Short Courses

Short courses will be hybrid.

Short Course 1: Machine Learning for Analyzing Patient Data

Instructor: Fei Wang, Cornell University

Fei Wang Fei Wang is an Associate Professor in Division of Health Informatics, Dept of Population Health Sciences, Weill Cornell Medicine, Cornell University. He is the founding director of the WCM institute of AI for Digital Health. His research interest is AI and digital health and has published more than 300 papers in areas such as ICML, KDD, NIPS, CVPR, AAAI, IJCAI, Nature Medicine, JAMA Internal Medicine, Annals of Internal Medicine, Lancet Digital Health. His papers have received over 24,000 citations with an H-index 76. His team won the championship of the AACC PTHrP result prediction challenge in 2022, NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations and Parkinson's Progression Markers' Initiative data challenge organized by Michael J. Fox Foundation. Dr. Wang is a recipient of the NSF CAREER Award, as well as the inaugural research leadership award in IEEE International Conference on Health Informatics and received the Sanofi iDEA Award, Google Faculty Research Award and Amazon AWS Machine Learning for Research Award. He is the past chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association and a fellow of AMIA, IAHSI, ACMI and a distinguished member of ACM.



Short Course 2: Bayesian Modeling in Oncology Trials

Instructors: Brian Hobbs, Dell Medical School & Alex Kaizer, Colorado School of Public Health

Brian Hobbs Dr. Hobbs completed a doctoral degree in biostatistics at the University of Minnesota and then joined The University of Texas MD Anderson as an Assistant Professor of biostatistics. He was promoted to Associate in 2017, and then recruited to Cleveland Clinic to found a Section of Cancer Biostatistics. He joined The University of Texas Dell Medical School in August 2020 as a tenured Associate Professor. In 2016, Dr. Hobbs was selected by The University of Minnesota for the Emerging Leader Award. Recognized as an expert in clinical oncology research methodology, in 2017 Dr. Hobbs was invited to lead the publication of NCI’s Clinical Trials Design Task Force providing recommendations for seamless designs in first-in-human cancer trials. In 2020, he was invited to contribute to an article for Nature Reviews Clinical Oncology describing the current state of tumor agnostic trials. In 2021, Dr. Hobbs was invited to review the landscape of basket trials in the Journal of Clinical Oncology.

Alex Kaizer Dr. Kaizer is an Assistant Professor in the Department of Biostatistics & Informatics and a faculty member in the Center for Innovative Design & Analysis (CIDA) at the University of Colorado-Anschutz Medical Campus. He is passionate about translational research and the development of novel adaptive clinical trial designs that more efficiently and effectively utilize available resources, past trials, and past studies. Dr. Kaizer strives to translate complex statistical topics into understandable material that is more than "just math" and something we can appreciate and utilize in our daily lives and research.



Short Course 3: Statistical Methods for Precision Medicine

Instructor: Lu Tian, Stanford University

Lu Tian Dr. Tian obtained his Doctor's degree from Harvard University at 2002. He was an assistant professor at Northwestern University before joining Stanford University at 2007. He is currently a Professor at the Department of Biomedical Data Science, and the Department of Statistics by courtesy at Stanford University. Dr. Tian's research interest includes clinical trial, survival analysis, precision medicine and meta-analysis. Dr. Tian is a fellow of American Statistical Association and has published more than 200 peer-reviewed papers.


The basic idea of precision medicine is to use patient's specific characteristics, such as genetic make-up, biomarker profile, clinical history, environmental exposure, etc., to guide clinical decision making for effective prevention and treatment. Recent advances in high-throughput and information technologies can easily and robustly generate a large amount data to characterize individual patients, offering opportunities to develop and promote precision medicine in daily clinical practice. The development of such a smart and targeted strategy needs to be empirically data-driven and the corresponding challenges in the statistical front are huge, mainly because analyzing heterogeneous treatment effect/associations, i.e., interaction, is much more difficult than analyzing the homogeneous counterparts, i.e., main effect. The goal of this course to introduce recently developed statistical and machine learning techniques for precision medicine. Most of the course will focus on how to optimally assign treatment to patients according to his or her personal characteristics in the context of two-arm randomized clinical trial. However, we will also discuss extensions to multi-arm trials and observational studies. Specifically, we will cover the following topics: the casual inference framework for personalized treatment effect based on counterfactual outcomes; estimation of optimal treatment selection rule including subgroup analysis, treebased method, regression modeling, modified covariates approach, Q-learning and outcome weighted learning; and validation and statistical inference of the optimal treatment selection strategy. We will also discuss the computational perspective of the aforementioned methods including dimension reduction via regularization and applications of machine learning methods. We will provide examples for how to construct and evaluate estimated optimal treatment regimes in R. There is no requirement for prior exposure to precision medicine or machine learning methods, but prior knowledge of basic methods such as regression, interactions, and analysis of randomized clinical trials is expected.

Short Course 4: Adaptive Designs for Early Phase Clinical Trials in Oncology

Instructor: Yuan Ji, University of Chicago

Yuan Ji 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 150 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.


In this half-day short course, we will introduce, describe, and demonstrate innovative designs for early-phase oncology trials. The teaching will be delivered by Dr. Yuan Ji, who will present novel statistical designs for phase 1a dose escalation, phase 1b expansion cohorts, drug combination dose finding, designs accommodating delayed toxicity like in immune oncology, designs for early-phase basket trials, and strategies for dose optimization in oncology. Most designs introduced in the short course will use Bayesian modeling and adaptive decision rules. A brief introduction of Bayesian statistics will also be provided. Lastly, some software packages will be briefly illustrated.