Featured Speakers
Li Wang, AbbVie
Li Wang, PhD, is Senior Director and Head of the Statistical Innovation group at AbbVie. He co-leads the ASA Biopharma Section Scientific Working Group on "Statistical Perspectives on AI/ML in Pharmaceutical Product Development." At AbbVie, Li leads strategic and quantitative innovative design evaluations across development teams in all therapeutic areas, and he also leads the Clinical Trial Innovation capability to advance machine learning and advanced analytics research and applications in development.
Tianxi Cai, Harvard University
Tianxi Cai is the John Rock Professor of Population and Translational Data Science at the Harvard T.H. Chan School of Public Health and a Professor of Biomedical Informatics at Harvard Medical School, where she is the founding Director of the Translational Data Science Center for a Learning Health System (CELEHS). She is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a Co-Editor of the Journal of the American Statistical Association. Her research focuses on transforming large, multi-institutional electronic health record data into trustworthy, clinically actionable evidence by developing statistical and AI methods that generalize across healthcare systems, integrate clinical and genomic data, and support precision medicine. Her group also develops and disseminates open-source tools that standardize EHR data, automate information extraction and outcome curation from clinical notes using natural language processing and large language models, and enable reproducible predictive modeling workflows for cross-health-system deployment.
Ping Gao, Innovatiostat
Dr. Ping Gao received his PhD in mathematics from the University of Washington, followed by a postdoctoral fellowship in biostatistics at the University of Rochester. He has 30 years of experience across the FDA, pharmaceutical industry, and contract research organizations. His therapeutic-area experience includes cardiology, oncology, rare diseases, and neurology. His statistical research interests include non-inferiority, adaptive designs, Phase 1 and Phase 2 oncology trial designs, dynamic external-control borrowing, and dynamic Bayesian design.
Pengling Sun, Pfizer
Pengling Sun is currently a Director of Biostatistics at Pfizer. She earned her PhD in 2011 from Rutgers University and has worked across early- and late-stage clinical development at Pfizer since 2016. She has worked across neuroscience, metabolic diseases, and, most recently, rare diseases with a focus on hemophilia.
Dr. Sun has extensive experience in clinical trial design, Bayesian methods, evidence synthesis, and regulatory interactions. Her recent work has focused on advancing innovative statistical approaches for pediatric drug development, including the use of Bayesian borrowing and extrapolation strategies to efficiently leverage adult data while maintaining rigorous evidence standards.
Tristan Massie, Insmed
Tristan Massie has an MS in Statistics from the University of Virginia, a PhD in Biostatistics from Virginia Commonwealth University, and more than 20 years of experience at the FDA supporting primarily neurology drug development, plus a year of experience supporting gene therapy development as a Director of Biostatistics at Insmed.
Christian Stock, Boehringer Ingelheim
Christian Stock leads the Capability Cluster “Decision Sciences and Methodology” within Global Biostatistics and Data Sciences at Boehringer Ingelheim. One focus of his work is the application of Bayesian statistics to rare disease and pediatric drug development. Before joining Boehringer Ingelheim in 2018, he worked at the German Cancer Research Center and the Institute of Medical Biometry in Heidelberg, Germany. He holds an MSc in Biostatistics, a doctorate in epidemiology, and a habilitation in epidemiology and biostatistics from Heidelberg, as well as an MSc in Health Sciences from the University of York, UK.
Annie Wang, Astellas
Annie Wang, PhD, is a Senior Director of Biostatistics at Astellas Pharma Global Development Inc., where she leads statistical support for early oncology, ophthalmology, and cell and gene therapy programs. With nearly two decades of pharmaceutical industry experience spanning multiple therapeutic areas and all stages of drug development, her research expertise and interests include adaptive design, Bayesian approaches, QDM, and dose-response modeling.
Ahrim Youn, Sanofi
Ahrim Youn is a Senior Statistical Project Leader at Sanofi, where she provides statistical leadership for the global fitusiran development program. Since joining Sanofi in 2021, she has supported statistical methodology development and novel clinical trial designs, including Phase 2/3 seamless trials, predictive modeling of efficacy and adverse events, dose-response modeling, causal inference applications, and machine learning approaches across multiple therapeutic areas.
Before joining Sanofi, Ahrim held roles at Bristol Myers Squibb/Celgene, The Jackson Laboratory for Genomic Medicine, Columbia University, and the National Cancer Institute. Her expertise spans oncology and hematology clinical trials, biomarker discovery, statistical genomics, cancer driver gene identification, and integrative pan-cancer analyses. She earned her PhD in Statistics from the University of Washington and has authored numerous publications in clinical trial methodology, causal inference, predictive modeling, statistical genomics, and computational biology.
Yanwei Zhang, Takeda
Yanwei Zhang, PhD, is a Senior Director in Biostatistics at Takeda Pharmaceuticals, with more than 18 years of experience in clinical development across rare diseases, gastroenterology, oncology, and other therapeutic areas. His expertise encompasses adaptive and Bayesian clinical trial designs, modeling and simulation, real-world data, and the application of AI/ML in drug development. Dr. Zhang applies innovative methodologies to optimize clinical trial designs, inform regulatory strategies, and support data-driven decision-making under uncertainty. He earned his PhD in Statistics from Michigan State University.
Ning Leng, AbbVie
Ning Leng recently joined AbbVie's Data & AI Acceleration group, where she leads end-to-end in-silico simulation efforts that optimize clinical trial decisions and advancement. Prior to AbbVie, Ning served as the Global Head of the Data Science Acceleration group at Roche/Genentech, where she led large-scale modernization efforts across clinical reporting, spanning end-to-end R-based regulatory filing, Shiny-enabled trial analysis dashboards, and GenAI productivity tools. Earlier, Ning was a statistician supporting early-phase oncology development and biomarker discovery.
Ning is also a cross-industry advocate for clinical data science modernization. She co-founded and co-led the R Consortium Submissions Working Group, which has partnered with the FDA across five pilots to demonstrate the feasibility of modern technologies for regulatory review and submission, including R, Shiny, containers, and WebAssembly. Ning earned her PhD in Statistics from the University of Wisconsin-Madison and her bachelor's degree in Information and Computing Science from the Beijing Institute of Technology.
Jin (Veronica) Liu, Takeda
Jin (Veronica) Liu is a statistician in Statistical and
Quantitative Sciences at Takeda. She supports clinical
development across therapeutic areas while conducting research
in quantitative methodology. Her research spans advanced
longitudinal statistical methods, with a focus on nonlinear
longitudinal modeling, as well as emerging areas including large
language model (LLM) evaluation, health AI, and AI agents for
clinical development. Her current work focuses on developing
reliable AI systems through uncertainty quantification, task
decomposition, multi-view evidence integration, and rigorous
evaluation methodologies. She is the sole author of the R
package nlpsem and has published first-author
research in quantitative methodology and large language model
evaluation.
Ran Duan or colleague, Vertex
TBD
Howard Mackey, Roche/Genentech
Howard Mackey is the Biostatistics Disease Area Lead for Neuromuscular, Neurodevelopmental, and Stroke at Roche/Genentech. He holds a PhD in Statistics from UC Santa Barbara and was a postdoctoral fellow in the Johns Hopkins Department of Biostatistics. Previously a biostatistician at Centocor and Genentech Inc., he temporarily left biostatistics to become the Global Development Lead for Erivedge®, the first approved treatment for metastatic basal cell carcinoma, before returning to clinical biostatistics.
Kaushik Patra, Ultragenyx
TBD
Award Winner, TBD
TBD
Kelley Kidwell, University of Michigan
Kelley M. Kidwell, Professor of Biostatistics and Senior Associate Dean of Faculty Affairs at the University of Michigan School of Public Health, is interested in the design and analysis of clinical trials. Her methodological work, funded by the FDA and PCORI, centers on better matching the way in which we practice medicine and public health, with critical decisions over time tailored to individuals, to the way in which we experimentally study it. Dr. Kidwell's methods work has primarily focused on the design and analysis of sequential, multiple assignment, randomized trials (SMARTs), in standard or large trials for treating common diseases and disorders, and in small samples for treating rare diseases.
C. Lee Cohen, FDA
C. Lee Cohen, MD, MBA, is a Medical Officer in the FDA's Center for Drug Evaluation and Research, Division of Rare Disease and Medical Genetics. Prior to joining DRDMG in 2025, she was a Senior Science Advisor in the Immediate Office of the Commissioner. She is also a practicing pulmonary and critical care physician at Brigham and Women's Hospital.
Bryan McComb, Pfizer
Bryan McComb is an Associate Director in the Non-Malignant Hematology group within Pfizer's Inflammation, Immunology & Specialty Care (I&I&SC) organization. He earned his PhD from New York University and has worked at Pfizer since 2021, providing statistical leadership for clinical development programs in hemophilia and other rare bleeding disorders. As Co-Lead of the ASA-DahShu Innovative Design Scientific Working Group (IDSWG), he collaborates with colleagues across industry, academia, and regulatory agencies to advance innovative clinical trial methodologies. He also serves as an instructor at the University of California, San Diego, where he teaches biostatistics and clinical trial design to students pursuing careers in public health and clinical research.
Dr. McComb's interests include innovative trial designs, Bayesian methods, master protocols, and artificial intelligence applications in drug development. His recent work has focused on advancing practical applications of innovative statistical methods to improve evidence generation, study efficiency, and decision-making in rare diseases and other settings involving small patient populations.
Alex Sverdlov, Novartis
Alex Sverdlov, PhD, is currently a Senior Director, Neuroscience and Ophthalmology Statistical Lead at Novartis. He earned his PhD in Information Technology with a concentration in Statistical Science from George Mason University. He has been actively involved in methodological research and applications of innovative statistical approaches in drug development. He has co-authored over sixty refereed articles and edited four monographs, the latest one, Development of Gene Therapies: Strategic, Scientific, Regulatory, and Access Considerations (CRC Press/Chapman & Hall, 2024).
Silvia Colicino, BMS
Silvia Colicino is an Associate Director in Hematology and Cell Therapy Biostatistics at Bristol-Myers Squibb (BMS) in Basel, Switzerland. She contributes to the design of efficient Phase I, II, and III clinical trials and leads statistical activities for lymphoma and cell therapy studies. She also supports health authority interactions, reviews key study documents, and mentors junior statisticians. Before joining BMS, Silvia worked at Novartis Pharma, where she led statistical strategy for oncology clinical trials and supported regulatory submissions. Earlier in her career, she worked as a Data Scientist at Astellas Pharma using real-world data to support drug development.
Silvia holds a PhD in Biostatistics and Epidemiology from Imperial College London, where her research used Bayesian machine learning to study respiratory disease. She also holds a master's degree in Biostatistics and Experimental Statistics, with highest honors, from the University of Milano-Bicocca, Italy. Silvia has also worked as a Research Assistant at Harvard School of Public Health in Boston, where she carried out statistical analysis in computational epigenomics.
Yingdong Feng, Eli Lilly and Company
Yingdong Feng, PhD, is a Senior Advisor in Statistics in Neuroscience at Eli Lilly and Company, where he serves as the lead statistician for the company's amyotrophic lateral sclerosis (ALS) research, working both internally and across the external ALS community to advance trial design for rare neurodegenerative diseases. He received his PhD in Biostatistics from the University at Buffalo in 2019.