ASA Connect

Dear Colleagues, 

This is a reminder that SLDS is hosting its webinar on this Wednesday (Aug 27).  We are fortunate to have Dr. Haoda Fu from Amgen as the speaker.  Dr. Fu has extensive experience both as a researcher and a practitioner.  He will discuss several pressing open problems in the era of artificial intelligence.  Topics will include human–computer interaction, data privacy, deep reinforcement learning, and related areas.  Hope to see you there!  

Title:                        Open Research Problems for Statistical Learning and Data Science

Speakers:               Dr. Haoda Fu, Head of Exploratory Biostatistics, Amgen

Date and Time:      August 27, 2025, 1:00 to 2:30 pm Eastern Time

Registration Link:  ASA SLDS Webinar Registration Link [eventbrite.com] 

Abstract:                In the era of artificial intelligence, statistical principles and theoretical research remain central to advancing data analytics. The emergence of new data types and the increasing scale of analysis have introduced numerous challenging research questions and novel areas of exploration. In this talk, we will examine several pressing open problems with the potential to significantly influence statistical learning and data science. Topics will include human–computer interaction, data privacy, deep reinforcement learning, and related areas.  We contend that addressing these challenges could yield valuable insights and drive further progress in AI and machine learning.

Presenter:              Dr. Haoda Fu is Head of Exploratory Biostatistics in Amgen, before that he was an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelligence, from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana University School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics and data science methodology research. He has more than 100 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS-B, Biometrika, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and also served as an associate editor for JASA theory and method from 2023, and JASA application and case study from 2025-2027.