Short Courses

Mastering Storytelling for Pharma Data Science Professionals

Abstract: In today's data-driven pharma industry, compelling storytelling has become paramount for data science professionals. This three-hour course encompasses three sections to help participants to learn the most essential skillset for storytelling.

Part 1: Engaging Storyline Composition

Here, participants will delve into the foundational "Why, Who, When, Where, What, and How" questions of crafting a storyline, the most important component for every story.

Part 2: Effective Story Delivery

This section illuminates the nuances of non-verbal communication with interactive exercises. Participants will learn to harness energy modulation and body language to transform standard presentations into compelling ones.

Part 3: Visualization with R Shiny

The concluding section shall teach participants to create dynamic, user-centric web applications, and how to use powerful visualization to facilitate effective communication of analytical insight.

Naitee Ting, Boehringer-Ingelheim

Naitee Ting Naitee Ting is a Fellow of American Statistical Association (ASA). He is currently a Director in the Department of Biostatistics and Data Sciences at Boehringer-Ingelheim Pharmaceuticals Inc. (BI). He joined BI in September of 2009, and before joining BI, he was at Pfizer Inc. for 22 years (1987-2009). Naitee received his Ph.D. in 1987 from Colorado State University (major in Statistics). He has an M.S. degree from Mississippi State University (1979, Statistics) and a B.S. degree from College of Chinese Culture (1976, Forestry) at Taipei, Taiwan.

Zhiwei Yin, Bristol Myers Squibb

Zhiwei Yin Dr. Zhiwei Yin currently serves as a Senior Manager at Bristol Myers Squibb within the BIA Commercial Data Science division, where he focuses on harnessing modeling and AI technology to enhance patient engagement with BMS medications. Before this, he had extensive tenure in small molecule drug development with research experiences in drug substance process development, crystallization, material science, and preformulation. With a strong passion in data, he has built digital capabilities to enable high throughput experimentation (HTE), portfolio management, and business decision-making. Dr. Yin obtained his PhD in Chemistry from City University of New York and computer science training from New York University.

Jonathan Tisack, BeiGene

Jonathan Tisack Jonathan Tisack is a Data Scientist currently working at BeiGene on the Data Science and Digital Innovations team. He builds products and tools for various application areas, including clinical operations, medical monitoring, and competitive intelligence. He also serves as an administrator for the R infrastructure at BeiGene and consults on R/Shiny development across the company.

Jonathan obtained his M.S. in Mathematics from Wichita State University and his B.S. in Statistics from University of Michigan.

Large Language Models in Biomedicine

Fei Wang, Weill Cornell Medicine

Fei Wang Fei Wang is an Associate Professor in Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine (WCM), Cornell University. He is also the founding director of the WCM institute of AI for Digital Health (AIDH). His major research interest is AI and digital health. He has published more than 300 papers on the top venues of related areas such as ICML, KDD, NIPS, CVPR, AAAI, IJCAI, Nature Medicine, JAMA Internal Medicine, Annals of Internal Medicine, Lancet Digital Health, etc. His papers have received over 27,000 citations so far with an H-index 79. His (or his students’) papers have won 8 best paper (or nomination) awards at top international conferences on data mining and medical informatics. His team won the championship of the AACC PTHrP result prediction challenge in 2022, NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations in 2017 and Parkinson's Progression Markers' Initiative data challenge organized by Michael J. Fox Foundation in 2016. Dr. Wang is the recipient of the NSF CAREER Award in 2018, as well as the inaugural research leadership award in IEEE International Conference on Health Informatics (ICHI) 2019. Dr. Wang also received prestigious industry awards such as the Sanofi iDEA Award (2021), Google Faculty Research Award (2020) and Amazon AWS Machine Learning for Research Award (2017, 2019 and 2022). Dr. Wang’s Research has been supported by a diverse set of agencies including NSF, NIH, ONR, PCORI, MJFF, AHA, etc. Dr. Wang is the past chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association (AMIA). Dr. Wang is a fellow of AMIA, a fellow of IAHSI, a fellow of ACMI and a distinguished member of ACM.

Abstract: Recently large language models (LLMs) have attracted enormous attentions from various disciplines including biomedicine due to their impressive performance. This short course will provide an overview of the history of LLMs and their fundamentals. I will particularly emphasize their potentials and limitations in biomedicine.

Short Courses Location

Short courses in both morning and afternoon sessions will be held at the School of Business Building, Room 218, located at 2100 Hillside Rd, Storrs, CT 06269. You can find it conveniently located across from the South Parking Garage (SPG) and the Bookstore/Starbucks.