Building a Stronger Community at JSM 2023

Hao Helen Zhang, 2023 JSM Program Chair

The 2023 Joint Statistical Meetings—held in Toronto, Ontario, August 5–10—attracted more than 6,000 participants from academia, industry, and government and served as a platform for intellectual exchange, business discussion, friendship building, and networking.

This years’ scientific program featured more than 600 diverse sessions, including technical presentations, panel sessions, lectures, posters, and roundtable discussions. They showcased the latest trends and daily practices of statistics and data science, from groundbreaking research and educational strategies to career development and community outreach efforts.

🍁 Plenary Talks and Lectures

JSM 2023 featured four plenary talks and 10 named or award lectures. They spanned a wide range of topics on the foundations of statistics, statistics methods for health and medicine, big and high-dimensional data analysis, causal inference, measurement errors, Bayesian inference, small area estimation, random forests, deep learning, and generative models. There were also presentations and interactive discussions on trustworthy AI, robust and differential privacy, and JEDI [justice, equity, diversity, and inclusion].

Plenary Talks

• ASA President’s Address and Awards, Dionne Price, “Our Mission in Action: Past, Present, and Future”
• ASA President’s Invited Address, Robert Santos, “Serving Through Leadership: My Approach to Heading a Federal Statistical Agency”
• COPSS Awards and Distinguished Achievement Award and Lecture, Bin Yu, “Veridical Data Science Towards Trustworthy AI”
• IMS Presidential Address, Peter Buhlmann, “IMS: What Does It Stand For? What Could It Stand For?”

Named Lectures

• Wald Lecture, Bin Yu, “Seeking Boolean Interactions in Biomedicine and Proofs” (I) and “Sparse Dictionary Learning and Deep Learning in Practice and Theory” (II)
• Deming Lecture, Malay Ghosh, “Small Area Estimation: A Personal Perspective”
• Grace Wahba Lecture, Wing-Hung Wong, “Causal Inference by Encoding Generative Modeling”
• David R. Cox Foundations of Statistics Award Lecture, Nancy Reid, “The Importance of Foundations in Statistical Science”

Medallion Lectures

• Ingrid Van Keilegom, “Copula-Based Cox Proportional Hazards Model for Dependent Censoring”
• Runze Li, “Feature Screening for Ultra-High Dimensional Data: Methods and Applications”
• Yingying Fan, “High-Dimensional Random Forests Estimation and Inference”
• Aurore Delaigle, “Measurement Errors in Diet and Nutrition”
• Blackwell Award Lecture, Ya’acov Ritov, “Minimax vs. (Empirical) Bayes Prediction”
• Florence Nightingale David Award Lecture, Karen Bandeen-Roche, “More Than Freedom from Disease: A Quest to Determine ‘Health’”

🍁 Introductory Overview Lectures

There were five well-attended introductory overview lectures on a range of emerging topics of interest in statistics, including computational social sciences, astronomy statistics, interpretable machine learning, randomized clinical trials with surrogate markers, and genome risk prediction.

On Sunday, Stephanie Eckman of the University of Maryland organized an IOL on computational social science to diverse fields. James Cochran of the University of Alabama, Sali Tagliamonte of the University of Toronto, and Monica Alexander of the University of Toronto presented applications of quantitative social science methods in political science, linguistics, and sociology, respectively.

On Monday, David Hunter and Hyungsuk Tak of The Pennsylvania State University invited experienced astronomers at the interface of astronomy and statistics to introduce and showcase their astronomical science and data-analytic challenges. Joel Leja of The Pennsylvania State University, Jo Bovy of the University of Toronto, Eric Feigelson of The Pennsylvania State University, and Kaisey Mandel of the University of Cambridge discussed statistical challenges in the deep universe, the formation of the Milky Way, detecting exoplanets, and supernova cosmology, respectively.

The third IOL took place Tuesday and featured Cynthia Rudin and Alina Barnett of Duke University, who gave an overview on fundamentals of interpretable machine learning and discussed fundamental principles for interpretable machine learning, recent research in the area, and common misunderstandings that dilute the importance of this topic.

On Wednesday, Lu Tian of Stanford University and Layla Parast of The University of Texas at Austin provided a survey of randomized clinical trials with surrogate markers. They introduced basic concepts of surrogate markers and surrogacy within a rigorous statistical framework, how to evaluate the strength of surrogate markers, and how to use the surrogate markers to assist the conduct of randomized clinical trials.

On Thursday, Indranil Ghosh of the University of North Carolina Wilmington organized an IOL titled “Genomic Risk Prediction: Algorithms, Fairness, and Applications.” Nilanjan Chatterjee of Johns Hopkins University introduced the concepts of genetic risk prediction in the context of large-scale, genome-wide association studies and cutting-edge methods of polygenic-risk scores and applications.

🍁 Late-Breaking Sessions

JSM featured two late-breaking sessions this year. One session, “It Takes More Than a Village: Capacity-Building Efforts for the Statistical Community,” was organized by Tian Zheng of Columbia University and David Banks of Duke University and chaired by Katherine Ensor of Rice University. This session was timely, as there is a great need for the statistics and data science communities to propose mathematical sciences research institutes in response to the National Science Foundation. The panelists highlighted the importance and urgency of a statistics-led institute in the era of big data and provided advice to potential proposers.

The second session, “ChatGPT: Job-Killer, Flash in the Pan, or a Statistician’s Best Friend?,” included a panel of experts in AI and statistics from academia, industry, and government. They provided their perspectives on the implications of large language models such as ChatGPT for the practicing statistician. They also discussed ethical issues and the use of large language models as statistics education tools.

🍁 Memorial Lectures

During every JSM, we memorialize statisticians who made a major contribution to our field and recently passed away. This year, we remembered and celebrated the lives of Jerry Sacks, James J. Filliben, Thomas B. Jabine, and Tze Leung Lai.

The success of JSM 2023 is a testament to the collective efforts and dedication of numerous societies and individuals. Serving as the program chair was an incredibly rewarding experience for me. I enjoyed working with many of you and, together, we are forging a stronger community. I strongly encourage you to become actively involved in future JSMs in any capacity. I look forward to seeing you at JSM 2024 in Portland, Oregon.

MORE FROM JSM

Check out these other JSM-related articles from the October issue.

🍁 Committee of Presidents of Statistical Societies Honors Top Statisticians

🍁 ASA Honors Founders, Inducts 47 Fellows

🍁 JSM Panel Urges Statistical Community to Propose Research Institutes