Dr. Ying Lu, Ph.D

Dr. Ying Lu, Ph.D., is a Professor of Biomedical Data Science, and by courtesy, of the Health Research and Policy and of Radiology, co-director of the Center for Innovative Study Design and Biostatistics Core of the Stanford Cancer Institute, Stanford University School of Medicine. He received his Ph.D. in Biostatistics from the University of California, Berkeley.  Professor Lu has considerable experience in statistical methodological research and led the planning and conduct of large multicenter clinical trials through the VA Cooperative Studies Program (Director of the Palo Alto Coordinating Center 2009-2016). Professor Lu’s current research interests include the statistical design and analytic methods for clinical trials, validation of biomarkers/medical diagnoses, meta-analysis, and medical decision making. He is the author of 245 peer-reviewed publications and editor of several books. He is the 2019 President Elect of WNAR, the 2014 President of the International Chinese Statistical Association (ICSA) and served as a member of DSMBs for clinical trials and FDA PCNS Drug Advisory Committee. Professor Lu was a fellow of ASA and biostatistics editor of JCO Precision Oncology.


Jason is a Professor and Chair of the Department of Biostatistics and Epidemiology at Rutgers University, Co-Director of the biostatistics core for the New Jersey Alliance For Clinical and Translational Science, and Co-Director of the Center for Causal Inference. He has expertise in Bayesian methods, causal inference, and missing data. His primary recent methodological research has focused on developing flexible Bayesian models for complex observational data, especially from large healthcare databases. He has collaborated in a wide variety of clinical research areas, including chronic kidney disease, chronic viral hepatitis infection, and HIV.

Susan S. Ellenberg, University of Pennsylvania

Dr. Ellenberg’s research has focused on practical problems and ethical issues in designing, conducting and analyzing data from clinical trials, including surrogate endpoints, data monitoring committees, clinical trial designs, adverse event monitoring, vaccine safety and special issues in cancer and AIDS trials. At Penn, in addition to her teaching and administrative duties she serves as senior statistician for several multicenter clinical trials and directs the Biostatistics Core of the Penn Center for AIDS Research.  She chairs the organizing committee for the annual Penn conference on statistical issues in clinical trials. She also served for many years as Associate Dean for Clinical Research, overseeing the human subjects protections programs, training and centralized research support of the Perelman School of Medicine.


Nandita Mitra, PhD is Professor of Biostatistics, Vice-Chair of Faculty Professional Development, Chair of the Graduate Group in Epidemiology and Biostatistics, and Co-Director of the Center for Causal Inference at the University of Pennsylvania. Her primary methodological research focuses on propensity score and instrumental variables approaches to the analysis of observational data and causal inference approaches to cost-effectiveness estimation. Her collaborative research areas include cancer outcomes, cancer genetics, health policy, and health economics.

Wei-Yin Loh, University of Wisconsin, Madison

Wei-Yin Loh is Professor of Statistics at the University of Wisconsin, Madison. His research interests are in bootstrap theory and methodology and algorithms for classification and regression trees. Loh is a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a consultant to government and industry. He is a recipient of the Reynolds Award for teaching, the U.S. Army Wilks Award for statistics research and application, an Outstanding Science Alumni Award from the National University of Singapore, and visiting fellowships from AbbVie, IBM and the Bureau of Labor Statistics.

Nan Lin, Washington University in St. Louis

Nan Lin is an Associate Professor in the Department of Mathematics at Washington University in St. Louis and has a joint appointment in the Division of Biostatistics, Washington University in St. Louis, School of Medicine.  His methodological research is in the areas of big data, quantile regression, bioinformatics, Bayesian statistics, longitudinal and functional data analysis. His applied research involves statistical analysis of data from anesthesiology and genomics. He teaches a wide range of statistics courses, including mathematical statistics, Bayesian statistics, linear models, experimental design, statistical computation, and nonparametric statistics.

He earned a B.S. (1999) from University of Science and Technology of China, a M.S. (2000) and Ph.D. (2003) in Statistics, and a second M.S. (2003) in Finance from University of Illinois at Urbana-Champaign.  Before joining Washington University, he was a postdoctoral associate (2003-2004) at the Center for Statistical Genomics and Proteomics, Yale University.

mark chang, Senior VP veristat

Dr. Mark Chang is founder of AGInception, a research organization for artificial general intelligence. He was Sr. Vice President, Strategic Statistical Consulting at Veristat and 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. Dr. Chang has published 10 books, including Adaptive Design Theory and Implementation Using SAS and RParadoxes in Scientific InferencesModern Issues and Methods in BiostatisticsMonte Carlo Simulation for the Pharmaceutical IndustryPrinciples of Scientific Methods, and Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials.

Frank Fleischer, PhD, Boehringer-Ingelheim Pharma

Dr. Frank Fleischer, Head of Methodology Statistics, Boehringer-Ingelheim Pharma GmbH & Co. KG. Being a trained mathematician and statistician Frank has worked for more than 10 years in the pharmaceutical industry. He is heading a global team of statisticians at Boehringer Ingelheim focusing on statistical methodology and the implementation of innovative statistical designs into practice. In that role, Frank and his team are considered with methodological questions regarding adaptive designs, statistical decision making, dose finding and Bayesian borrowing designs as well as with piloting these methods in clinical trials. Through this function several projects across different therapeutic areas and phases are supported. Formerly he has been a lead project statistician for different projects in oncology, immunology and the biosimilars.

Pinggao Zhang, PhD, Takeda

Pinggao Zhang is now a team lead and director of biostatistics in Takeda Pharmaceutical Company Limited, Cambridge, MA. He previously worked for Shire, Purdue Pharma, Scirex, and Aventis with increasing responsibilities. Dr. Zhang has been managing and leading biostatistics activities in support of clinical research across all development phases, regulatory submissions, and publications. He has worked in various therapeutic areas including vaccine, oncology, CNS, analgesics, immunology, and hematology, and has contributed to several successful drug approvals. In addition, Dr. Zhang is a committee member of the Deming Conference and has served as an invited speaker at various occasions. Both speakers have extensive experience in biostatistical and clinical trial methodological development and consulting to government and biopharmaceutical industries in SAS and R.

Mark J. van der Laan, University of California at Berkeley

Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. He has made contributions to survival analysis, semiparametric statistics, multiple testing, and causal inference. He also developed the targeted maximum likelihood methodology and general theory for super-learning. He is a founding editor of the Journal of Causal Inference and International Journal of Biostatistics. He has authored 4 books on targeted learning, censored data and multiple testing, authored over 300 publications, and graduated 45 Ph.D. students. He received his Ph.D. from Utrecht University in 1993 with a dissertation titled “Efficient and Inefficient Estimation in Semiparametric Models”. He received the COPSS Presidents’ Award in 2005, the Mortimer Spiegelman Award in 2004, and the van Dantzig Award in 2005.

Din Chen, UNC

Dr. 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 25 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. Dr. Chen has been invited nationally and internationally to speak and give short courses at various scientific conferences. In fact, Dr. Chen is a committee member of the Deming Conference and has been invited to give various tutorials at Deming Conference since 2011.

Joseph Cappelleri, PhD, Pfizer

Joseph C. Cappelleri, PhD, MPH, MS is an executive director in the Statistical Research and Data Science Center at Pfizer Inc. He earned his M.S. in statistics from the City University of New York (Baruch College), Ph.D. in psychometrics from Cornell University, and M.P.H. in epidemiology from Harvard University. As an adjunct professor, Dr. Cappelleri has served on the faculties of Brown University, University of Connecticut, and Tufts Medical Center. He has delivered numerous conference presentations and has published extensively on clinical and methodological topics (approximately 1,500 co-authored publications and conference presentations), including on regression-discontinuity designs, meta-analyses, and health measurement scales. He is lead author of the book Patient-Reported Outcomes: Measurement, Implementation and Interpretation and has co-authored or co-edited three other books (Phase II Clinical Development of New Drugs, Statistical Topics in Health Economics and Outcomes Research, Design and Analysis of Subgroups with Biopharmaceutical Applications). Dr. Cappelleri is a Fellow of the American Statistical Association.

Professor Thomas Mathew, UMBC

Thomas Mathew, PhD, is Professor, Department of Mathematics & Statistics, University of Maryland  Baltimore County (UMBC). He earned his PhD in statistics from the Indian Statistical Institute in 1983, and has been a faculty member at UMBC since 1985. He has delivered numerous conference presentations, nationally and internationally, and has published extensively on methodological and applied topics, including cost-effectiveness analysis, bioequivalence testing, exposure data analysis, meta-analysis, mixed and random effects models, and tolerance intervals. He is the co-author of two books Statistical Tests in Mixed Linear Models and Statistical Tolerance Regions: Theory, Applications and Computation, both published by WileyHe has served on the Editorial Boards of several journals, and is currently an Associate Editor of the Journal of the American Statistical Association, Journal of Multivariate Analysis, and  Sankhya. Dr. Mathew is a Fellow of the American Statistical Association, and a Fellow of the Institute of Mathematical Statistics.  He has also been appointed as Presidential Research Professor at his campus.

S. Stanley Young, PhD, CGSTAT

S. Stanley Young is currently the CEO of CGStat and previously worked at Eli Lilly, GlaxoSmithKline and the National Institute of Statistical Sciences on questions of applied statistics. His current interest is studying methods used in the evaluation of observational studies. He also works on bioinformatics problems. Dr. Young graduated from North Carolina State University, BS, MES and a PhD in Statistics and Genetics. He worked in the pharmaceutical industry on all phases of pre-clinical research. He has authored or co-authored over 70 papers including six “best paper” awards, and a highly cited book, Resampling-Based Multiple Testing. He has three issued patents. He is interested in all aspects of applied statistics. He conducts research in data mining.

Professor Shein-Chung Chow, Duke University

Shein-Chung Chow, Ph.D. is a Professor of Biostatistics and Bioinformatics at Duke University School of Medicine, Durham, NC. Dr. Chow is also a special government employee (SGE) appointed by the FDA as an Advisory Committee voting member and Statistical Advisor to the FDA. Between 2017 and 2019, Dr. Chow was on leave for the Food and Drug Administration (FDA) as an Associate Director at Office of Biostatistics, Center for Drug Research and Evaluation (CDER), FDA. Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Editor-in-Chief of the Biostatistics Book Series at Chapman and Hall and CRC Press, Taylor & Francis. Dr. Chow is Fellow of the American Statistical Association, who is the author or co-author of over 300 methodology papers and 30 books including Design and Analysis of Clinical Trials (Wiley & Sons), Adaptive Design Methods in Clinical Trials (Chapman and Hall/CRC Press), and most recently, Statistics in Regulatory Science (Chapman and Hall/CRC Press)

Walter R. Young

BChE from CUNY. MChE and MSOR from NYU. NY Professional Engineering license. ASQ Quality and Engineering Certification. Elected ASQ Fellow in 1980. Founder and chair for 3 years of the Tappan Zee section. Awarded the Deming Silver Medal and the Ellis R. Ott award. Chaired the ASTM E-11 nonparametric methods subcommittee and the working group that rewrote its outlier standard. Worked for 31 years for Lederle Labs until Wyeth acquired it. Retired as a Principal Clinical Programmer in 2005. A Pfizer retiree of 41 years as Pfizer later acquired Wyeth. Published more than 2-dozen professional papers and gave numerous talks. Expertise in a number of programming languages and graphics.

Dr. Jie Chen, Merck Research Laboratories

Jie 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. Jie’s experience includes statistical methodology research and applications in non-clinical and pre-clinical research, clinical development, and post-licensure product life-cycle management. He has given short courses at FDA/Industry statistics workshop, EMA statistics symposium and many invited talks at academic institutions and statistical conferences.

Professor Chris Schmid, Brown University

Christopher Schmid is Professor and Chair of Biostatistics at Brown University School of Public Health where he co-founded the Center for Evidence Synthesis in Health. Before that he worked for many years directing the Biostatistics Research Center at Tufts Medical Center in Boston. He has a long record of collaborative research in diverse areas of medicine and health with academia, government and industry and has more than 200 peer-reviewed publications. He has coauthored consensus CONSORT reporting guidelines for N-of-1 trials and single-case designs, and PRISMA guidelines extensions for meta-analysis of individual participant studies and for network meta-analyses as well as the Institute of Medicine report that established US standards for systematic reviews. His research focuses on Bayesian methods for meta-analysis, including networks of treatments and N-of-1 designs, as well as open-source software tools. He has developed predictive models for heart attack risk and the risk of dehydration in children suffering from diseases in the developing world.

Matt Downs - University of California, Berkeley

Matt Downs (MPH in Epidemiology and Biostatistics from the University of California, Berkeley) is a statistical scientist at Statistics Collaborative. Since 1999, he has served as the independent reporting statistician to DMCs for multinational Phase 2 and 3 trials in many disease areas. Mr. Downs speaks at professional meetings on a range of statistical topics, including dynamic allocation methods, conditional power, and implementation of treatment assignment algorithms. He is a member of the American Statistical Association and the Society for Clinical Trials.

Rachael V. Phillips -PhD at University of California

Rachael V. Phillips is a second year Ph.D. student in Biostatistics at the University of California, Berkeley. She has an M.A. in Biostatistics, B.S. in Biology with a Chemistry minor and a B.A. in Mathematics with a Spanish minor. She has extensive experience transforming large, messy datasets into insights and communicating to technical and non-technical audiences. Motivated by issues arising in health care, Rachael leverages strategies rooted in causal inference and nonparametric estimation to build clinician-tailored, machine-driven solutions.

Find out more on our Walter Young Scholarship Award Program