Jonathan G. Seidman, PhD, Henrietta B. and Frederick H. Bugher Professor of Cardiovascular Genetics at Harvard Medical School, members of National Academy of Science and the members of the National Academy of Medicine

Dr. Jonathan Seidman is the Henrietta B. and Frederick H. Bugher Professor of Cardiovascular Genetics at Harvard Medical School. He received his undergraduate degree from Harvard University (’72) and his Ph.D. degree from the University of Wisconsin-Madison. His postdoctoral studies were carried out in Dr. Philip Leder’s laboratory at the National Institute of Child Health and Human Development. He has been a member of the Genetics Department, Harvard Medical School since 1981. The Seidman Laboratory, which Jonathan co-runs with his wife Christine Seidman, MD, studies the genetic basis for human disease. The laboratory’s principal focus of research is genetic and non-genetic approaches to define mechanisms leading to human cardiac disease. The current focus of the research is defining the genetic contribution to both adult and pediatric cardiovascular disease using genomic approaches including target-capture DNA sequencing, RNAseq, single cell RNAseq and ChIPseq. To further understand the mechanisms by which gene mutations cause disease, the lab models human mutations in animals and cultured cells. Most recently, they have assessed the effects of sarcomere protein titin mutations on contractile function in induced pluripotent stem cell (iPS) derived cardiomyocytes. Dr. Seidman is a member of The Genetics Society of America and the American Society of Human Genetics. He has received several awards including the 12th Annual Bristol-Myers Squibb Award for Distinguished Achievement in Cardiovascular Research (2002), jointly with Christine Seidman, MD; the Lefoulon-Delalande Foundation Grand Prix for Science (2007), joint recipient with Christine Seidman, MD; the Katz Prize for Cardiovascular Research awarded by Columbia University School of Medicine (2008), jointly with Christine Seidman, MD; the Distinguished Scientist Award from the American Heart Association (2013) and the Sarnoff Cardiovascular Research Foundation Mentorship Award (2014). He is also a member of the National Academy of Science (2007) and the Institutes of Medicine (2007).

Li Wang, Head of Statistical Innovation group, AbbVie

Li Wang, PhD, is currently Senior Director and Head of Statistical Innovation group in AbbVie. Li is leading Design Advisory which provides strategic and quantitative consulting as requested to all Development teams in all Therapeutic Areas to facilitate innovative thinking and complex innovative design evaluation. Li also leads Clinical Trial Innovation capability in AbbVie to drive Machine Learning and Advanced Analytics research and application in Development. Prior to this senior leadership role, he led Immunology and Solid Tumor statistical design and strategy discussions and multiple ML, RWE and Bayesian innovation projects from 2017 to 2019. From 2006 to 2017, he contributed to and subsequently led several NDAs and SNDAs including blockbusters Eliquis, Onglyza and Rinvoq. He is enthusiastic in teaching statistical courses to non-statisticians, and investigating/ promoting novel statistical and machine learning methodologies.

Yuan Ji, Professor of Biostatistics, University of Chicago

Presentation title: Bayesian Approaches for Information Borrowing and Adaptive Decisions

Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author of over 170 publications in peer-reviewed journals including across medical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. His work on cancer genomics has been reported by a large number of media outlets in 2015. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.

Qi Zhang, senior biostatistics team leader for Rare Disease & Rare Blood Disorder within Evidence Generation and Decision Sciences, Sanofi

Presentation title: FDA case study of Xenpozyme: A Clinical Dose Escalation Strategy for a Rare Disease Drug Program

Dr. Qi Zhang is the senior biostatistics team leader for Rare Disease & Rare Blood Disorder within Evidence Generation and Decision Sciences at Sanofi, where she leads and oversee multiple rare disease late phase clinical development programs including the study design and regulatory submission. Prior to joining Sanofi, Qi has worked mainly in neuroscience area (including depression, pain and migraine) at Eli Lilly and company. Her research interest focuses on statistical methods for rare disease clinical trials, including adaptive design, approaches for utilization of external control, etc. Qi holds a PhD degree in biostatistics from University of Cincinnati.

Ziqian Geng, Statistics TA Head for Gastroenterology at AbbVie

Presentation title: Efficient platform design for screening indications in rare disease

Ziqian Geng is Statistics TA Head for Gastroenterology at AbbVie. He received his PhD in Biostatistics from University of Michigan at Ann Arbor in 2014 with 11+ years of late development experience at AbbVie. During the past years, Ziqian focused on late-stage clinical development in various disease areas in immunology with increasing responsibilities. He has led and overseen multiple assets development, regulatory submissions, approvals and launch in AbbVie brands including Humira, Rinvoq and Skyrizi. Ziqian is also passionate about innovative clinical trial designs and novel analysis methods.

Yibo Wang, Statistics Senior Statistician at AbbVie

Presentation title: Efficient platform design for screening indications in rare disease

Yibo Wang is a Senior Statistician at AbbVie in the Statistical Sciences Department. She received her Ph.D. in Biostatistics from University of Michigan in 2023. She has been active in research for using novel analytics and statistical methods in late-phase drug development.

Roland Brown, biostatistician at Biogen

Presentation title: Enhancing clinical trials through prognostic score covariate adjustment – opportunities & challenges in rare disease

Roland is a biostatistician at Biogen, where he works on a variety of nonclinical and clinical applications, including biomarker discovery and development, preclinical animal studies, digital measure development, and prognostic score development.

Lisa Strug, PhD, University of Toronto

Presentation title: Modifier-directed therapeutics in Cystic Fibrosis

Dr. Strug is a Professor in the Departments of Statistical Sciences and Computer Science at the University of Toronto and is a Senior Scientist in the Program in Genetics and Genome Biology at the Hospital for Sick Children. Dr. Strug is the Academic Director of the Data Sciences Institute (DSI), a tri-campus, multi-divisional, multi-institutional, multi-disciplinary hub for data science activity at the University of Toronto and affiliated Research Institutes. The DSI’s goal is to accelerate the impact of data sciences across the disciplines to address pressing societal questions and drive positive social change. Dr. Strug is a statistical geneticist and her research focuses on the development of novel statistical approaches to analyze and integrate multi-omics data to identify contributors to genetic diseases, advancing them from target identification to therapeutic development. She holds the Tier 1 Canada Research Chair in Genome Data Science.

Shuguang Wang, Stat4ward

Presentation title: TBD

Dr. Shuguang Huang spent the first 15 years of his career working at Eli Lilly, Pfizer, and cancer diagnostic company Precision Therapeutics. In 2014, he co-founded the statistics consulting company Stat4ward. Stat4ward works with pharmaceutical and medical device companies, supporting CDx and IVD/LDT product development. He has rich experience in PMA and 510k submissions, and he is actively involved in CLSI guideline development.

Yang Song, PhD, Neurocrine Bioscience

Presentation title: A Single-Arm Confirmatory Trial Design for Cell and Gene Therapy Development

Dr. Yang Song is Vice President of Analytics and Data Sciences at Neurocrine Biosciences in San Diego, where he leads a multidisciplinary department spanning biostatistics, statistical programming, data management, and epidemiology & RWE analytics. From 2016 to 2024, he served as Executive Director and Head of the Biostatistics Group for Pipeline Development at Vertex Pharmaceuticals in Boston. In this role, he oversaw biostatistical support for multiple industry leading drug development programs, including renal diseases, Nav1.8 in pain, and CRISPR gene editing in hematology. During his tenure at Vertex, he co-founded the NERDS workshop series with community leaders in industry and academia, led the organization of the first two workshops, and introduced the “NERDS” acronym that has represented the event since 2019. Earlier in his career, Dr. Song held positions of increasing responsibility at Merck and Johnson & Johnson, with experience across New Jersey, Pennsylvania, and Beijing, supporting global drug development across diverse therapeutic areas. He earned his Ph.D. in Statistics from the University of Wisconsin–Madison.

Avery McIntosh, Director of Biostatistics at Pfizer

Presentation title: Model Informed Drug Development for Advanced Therapeutics

Avery McIntosh, PhD is a Director of Biostatistics at Pfizer, where he works in the Internal Medicine Research Unit. Dr. McIntosh has worked in multiple disease areas, stages of clinical development, and drug modalities. He is a member of the American Statistical Association Cell and Gene Therapy Working Group, and is the Chief Statistical Consultant for Clinical Pharmacology & Therapeutics, the flagship journal of the American Society of Clinical Pharmacology & Therapeutics (ASCPT). Additionally, he is co-author of the invited author textbook Development of Gene Therapies: Strategic, Scientific, Regulatory, and Access Considerations (Taylor & Francis/CRC Press).

Daniel Li, Global Head of Hematology and Cell Therapy Biostatistics at Bristol Myers Squibb (BMS)

Daniel Li is the Global Head of Hematology and Cell Therapy Biostatistics at Bristol Myers Squibb (BMS). In this role, he leads the quantitative strategy and execution for hematology and cell therapy programs. With over two decades of experience in the pharmaceutical industry, Daniel has been at the forefront of cell therapy development since 2014, playing a critical role in securing regulatory approvals for Breyanzi and Abecma.

Jake Gagnon, director of biostatistics at Biogen

Presentation title: Deep Learning Foundation Models for Medical Image Analysis

Dr. Jacob Gagnon is a director of biostatistics at Biogen and leads a team of medical researchers in the areas of neurology and immunology. He leads statistical methodology development efforts for the latest omics technologies (i.e. spatial transcriptomics, scRNAseq, single cell proteomics, etc.), performs preclinical research, is a core member of the text mining center of excellence, and leads a ML/DL focus group. His team’s research interests include deep learning, machine learning, translational biology, omics analysis, computer vision, and text mining. He obtained a PhD in statistics from UMASS Amherst and did postdoctoral work in biostatistics at WPI. After his postdoctoral work, he did biostatistics research for Abbvie, Roche, and then Biogen. He has authored/co-authored around 20 publications including three in Nature journals. Additionally, he has won multiple awards including: a winner of the PHUSE/FDA innovation challenge, NESDI’s best application of theory award, and Wiley’s highly viewed article award.

Tim Smith, Takeda

Presentation title: Artificial Intelligence in Rare Disease Research: Accelerating Relief for the Underserved

Currently at Takeda Pharmaceuticals, Tim built a career on successive moves across disciplines with one unifying theme—drug discovery. After initial training in chemistry, Tim moved into an informatics startup following his postdoc in toxicology. The transition to informatics opened new avenues of research that spanned the pharmaceutical pipeline from target discovery to portfolio and cheminformatics systems. Bitten by the AI bug 10 years ago, Tim published a book on the subject and helped develop a wide variety of AI solutions across the drug discovery pipeline.

Christian Merrill, Technical Director - AI and Computational Sciences at Novartis

Presentation title: Navigating the Scientific Landscape: A Multi-Agent Approach to Research Synthesis

Christian Merrill is currently a Technical Director - AI and Computational Sciences at Novartis, where he leads the development of Agentic AI and Generative AI solutions within the R&D organization. With over a decade of experience spanning biopharma, data science, and machine learning engineering, Christian specializes in applying advanced AI technologies to accelerate drug discovery and clinical development. Previously, as Director of Generative AI at Takeda and in various AI leadership roles at Bristol-Myers Squibb, he pioneered enterprise-scale implementations of large language models for clinical trial documentation, automated literature review, and knowledge management systems. His work has focused on leveraging AI to streamline medical writing workflows, enhance information extraction, and enable more efficient research processes across drug development organizations. Christian holds an MBA in International Business, an MS in Information Technology with a focus on Data Analytics, and a BS in Biochemistry and Molecular Biology. He is an AWS Certified Solutions Architect and has multiple patents pending in AI-driven document processing and knowledge generation. His current research focuses on multi-agent AI systems and their interactions.

Yingyi Liu, PhD, Abbvie

Presentation title: Population-adjusted indirect comparisons in rare disease: methods, challenges and considerations

Dr. Yingyi Liu is currently an Associate Director in Statistical Science at AbbVie. She holds a PhD from the University of Illinois Urbana-Champaign. Her research interests focus on indirect treatment comparison, causal inference and real-world evidence.

Susan Gruber, PhD, TL Revolution

Presentation title: Beyond MMRM: The crucial role of targeted machine learning in RCTs with small sample size and loss to follow-up

Susan Gruber, co-founder of software start-up TL Revolution, is a biostatistician and computer scientist who is working to providing accessible, expert-driven tools for targeted learning. She previously founded Putnam Data Sciences, a statistical consulting firm specializing in causal inference and predictive modeling to support public health and regulatory decision making. In addition to leading and collaborating on FDA-funded projects to improve evaluation of drug safety and effectiveness, Dr. Gruber developed the first open-source R package for TMLE and has an extensive record of publications, presentations, and workshops on Targeted Learning.

Alex Sverdlov, Senior Director, Neuroscience & Ophthalmology Statistical Lead at Novartis

Presentation title: Integrating natural history insights into clinical development planning for gene therapy in a rare neurodegenerative disorder

Alex Sverdlov, PhD is currently a Senior Director, Neuroscience & Ophthalmology Statistical Lead at Novartis. He earned his PhD in Information Technology with 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 co-authored with Dr. Avery McIntosh, Development of Gene Therapies: Strategic, Scientific, Regulatory, and Access Considerations (CRC Press/Chapman & Hall, 2024)

Yusuke Yamaguchi, statistical Scientist at Astellas Pharma Global Development Inc

Presentation title: Propensity score-based unequal matching for rare disease clinical trials with external controls

Yusuke Yamaguchi is a Statistical Scientist at Astellas Pharma Global Development Inc., belonging to Innovative Statistical Science Group and serving as a lead statistician supporting cell & gene therapy programs. He has published over 40 papers on statistical methodology and clinical trials, including longitudinal data analysis and dose-finding trial design