Though this conference focuses on risk analysis, it should be of interest to any quantitative minded research interested in big data analytics.

Invited Speakers:
Ed Melnick - Professor of Statistics and Deputy Chair of the Department of Information , Operations and Management Science, NYU Stern School of Business.
Richard Smith - Mark L. Reed III Distinguished Professor, UNC – Chapel Hill.
Dale Hall - Managing Director of Research - Society of Actuaries.
John Wambaugh – Project lead for Rapid Exposure and Dosimetry Project, ExpoCast US EPA
Clarice Weinburg – Deputy Branch Chief, Biostatistics & Computational Biology Branch, NIEHS
David Banks -- Professor of the Practice of Statistics, Duke University
Iliyan Iliev – Assistant Professor of Political Science and International affairs, The University of Southern Mississippi

(PDF Version)