Speakers
Dr. Hongtu Zhu, UNC Chapel Hill
Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science and Genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, the ICSA 2025 Distinguished Achievement Award, the IMS 2027 Medallion award and Lecture, and the COPSS 2025 Snedecor Award. He has published more than 360 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, JASA, Biometrika, and JRSSB, as well as presenting 58+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI, and KDD. He is the coordinating editor of JASA and the editor of JASA ACS.
Dr. Junshui Ma, Associate VP and Head of Biometrics Research Department, Merck Research Laboratories
Dr. Junshui Ma serves as an Associate VP and Head of the Biometrics Research Department at Merck Research Laboratories, Merck. He obtained his Ph.D. from Ohio State University in 2001 and joined Merck in 2005. He has navigated the entire spectrum of pharmaceutical R&D, including preclinical discovery, clinical development, regulatory filing and approval, biomarker research, and translational medicine. A key area of his research is integrating AI and Machine Learning into pharmaceutical R&D. Since early 2023, he has spearheaded the development and deployment of generative AI applications in pharmaceutical settings.
Dr. John Schwarz, BMS
John Schwarz is a Director of Data Sciences leading a team supporting Cardiovascular, Immunology and Neuroscience therapeutic areas at Bristol Myers Squibb. The team is responsible for developing predictive models and exploratory analysis in late stage assets. Prior to BMS, John was leading a team of data scientists and biostatisticians performing REMS and post marketing surveillance epidemiologic studies for CNS active pharmaceutical agents with Rocky Mountain Poison and Drug Safety. He received his PhD in biostatistics from the University of North Carolina at Chapel Hill.
Dr. Ryan Shewcraft, BMS
Ryan is an Associate Director on the Data Science team at Bristol Myers Squibb supporting the neuroscience therapeutic area. Prior to this role, he was a data scientist at Sema4 (now GeneDx) building predictive models from real-world data. He earned a PhD in neuroscience and was a post-doctoral fellow at NYU. He completed a bachelor's degree in mathematics-physics and philosophy from Brown University.
Dr. Patrick Long, IQVIA
Patrick Long leads an AI/ML engineering team at IQVIA, building AI solutions using real‑world data for pharmaceutical and biotech clients. His work focuses on patient outcome prediction, disease detection, and subtype discovery across therapeutic areas, including respiratory, metabolic, CNS, autoimmune and rare disease. Patrick holds a PhD in Neuroscience and completed postdoctoral training at Harvard and the University of Michigan. He has 15+ publications in healthcare AI and neuroscience.
Dr. Yuting Xu, Merck
Yuting Xu is a statistician at Merck & Co., where she supports preclinical development programs with innovative methodologies. She received her M.S.E. in Computer Science and Ph.D. in Biostatistics from Johns Hopkins University. Her professional interests include designing quantitative methods and implementing practical solutions to tackle complex challenges across the pharmaceutical R&D landscape.
Dr. Yilong Zhang, Meta
Yilong Zhang, PhD, is the Manager of the Health Quantitative Science team at Meta. He previously worked as a statistician at Merck & Co., Inc. on late-stage clinical trial development. His interests include developing R and Python packages to improve clinical trial analysis, reporting, and regulatory submission. Yilong has published over 25 peer-reviewed articles and holds a PhD in Biostatistics from New York University.
Dr. Albert Man, Servier
Dr. Qi Tang, Sanofi
Qi Tang is Director of AI/ML for R&D at Sanofi, leading data science initiatives in pharmaceutical research and development. With granted AI patents and publications in top-tier AI journals, Qi drives innovation at the intersection of artificial intelligence and drug discovery, advancing computational approaches to accelerate therapeutic development.
Dr. Alex Goh, BeOne Medicines
Dr. Alex Goh, is an Associate Director of Product Management at BeOne Medicines, where he drives the Protocol Digitalization and Automation Initiative and oversees the AI Platform for Global Statistics and Data Science, using advanced analytics to streamline clinical R&D. Backed by 23 peer-reviewed publications (h-index 11), he pairs deep data-science expertise with strategic product leadership to cut manual workloads, accelerate development timelines, and lead cross-functional teams in delivering AI-enabled innovation products.
Dr. Zhaohua Lu, Daiichi Sankyo
Zhaohua Lu is an Associate Director of Biostatistics at Daiichi Sankyo and a Ph.D.-trained statistician with over ten years of experience in statistical modeling, data science, and clinical trial design. Before joining industry, he served for five years as a Faculty Member in the Department of Biostatistics at St. Jude Children’s Research Hospital, where he worked extensively with large-scale neuroimaging, genomics, and natural language datasets. He has authored more than 40 peer-reviewed clinical papers and 30 methodological publications, advancing innovative statistical and AI/ML methods and translating them into practical biomedical and clinical applications.
Arlina Shen, Stanford DS Graduate Program
Arlina Shen is a second-year Master’s student in Biomedical Data Science at Stanford University, where she specializes in statistical methodology for clinical research. Her work centers on designing and analyzing clinical trials with innovative approaches, including adaptive and rank-based methods for composite endpoints in progressive diseases such as ALS. Arlina is particularly interested in improving trial efficiency and fairness by integrating real-world evidence with randomized controlled trials, bridging the gap between traditional study designs and practical clinical applications. Her research aims to advance drug evaluation strategies and contribute to more equitable and effective therapeutic development.
Dr. Qiao Liu, Yale
Dr. Qiao Liu is an Assistant Professor in the Department of Biostatistics at Yale University. His is also the core faculty member of Yale Computational Biology and Biomedical Informatics Program. His research lies at the intersection of statistics, artificial intelligence, and computational biology, where he develops practical statistical and AI-driven tools with both theoretical and applied significance. His work leverages advances in generative AI to address high-dimensional statistical challenges, including Bayesian computation and causal inference, with broad applications in single-cell genomics, multi-omics data integration, pharmacogenomics, and genomic large language models. Dr. Liu has authored over 40 publications in leading international journals and conferences. His contributions have been recognized with prestigious honors, including the NIH Pathway to Independence Award.
Dr. Pingye (Eric) Zhang, Eikon Therapeutics
Eric Zhang is the Director of Biostatistics at Eikon Therapeutics, where he supports oncology programs spanning early to late-stage clinical development. He earned his Ph.D. in Biostatistics from the University of Southern California and began his career at Merck, where he served as the lead statistician for late-stage oncology trials and played a pivotal role in the regulatory filing and approval of pembrolizumab (KEYTRUDA) for RCC and HN cancer. Following his time at Merck, Eric worked at BeiGene and Gilead Sciences. In addition to his project work, Eric is also an active participant in scientific discussions across industry and regulatory bodies, and has published extensively in both statistical and medical journals.
Andrew Semmes, Moderna
Andrew Semmes is the Associate Director of Pharmacovigilance Artificial Intelligence and PV Transformation at Moderna, where he leads AI adoption and digital transformation initiatives within Clinical Safety & Pharmacovigilance (CSPV). His work focuses on leveraging AI to enhance pharmacovigilance processes, automate workflows, and improve operational efficiency while ensuring GxP compliance. He spearheads enterprise-wide initiatives to enable Moderna’s AI infrastructure to scale effectively in highly regulated environments. Prior to joining Moderna, Andrew was a strategy and analytics consultant at Deloitte, where he helped pharmaceutical and biotech companies integrate AI into pharmacovigilance, automate adverse event triage, and optimize regulatory workflows. He led efforts to develop safety and compliance systems, streamline business processes within digital transformations, and drive data strategy initiatives that generated global cost savings. Andrew holds a Master’s in Information Science with a focus on Data Science and a Bachelor’s in Information Science with a concentration in User Experience, both from Cornell University.
Dr. Rebecca Taha, waterworksAI
Rebecca Taha, PhD, MBA, CEO & Founder of waterworksAI, is a strategic leader in the life sciences industry. With a deep understanding of scientific research and business operations, Dr. Taha guides waterworksAI in delivering innovative, technology-based solutions that address critical challenges in drug development. With over 20 years of experience in the industry, she has a proven track record of delivering impactful solutions.
Dr. Taha and her team develop and evaluate innovative GenAI applications in the pharmaceutical industry, focusing on ensuring that AI-powered tools deliver reliable and safe outcomes while improving the efficiency, effectiveness, and cost of drug development. Prior to founding waterworksAI, Dr. Taha served small, medium, and large pharma and biotech organizations in the strategic development and implementation of their clinical development programs. Dr. Taha received her MS and PhD from the University of Kentucky in Statistics and Gerontology, respectively, and her MBA from the Kelley School of Business.
Dr. Will Ma, HopeAI
Will Ma is the founder and CEO of HopeAI, a Mayo Clinic Platform_Accelerate company on a mission to bring hope to patients through AI-accelerated clinical development. HopeAI has developed AI assistants that combine clinical insights with statistical innovations, enabling faster and more precise clinical trials. Prior to founding HopeAI, Will had over 10 years of experience in clinical development, including working as a statistician at Sanofi and BMS, and serving as a faculty member at Moffitt Cancer Center.
Dr. Pranava Goundan, ZS
With over 16 years of experience in life sciences analytics and AI consulting, Pranava leads the R&D AI practice at ZS. In his role, Pranava champions the use of AI and analytics to help life sciences R&D and Medical Affairs organizations transform data into insights and enable better decision making across the entire R&D value chain to drive patient impact. Pranava also advises clients on the rapidly evolving AI landscape and approaches to harnessing emerging AI technologies such Generative AI and foundation models. Pranava holds a Ph.D. in Operations Research from MIT. Prior to that, Pranava graduated with a bachelor’s degree in Computer Science from the Indian Institute of Technology, Madras.
Dr. Dongdong Lin, Sanofi
Dongdong Lin is a Statistical Biomarker Leader at Sanofi, leading precision medicine and biomarker statistical projects in clinical development. His work focuses on implementing advanced statistical and AI/ML methodologies to transform complex biomarker data into actionable insights that accelerate evidence-based drug development. He also integrates diverse data sources—including pre-clinical studies, real-world evidence, and scientific literature—to collaboratively shape biomarker strategies, with specialization in immunology and neurodegenerative disorders.
Dr. Félix Balazard, Sanofi
Félix Balazard, PhD, is a Statistical Project leader in I&I at Sanofi since 2025. From 2018 to 2025, he worked at Owkin, an AI company for medical research, initially as a data scientist and then in charge of projects for the optimization of clinical development. In particular, he worked on covariate adjustment using deep learning histological covariates that resulted in a publication and a letter of support from the EMA. He also led an effort to apply federated learning to perform external control arms when the data cannot be pooled in a central server.