Keynote and Panel Discussion on Storytelling Statistics

Sunday, December 11^th, 2022, San Jose State Univeristy

Keynote Speakers and Panelists

Dr. Tara Maddala

PandoraBio and TMBiostats.

Dr. Maddala is the founder of PandoraBio, a mental and behavioral health startup, and Principal at TMBiostats, a consulting firm specializing in clinical strategy and statistics. Dr. Maddala has a proven track record of developing genomic diagnostic products from feasibility through commercialization. As Vice President of Clinical Development at Delfi and before that as a Vice President at GRAIL, she led Clinical Operations and Data Science teams. Prior to that, at Genomic Health, she led a statistical science team and was a co-inventor of the Oncotype DX® Genomic Prostate Score test, employing statistical machine learning for algorithm development. She is co-inventor on several ML-based cancer genomic patents and has co-authored over 20 peer-reviewed publications and over 50 congress presentations. She enjoys volunteering with the BBSW and Young Women In Bio. Dr. Maddala also regularly lectures for the UCSF-Berkeley Translational Medicine program, is an advisor to the UCSF Center for Translational and Policy Research on Personalized Medicine, and is the Chair of the Delfi Scientific Advisory Board. She holds a PhD in Biostatistics from The University of Texas and Engineering BS and MS degrees from The University of Florida and Georgia Tech.

Prof. Nigam Shah

Stanford University

Dr. Shah is Professor of Medicine (Biomedical Informatics) at Stanford University, and serves as the Chief Data Scientist for Stanford Health Care. Dr. Shah's research group analyzes multiple types of health data (EHR, Claims, Wearables, Weblogs, and Patient blogs), to answer clinical questions, generate insights, and build predictive models for the learning health system. In his Chief Data Scientist role, he leads Stanford Healthcare's artificial intelligence and data science efforts in three main areas of impact: advancing the scientific understanding of disease, improving the practice of clinical medicine and orchestrating the delivery of health care. Dr. Shah is an inventor on eight patents and patent applications, has authored over 200 scientific publications and has co-founded three companies. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and was inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.

Prof. Mark Van Der Laan

University of California, Berkeley

Dr. Van Der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor in Biostatistics and Statistics at the University of California, Berkeley. Dr. Van Der Laan research interests include censored data, causal inference, genomics, observational studies and adaptive designs. Dr. Van Der Laan has led the development of two general statistical approaches: Super Learning and Targeted Learning. Targeted Learning improves on typical current statistical practice by avoiding reliance on wrong model assumptions, and its capability to target any question of interest. In 2005 Dr. Van Der Laan was awarded the Committee of Presidents of Statistical Societies (COPSS) Presidential Award in recognition of outstanding contributions to the statistics profession. He also received the 2004 Spiegelman Award and 2005 van Dantzig Award. He is co-founder of the international Journal of Biostatistics and Journal of Causal Inference. Dr. Van Der Laan has authored various books on Targeted Learning, Censored Data and Multiple Testing, published over 400 publications, mentored 60 Ph.D students and 30 postdoctoral fellows.

Dr. Minjie Fan

Google

Dr. Fan is a machine learning engineer and tech lead at the Google Shopping Ads team, leading two efforts: developing deep learning models for ads scoring and ranking, and developing user features and user embeddings based on user past search/click activities. Before that, he was a data scientist at the Google Accelerated Science team solving scientific problems in biology and physics using statistics and machine learning. He graduated from UC Davis with a PhD degree in Statistics in 2017.