Keynote Sessions

Haoda Fu, Head of Exploratory Biostatistics, Amgen

Title: Leading Science and Clinical Research-Biostatistics in the Age of AI

Abstract: AI is rapidly transforming society and has emerged as the defining technology of our generation. Its influence spans across industries, with significant implications for scientific research, healthcare, and drug development. In this work, we review the historical progression of AI and its growing role in pharmaceutical research, highlighting how AI-driven methodologies are revolutionizing drug discovery and development processes. As the integration of AI in biostatistics and clinical research deepens, scientists must cultivate essential skills to remain effective in this evolving landscape. We discuss four critical competencies for scientists working in the AI era: AI Mindsets, AI Communication, AI Integration, and AI-Enabled Innovation. Looking ahead, we explore the potential future impact of AI on the pharmaceutical data analytics ecosystem. The convergence of AI with biostatistics presents both challenges and opportunities, requiring a thoughtful balance between leveraging AI’s capabilities and maintaining rigorous scientific and ethical standards. By embracing AI-driven approaches while upholding core statistical principles, the next generation of scientists can contribute to more efficient, data-driven advancements in clinical research. This discussion aims to provide insights into the evolving role of AI in biostatistics and inspire forward-thinking strategies for navigating the intersection of AI and scientific discovery in the pharmaceutical industry.

Haoda Fu Dr. Haoda 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.

Hansheng Wang, Professor of Business Statistics and Econometrics, Guanghua School of Management, Peking University

Title: Speech Emotion and AI-Driven Intelligent Marketing

Abstract: This report focuses on speech emotion recognition technology and its applications in AI-driven intelligent marketing, examining three practical scenarios in the automotive industry: live streaming and short-video marketing, telemarketing, and AI tele-robot marketing, to explore its technical framework, implementation, and business value. For live streaming and short-video marketing, a CNN-based speech emotion recognition model is built using a dataset of 9,303 audio clips with MFCC features to quantify hosts’ positive emotions, a key factor in conversion. For telemarketing customer conversion prediction, an emotion-enhanced dual-attention model fusing speech Mel spectrograms and textual dialogue data is proposed, achieving an AUC of 0.921 and significantly improving efficiency while reducing costs. The report also establishes a state-space model-based AI tele-robot framework integrating ASR, TTS, and large language models, with continuously enhanced conversion performance. Finally, a theoretical framework for intelligent speech marketing centered on cost, trust, and benefit is proposed, highlighting the technology’s extensibility to education, healthcare, and public sectors and providing a reference for the integration of speech technology and digital marketing.

Dr. Hannsheng Wang is Professor and PhD Supervisor, Department of Business Statistics and Econometrics, Guanghua School of Management, Peking University. He is a recipient of the National Science Fund for Distinguished Young Scholars, a Changjiang Distinguished Professor appointed by the Ministry of Education, and the Founding President of the Young Statisticians Association of the Chinese Industrial Statistics Teaching and Research Association. He is an IMS Fellow, ASA Fellow, and Elected Member of the ISI. He has served as Associate Editor or Editor for 10 international academic journals. He has published over 200 papers in professional journals worldwide, co-authored one English monograph and five Chinese textbooks. He has been selected as an Elsevier Highly Cited Chinese Researcher in Mathematics (2014–2019), Applied Economics (2020), and Statistics (2021–2025).

Tian Zheng, Professor of Statistics, Columbia University

Title: Statistics Beyond Models: Data, Decisions, and Workflows

Abstract: With the rise of data science and AI, statistical reasoning increasingly operates within workflows that link data processing, analysis, and decision-making, often extending beyond the boundaries of individual statistical models. The growing use of generative AI tools in these workflows can distance data-driven practice from statistical rigor when used without appropriate guardrails, but it also creates opportunities for statisticians to play a more central role in designing, evaluating, and overseeing end-to-end data workflows as technical barriers to entry are lowered. In this talk, I draw on collaborations in climate science, ecology, and public health research to show how key statistical challenges arise across workflows, including the “first mile” of data processing and the “last mile” of translating results into insights. I conclude by discussing implications for statistical research training, arguing that working across workflows, experimenting with and evaluating automated analyses, and understanding how uncertainty propagates to decisions are becoming central to statistical education.

Dr. Tian Zheng Professor of Statistics at Columbia University. She obtained her Ph.D. from Columbia in 2002. In her research, she develops novel methods for exploring and understanding patterns in complex data from different application domains such as biology, psychology, climatology, and etc. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling, and social network analysis, collaborating with ecologists and earth scientists. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA, and a Google research award. She became a Fellow of the American Statistical Association in 2014. Professor Zheng is passionate about education and mentoring. From 2015-2016, she was one of the series creators for Columbia’s edX Massive Online Open Course (MOOC) series on data science. From 2017-2020, she was associate director for education of Columbia Data Science Institute. She led a number of education programs, including the MS in Data Science program at Columbia, data science capstone projects with data ethics components, DSI Scholars program that connects students with academic research projects in data science, the Collaboratory program for interdisciplinary data science curriculum development, a number of popular Data Science boot camps. She created DSI’s working group on Data Science Education and has been coordinating data science education efforts across Columbia. Professor Zheng is the receipt of the 2017 Columbia’s Presidential Award for Outstanding Teaching. In 2021, she was recognized by a Lenfest Distinguished Columbia Faculty Award that recognizes the excellence of faculty as teachers and mentors of both undergraduate and graduate students.