Skip Olson is currently the Global Head of RWD Strategy and Innovation at Novartis. As such, he is responsible for promoting the very best in research methodology and applications of Real World Data across all therapeutic areas and around the globe to drive better decision making. He comes from a background in HE&OR where he has led the use of RWE to transform the generation of patient insights and value for money assessments. He earned a PhD in Biostatistics from Harvard University and has worked in the pharmaceutical industry for over 25 years.

Jenny Fang, MD, MS, is a Senior Real-World Evidence (RWE) Scientist at Novartis Pharmaceuticals Corporation. During her 20+ year professional career, she spent over 3 years as a practice physician with the specialty in neuroscience and 17 years in pharmaceutical industry. Jenny held various industry positions with expanded and increased responsibilities in clinical statistics, medical affairs and real-world evidence. Her experiences cross drug development to post-approval and lifecycle management. In her current role at Novartis, she has been supporting evidence generation leveraging real-world data. Jenny holds a degree in Medicine from Xian JiaoTong University, China and completed her studies in biostatistics from University of Illinois at Chicago and Health Economics and Outcomes Research (HEOR) from University of Washington. She has over 20 peer-reviewed publications.

Dr. Sue-Jane Wang is Associate Director for Adaptive Design and Pharmacogenomics and the Biostatistics Leader for the Biomarker Qualification Program from Office of Biostatistics, Office of Translational Sciences in Center for Drug Evaluation and Research, U.S. Food and Drug Administration. Other than her current role representing the Office providing services to all 18 medical divisions in CDER/FDA on adaptive designed clinical trials, biomarker associated pharmacogenomics clinical trials and biomarker qualification, Dr. Wang has published over 80 peer-reviewed collaborative research papers in clinical trials, medical genetics, bioinformatics and pharmacogenomics journals and has given more than 200 invited presentations domestic and internationally.

Dr. Yinglin Xia is a Research Associate Professor at the Department of Medicine, the University of Illinois at Chicago, USA. He was a Research Assistant Professor in the Department of Biostatistics and Computational Biology at the University of Rochester, Rochester, NY and was a clinical statistician at Abbvie Inc, North Chicago, IL. Dr. Xia has worked on a variety of research projects and clinical trials in microbiome, gastroenterology, oncology, immunology, psychiatry, sleep, neuroscience, HIV, mental health, public health, social and behavioral sciences, as well as nursing caregiver. He has published more than 100 papers in peer-reviewed journals on Statistical Methodology, Clinical Trial, Medical Statistics, Biomedical Sciences, and Social and Behavioral sciences. He serves the editorial board for several scientific journals. He has successfully applied his statistical knowledge, modeling and programming skills to study designs and data analysis in biomedical research, clinical trials, and in microbiome research.

JDr. Din Chen is now the Wallace H. Kuralt distinguished professor in Biostatistics at University of North Carolina-Chapel Hill. Before this, Dr. Chen was a professor in biostatistics at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair and professor in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of American Statistical Association (ASA), an elected member of the International Statistics Institute (ISI) and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Dr. Chen has more than 200 referred professional publications and co-authored/co-edited 30 books on biostatistics clinical trials, biopharmaceutical statistics, interval-censored survival data analysis, meta-analysis, public health statistics, statistical causal inferences; statistical methods in big-data sciences and Monte-Carlo simulation based statistical modeling.

Diane Uschner, PhD., is an Assistant Research Professor in the Department of Biostatistics and Bioinformatics at the George Washington University School of Public Health. She joined the GW Biostatistics Center in April 2018 and currently serves as Co-Investigator of the Coordinating Center for the TODAY2 long-term follow-up study of the Treatment Options for T2D in Adolescents and Youth (TODAY) study. She served as a statistician for the C-Peptide Ancillary Study of the DCCT/EDIC (Diabetes Control and Complications Trial/Epidemiology of Interventions and Complications) study. Dr. Uschner has recently developed an interest in antibacterial resistance and infectious diseases, and is serving as a biostatistician in the Antibiotic Resistance Leadership Group (ARLG). She is the PI of the Data Coordinating Center of the North Carolina COVID-19 Community Research Partnership.

Nicole (Xiaoyun) Li received her BS in Probability and Statistics from Peking University in 2006 and her PhD degree in Statistics from Florida State University in 2010, after which she joined Merck and have been working as a statistician on oncology clinical trials.

She was the lead statistician for Keytruda’s first regulatory submission and approval for advanced melanoma in the United States, Europe, and the rest of the world, and a key contributor to Keytruda’s Prix Galien USA 2015 Award for Best Biotechnology Product.

Chengxing (Cindy) Lu (PhD in Biostatistics from Emory University) is a director of Biostatistics in Biogen Inc, Cambridge, MA. She has been leading statistical aspects of multiple compounds in various disease areas, from early to late phases of clinical development to post-marking activities, resulting several successful regulatory approvals. Dr. Lu is currently the co-lead of master protocol design sub-team of ASA Biopharmaceutical Section Oncology Scientific Working Group. Her research interest is study designs in clinical trials, real-world evidence and designs, and oncology/rare disease drug development strategy.

Brad Carlin is a statistical researcher, methodologist, consultant, and instructor.He spent 27 years on the faculty of the Division of Biostatistics at the University of Minnesota School of Public Health, serving as division head for 7 of those years.He has also held visiting positions at Carnegie Mellon University, Medical Research Council Biostatistics Unit, Cambridge University (UK), Medtronic Corporation, HealthPartners Research Foundation, the M.D Anderson Cancer Center, and AbbVie Pharmaceuticals.He has published more than 185 papers in refereed books and journals, and has co-authored three popular textbooks:

Ji Yuan (PhD in Statistics from University of Wisconsin). Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. He is an NIH-funded PI focusing on innovative computational and statistical methods for translational cancer research. Dr. Ji is author of over 140 publications in peer-reviewed journals, conference papers, book chapters, and abstracts, including Nature, Nature Methods, JCO, JNCI, JASA, and Biometrics, 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, including trials published on Lancet Oncology, JAMA oncology and JCO. . He is also a co-founder of Laiya Consulting, Inc., focusing on innovative and adaptive designs for clinical trials in new drug development, including the development of novel early-phase statistical platform allowing seamless and efficient clinical trials with master protocols. He is an elected fellow of the American Statistical Association.

Dr. Mark Chang is Sr. Vice President, Strategic Statistical Consulting at Veristat. Before joining Veristat, Chang was Vice President of Biometrics at AMAG Pharmaceuticals. Chang is a fellow of the American Statistical Association and an adjunct professor of Biostatistics at Boston University. He is a co-founder of the International Society for Biopharmaceutical Statistics, co- chair of the Biotechnology Industry Organization (BIO) Adaptive Design Working Group, and a member of the Multiregional Clinical Trial (MRCT) Expert Group. Chang has served associate editor for Journal of Pharmaceutical Statistics. He has been invited to serve as a co-chair on the scientific advisory board and organization committees for several national and international professional/academic conferences on statistics and clinical trial designs.

Walt Stroup is Emeritus Professor of Statistics at the University of Nebraska-Lincoln. He served on the University of Nebraska faculty from 1979 until 2020. His responsibilities included teaching statistical modeling, design of experiments, and research specializing in mixed models and their applications in agriculture, natural resources, medical and pharmaceutical sciences, education, and the behavioral sciences. He is the founding chair of Nebraska’s Department of Statistics, and served as chair from 2001 until 2010. In 2020, he received the University of Nebraska’s Outstanding Teaching and Innovative Curriculum Award, the university’s highest teaching honor. He was a member of PQRI’s Stability Shelf-Life Working Group from its inception in 2006 until its disbanding in 2019. He received PQRI’s Excellence in Research award in 2009. He is a Fellow of the American Statistical Association.

Ram C. Tiwari, Ph.D. is the Director for Division of Biostatistics, CDRH, effective June 27, 2016. He joined FDA in April 2008 as Associate Director for Statistical Science and Policy in the Immediate Office, Office of Biostatistics, Office of Translational Sciences, CDER. Prior to joining FDA, he served as Program Director and Mathematical Statistician in the Division of Cancer Control and Population Sciences at National Cancer Institute, NIH; and as Professor and Chair, Department of Mathematics, University of North Carolina at Charlotte. Dr. Tiwari received his MS and PhD degrees from Florida State University in Mathematical Statistics.

Dionne Price is the Director of Division of Biometrics IV in the Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration (FDA). In this role, Dr. Price provides leadership to statisticians involved in the development and application of methodology used in the regulation of drug products in therapeutic areas including anti-infectives, anti-virals, ophthalmology, rare diseases, and urology, obstetrics, and gynecology. Dr. Price is a member of the Senior Leadership Team and Statistical Policy Council within the Office of Biostatistics. Throughout her career, she has been actively involved in the FDA response to various public health challenges including serving on the Antibacterial Drug Development Task.

Dr Bo Huang graduated from Nankai University as the top student in his class with a bachelor’s degree in Mathematical Statistics. He joined Pfizer in 2008 after receiving his PhD in Statistics from the University of Wisconsin-Madison. He is currently Senior Director, Head of Immuno-Oncology Statistics at Pfizer. Dr. Huang has extensive experience in the pharmaceutical industry across all stages of global clinical development of medical products, with extensive global regulatory and submission related experience. He significantly contributed to all aspects related to major filings to Health Authorities and approval, including the development of strategy, planning, and execution leading up to the global submissions as well as rapid responses to global queries. He is the recipient of the Craig Saxton Clinical Excellence Award at Pfizer in 2019.

Dr. Cong Chen is Executive Director of Early Oncology Development Statistics at Merck & Co., Inc. He joined Merck in 1999 after graduating from Iowa State University with a Ph.D. in Statistics. He also holds a MS degree in Mathematics from Indiana University at Bloomington and a BS degree in Probability and Statistics from Peking University, PR China. As head of the group, he oversees the statistical support of oncology early clinical development and translational biomarker research at Merck. Prior to taking the role in March 2016, he led the statistical support for the development of pembrolizumab (KEYTRUDA), a paradigm changing anti-PD-1 immunotherapy, and played a pivotal role in accelerating its regulatory approvals.

Kaspar Rufibach is an Expert Statistical Scientist in Roche’s Methods, Collaboration, and Outreach group and located in Basel. He does methodological research, provides consulting to Roche statisticians and broader project teams, gives biostatistics trainings for statisticians and non-statisticians in- and externally, mentors students, and interacts with external partners in industry, regulatory agencies, and the academic community in various working groups and collaborations. He has co-founded and co-leads the European special interest group “Estimands in oncology” (sponsored by PSI and EFSPI, which also has the status as an ASA scientific working group, a subsection of the ASA biopharmaceutical section) that currently has 38 members representing 22 companies and several Health Authorities and works on various topics around estimands in oncology.

Evgeny Degtyarev is Global Program Biostatistics Head leading a team of quantitative scientists on CAR-T program in hematology at Novartis. Before joining Novartis in Basel in 2013, Evgeny studied mathematics and economics at the University of Magdeburg in Germany. Since then he has supported several oncology programs with targeted and immunotherapies in different stages of development. He has co-founded and co-leads the industry working group “Estimands in oncology” in Feb 2018 which has been later granted the status of EFSPI/PSI Special Interest Group and ASA Biopharmaceutical Section Scientific Working Group (www.oncoestimand.org).

Zhihao Yao received his master’s degree from University of Rochester in 2009. He has over 10-year experience of data analysis and software development. Before Mr. Yao joined FDA as a mathematical statistician in 2016, he worked as a database programmer at department of Biostatistics University of Mississippi Medical Center and a data analyst at NIH. His works are focused on software development, data analysis and data visualization.

Dr. Lan Huang received her Ph.D. in Statistics from University of Connecticut in 2004. From 2004 to 2009, Dr. Huang worked on cancer surveillance at national cancer institute (NCI). Dr. Huang joined FDA/CDER in 2009 as a statistical reviewer and moved to FDA/CDRH in 2016. She has reviewed submissions for both therapeutic and diagnostic products/devices and has participated in regulatory research for methodologies to improve the quality of review in statistical analysis in clinical trials and safety surveillance in CDER and CDRH.

Dr. Jie Chen is a Distinguished Scientist in Methodology Research at Merck Research Laboratories. Before rejoining Merck in February 2017 (he worked at Merck from 1995-2009), Jie worked in China for six and a half years, leading statistics and statistical programming groups for global pharma companies to support drug development globally and in China. Jie received an M.D. in 1984 from Shanghai First College of Medicine, an MPH in 1994 in biostatistics & epidemiology from the University of Oklahoma Health Science Center, Oklahoma City, and a Ph.D. in 2003 in statistics from Temple University, Philadelphia, Pennsylvania.

Adèle H. Ribeiro is a Postdoctoral Researcher in the Causal Artificial Intelligence Lab at Columbia University. Her research focuses on developing the emergent field of Causal Health Sciences. She holds a Bachelor’s degree in Computational and Applied Mathematics (2012) and Master’s and Ph.D. degrees in Computer Science (2014 and 2018, respectively), all from the Institute of Mathematics and Statistics of the University of São Paulo (USP), Brazil. She also undertook a doctoral research internship in the Developmental Neuromechanics and Communication Lab at Princeton University. Previously, she worked as a Postdoctoral Fellow in the Laboratory of Genetics and Molecular Cardiology at the Heart Institute in USP.

Elias Bareinboim is an Associate Professor in the Department of Computer Science and Director of the Causal Artificial Intelligence Lab at Columbia University, New York. Before joining Columbia, he worked as an Assistant Professor at the Department of Computer Science at Purdue University. He obtained his Ph.D. from the University of California at Los Angeles (UCLA) under the supervision of Professor Judea Pearl, where he also did his post-doctoral fellowship. He is a recipient of the prestigious NSF Career Award. His research area is in the domain of artificial intelligence, more specifically in causal inference. Building on the modern language of causation emerged in the last decades, his work develops a theoretical framework for understanding, representing, and algorithmizing causal generalizations from a heterogeneous mixture of observational and experimental studies.

Dr. Jingjing Ye currently is head of system and standard within Global Statistics and Data Sciences (GSDS) in BeiGene. She leads a team to promote statistical innovations in clinical trials, develop visualization tools to support pre-clinical and clinical development and establish standard and process within BeiGene. She has over 15 years experience in pharmaceutical industry and US FDA. Before BeiGene, she was most recently a statistics team leader in the Office of Biostatistics in CDER. At CDER, she supervised a team of statistical analysts and reviewers for designing, reviewing and analyzing clinical trials to support drug approvals throughout preIND, IND, NDA/BLA and post-approval studies in oncology and hematology.

Fang Liu (PhD in Statistics from Temple University), is a principal scientist at Merck Research Laboratories, Merck & Co., Inc. Dr Liu has been providing statistical support in various areas including early oncology studies, clinical pharmacology studies, PK-PD modeling and oncology biomarker statistics. She has been actively involved in statistical research and has authored/co- authored multiple scientific publications in peer-reviewed statistical and clinical journals. Her current research interests include causal inference, mediation analysis, basket trial design, umbrella trial design, missing data imputation, etc.

Yanping Liu (PhD in Statistics from Temple University), is an Associated Principal scientist at Merck Research Laboratories, Merck & Co., Inc. She works on late stage Cardiovascular and Oncology studies. Her research interests include multiple testing, survival analysis, causal inference and mediation analysis.