Dr. Qiao Liu is an Assistant Professor in the Department of Biostatistics at Yale University. His research lies at the intersection of statistics, artificial intelligence, and computational biology, where he develops practical statistical and AI-driven tools with both theoretical and applied significance. His work leverages advances in generative AI to address high-dimensional statistical challenges, including density estimation and causal inference, with broad applications in single-cell genomics, multi-omics data integration, and genomic large language models. Dr. Liu has authored over 40 publications in leading journals and conferences, including Nature Machine Intelligence, PNAS, Genome Biology, Nucleic Acids Research, ISMB, ECCB, NeurIPS and MICCAI. His contributions have been recognized with prestigious honors, including the NIH Pathway to Independence Award.

Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, and the COPSS 2025 Snedecor Award. He has published more than 340 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, JASA, Biometrika, and JRSSB, as well as presenting 58+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI, and KDD. He is the coordinating editor of JASA and the editor of JASA ACS.

Dr. D’Angelo has extensive experience with biomarkers. She has over 20 years academic and industry experience on biomarker-related statistical methods, biomarker discovery, and biomarker-driven clinical trials. She received a PhD in Biostatistics from University of Pittsburgh focusing on missing data methods. After receiving her degree, she did a postdoc in Biostatistics at University of Pittsburgh focusing on genetics and neuroimaging statistical approaches. From 2007-2013, she was faculty at Washington University Division of Biostatistics where she was involved as collaborator on Alzheimer’s and depression studies and grants incorporating biomarkers. She was a PI on a K award focused on statistical approaches for neuroimaging in dementia. Educating has been a constant component for Dr. D’Angelo where she has taught many courses as faculty in SAS, survival, and clinical trials, and has mentored many junior faculty and junior colleagues.

Currently, Gina is a Director at AstraZeneca Oncology Statistical Innovation and offers her guidance and expertise on many of the early-late phase trials including a program devoted to a novel AI biomarker in Oncology, where she participates in strategic decisions regarding patient enrichment and development of relevant biomarkers. She has designed numerous trials and consulted on statistical analyses, approaches, and design for numerous trials. Her involvement in a respiratory biomarker-driven design led to a successful noninferiority trial and novel design and successful lung cancer trial with a novel combined biomarker leading to two patents pending. As a result of collaborations, she has published multiple statistical and clinical papers on biomarkers. She is currently co-editing a book on Statistical and design consideration for biomarkers in clinical trials. Her experience spans from discovery to late phase trials, with small to high-dimensional data, across various therapeutic areas. This instructor will offer abundant and valuable information where the attendees will benefit from her extensive knowledge and hands-on experience.

Some related publications

• Yende S, D’Angelo G, Kellum JA, Weissfeld L, Fine J, Welch RD, Kong L, Carter M, Angus DC. Inflammatory markers at hospital discharge predict subsequent mortality after pneumonia and sepsis. Am J Respir Crit Care Med, 177(11):1242–1247, 2008.
• D’Angelo G, Weissfeld L. An index approach for the Cox model with left censored covariates. Stat Med, 27(22):4502–4514, 2008.
• Tarawneh R, D’Angelo G, Macy E, et al. Visinin-like protein-1: diagnostic and prognostic biomarker in Alzheimer’s disease. Ann Neurol, 70(2):274–285, 2011.
• Kelly JE, Mar S, D’Angelo G, et al. Susceptibility-weighted imaging helps to discriminate pediatric multiple sclerosis from ADEM. Pediatr Neurol, 52(1):36–41, 2015.
• D’Angelo G, Chaerkady R, Yu W, et al. Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics. J Proteome Res, 16(9):3124–3136, 2017.
• D’Angelo G, Ran D. Tutorial on Firth’s logistic regression models for biomarkers in preclinical space. Pharm Stat, 2024. doi:10.1002/pst.2422.
• D’Angelo G, Tian X, Deng C, Zhou X. Predictive biomarker graphical approach (PRIME) for Precision medicine. arXiv:2504.08087, 2025.
• D’Angelo G, Ran D, Yu B. Evaluation of optimal cut-offs and dichotomous combinations for two biomarkers to improve patient selection. arXiv:2504.07159, 2025.

Dr. Shen obtained his Ph.D. from the University of Pittsburgh, specialized in statistical genetics and genomics. Dr. Shen is currently a Senior Director of Clinical Statistics at Bayer. He plays a pivotal role in early-phase clinical development and precision medicine. Among his notable accomplishments, Dr. Shen led Bayer’s first global companion diagnostic (CDx) submissions, achieving regulatory approvals from health agencies in the United States, Japan, and Europe, as well as completing the submission process in China. Furthermore, he led the statistical development of biomarker sections in multiple global drug submission packages, ensuring that the statistical strategies for patient selection and efficacy assessment met the rigorous standards of diverse global health agencies.

In addition to his leadership and innovation in clinical statistics at Bayer—where he also serves as a science fellow focusing on novel methods such as Bayesian approaches and deep learning—Dr. Shen brings a wealth of teaching experience. From 2013 to 2016, he taught a series of courses through NIH Continuing Medical Education (CME) programs, introducing participants to statistical software (R), fundamental and advanced statistical techniques, machine learning methodologies, and the principles of experimental design. Offered both on-site and online, these courses attracted over 500 attendees, including healthcare professionals, clinical researchers, and biostatisticians, and were designed to enhance practical skill sets, promote best practices in data analysis, and foster evidence-based decision-making in clinical research.

Dr. Shen’s unique combination of hands-on clinical studies, methodological innovation, and extensive teaching background positions him ideally to guide course participants through the complexities of companion diagnostic (CDx) studies. His real-world insights and expertise ensure that attendees will gain not only a solid theoretical understanding of statistical methods but also the practical knowledge needed to apply these principles effectively in their own clinical studies.

Some related publications

• Erin R. Rudzinski, Jaclyn Hechtman, Sinchita Roy-Chowdhuri, Marion Rudolph, Christina M. Lockwood, Josh Silvertown, Justyna Wierzbinska, Kui Shen, Ricarda Norenberg, Hendrik Nogai, David S. Hong, Alexander Drilon, Theodore W. Laetsch, (2022) Diagnostic testing approaches for the identification of patients with TRK fusion cancer prior to enrollment in clinical trials investigating larotrectinib, Cancer Genetics, Volumes 260–261, Pages 46-52.
• Franck Morschhauser, Jean-Pascal Machiels, Gilles Salles, Sylvie Rottey, Simon A.J. Rule, David Cunningham, Frederic Peyrade, Christophe Fruchart, Hendrik-Tobias Arkenau, Isabelle Genvresse, Li Liu, Karl Köchert, Kui Shen, Christoph Kneip, (2020) On-Target Pharmacodynamic Activity of the PI3K Inhibitor Copanlisib in Paired Biopsies from Patients with Malignant Lymphoma and Advanced Solid Tumors, Mol Cancer Ther,19 (2): 468–478.
• Asis Khana, Ayako Wendy Fujitaa, Nadine Randleb, Javier Regidor-Cerrilloc, Jahangheer S. Shaika, Kui Shen, Andrew J. Olerd, Mariam Quinonesd, Sophia M. Lathame, Bartholomew D. Akanmorif, Sarah Cleavelandg, Elisabeth A. Innesh, Una Ryani, Jan Slapetaj, Gereon Scharesk, Luis M. Ortega-Morac, Jitender P. Dubeyl,Jonathan M. Wastlingb, and Michael E. Grigg, (2019) Global selective sweep of a highly inbred genome of the cattle parasite Neospora caninum. PNAS, Vol. 116, No. 45, 116 (45) 22764-22773.
• Sriram Sridhar, Luis Paz-Ares, Hao Liu, Kui Shen et. al. (2019) Prognostic Significance of Liver Metastasis in Durvalumab-Treated Lung Cancer Patients. Clinical Lung Cancer, Volume 20, Issue 6, November 2019, Pages e601-e608.

Ying Yuan is Bettyann Asche Murray Distinguished Professor and Chair in the Department of Biostatistics at University of Texas MD Anderson Cancer Center. Dr. Yuan is an internationally renowned researcher in innovative Bayesian adaptive designs, including early phase trials, seamless trials, biomarker-guided trials, and basket and platform trials. The designs and software developed by Dr. Yuan’s lab (www.trialdesign.org) have been widely used in medical research institutes and pharmaceutical companies. The BOIN design, developed by Dr. Yuan’s team, is a groundbreaking oncology dose-finding design that has been recognized by the FDA as a fit-for-purpose drug development tool. Dr. Yuan was elected as the American Statistical Association Fellow, and is the leading author of two books, “Bayesian Designs for Phase I-II Clinical Trials” and “Model-Assisted Bayesian Designs for Dose Finding and Optimization,” both published by Chapman & Hall/CRC.

Dr Yong Zang is the Indiana University School of Medicine Showalter Scholar Associate Professor in the Department of Biostatistics and Health Data Science, Indiana University. He also serves as the Co-Director of Clinical Research for the Biostatistics and Data Management Core, IU Simon Comprehensive Cancer Center. He received his Ph.D. degree in Statistics from the University of Hong Kong and finished his Postdoctoral training in The University of Texas, MD Anderson Cancer Center. His research interests are clinical trial design and health data informatics. He has published over eighty papers in peer-reviewed statistical, informatics and medical journals. His research is supported by National Institute of Health, Showalter Trust and Indiana CTSI.