Generative AI Coast-to-Coast Webinar Series

The Generative AI Coast-to-Coast webinars brought together prominent speakers from peer data science institutions to discuss Generative AI in research. These webinars aimed to foster a broad community of cross-institutional and interdisciplinary researchers, build research ideas and spark collaboration across disciplines. 

Participating institutions include Johns Hopkins University; the Ohio State University; Rice University; the International Computer Science Institute, an Affiliated Institute of University of California, Berkeley; University of Michigan; and the University of Washington.


Generative AI in Healthcare and Public Health

August 8, 2023, 2 PM EST / 1 PM CST / 11 AM PST


Large Language Models in Medicine: Opportunities and Challenges

Mark Dredze, John C Malone Professor of Computer Science; Director of Research (Foundations of AI), JHU AI-X Foundry; Johns Hopkins University

Abstract of presentation

Abstract: The rapid advance of AI driven by Large Language Models (LLMs), like ChatGPT, has led to impressive results across a range of different use cases. This has included several models developed for the medical domain which have exhibited surprising behaviors, such as answering medical questions and performing well on medical licensing exams. These results have demonstrated the coming transformation of medicine by AI. In this talk, I will provide an overview of some of the recent advances in this area, and discuss challenges and opportunities for the use of these models in medicine.

Bio: Mark Dredze is the John C Malone Professor of Computer Science at Johns Hopkins University and the Director of Research (Foundations of AI) for the JHU AI-X Foundry. He develops Artificial Intelligence Systems based on natural language processing and explores applications to public health and medicine.

Prof. Dredze is affiliated with the Malone Center for Engineering in Healthcare, the Center for Language and Speech Processing, among others. He holds a joint appointment in the Biomedical Informatics & Data Science Section (BIDS), under the Department of Medicine (DOM), Division of General Internal Medicine (GIM) in the School of Medicine. He obtained his PhD from the University of Pennsylvania in 2009.

Abraham Flaxman

Generative AI in Global Health Metrics: opportunities and risks in natural language processing, AI-assisted data analysis, and simulation modeling || VIEW SLIDES

Abraham D. Flaxman, Associate Professor of Health Metrics Sciences, Institute for Health Metrics and Evaluation (IHME), University of Washington

Abstract of presentation

Abstract: Five years ago, I coauthored a short piece titled Machine learning in population health: Opportunities and threats. Now seems like an apt time to revisit it. We argued that AI could automate tasks that people do not like doing, cannot do fast enough, or cannot afford to do. The breakthroughs in generative AI over the last year have expanded the opportunities even further, but the threats have grown as well. We have very promising results from our preliminary investigations into the ability of large language models (LLMs) to identify underlying causes of death by analyzing so-called Verbal Autopsy Interviews, and I am optimistic about the potential for AI-assisted data analysis to broaden participation in technical analyses central to evidence-based public health. While I have only just begun to explore where generative AI fits into agent-based simulation modeling, it seems like a promising direction as well. However, the well-documented penchant of LLMs to hallucinate seems likely to amplify the challenges of explainability that have plagued nonparametric models in the past.

Bio: Abraham Flaxman, PhD, is an Associate Professor of Health Metrics Sciences at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. He is currently leading the development of a simulation platform to derive “what-if” results from Global Burden of Disease estimates and is engaged in methodological and operational research on verbal autopsy. Dr. Flaxman has previously designed software tools such as DisMod-MR that IHME uses to estimate the Global Burden of Disease, and the Bednet Stock-and-Flow Model, which has produced estimates of insecticide-treated net coverage in sub-Saharan Africa.

ModeratorJing Liu, Executive Director, Michigan Institute for Data Science, University of Michigan


Generative AI in the Lab

August 23, 2023, 1 PM EST / 12 PM CST / 10 AM PST


Generative AI for Drug Discovery || VIEW SLIDES

Xia Ning, Professor, Biomedical Informatics, Computer Science and Engineering, The Ohio State University

Abstract of Presentation

Abstract: Artificial Intelligence (AI) for drug discovery has been going far beyond predictive analysis over existing drug candidates. The recent, cutting-edge generative AI enables tremendous opportunities to generate new drug structures and peptide sequences that may not exist but exhibit better properties than any existing ones. This talk will demonstrate three studies on generative AI to 1) generate new small-molecule drug candidates, 2) identify synthetic paths for any (generated) small molecules and 3) generate new binding peptide sequences for MHC Class I proteins. Dr.Ning will present work using auto-encoder-based deep learning, graph neural networks, and deep reinforcement learning for the three studies. Overall, it will show how generative AI can help drug discovery that cannot be achieved using conventional methods.

Bio: Dr. Xia Ning is a Professor in the Biomedical Informatics Department (BMI), and the Computer Science and Engineering Department, The Ohio State University. She is the Vice Chair for Diversity, Equity and Inclusion at BMI, the Section Chief of AI, Clinical Informatics and Implementation Science at BMI, and the Associate Director of Biomedical Informatics at OSU Center for Clinical and Translational Science (CCTS). She received her Ph.D. in Computer Science and Engineering from the University of Minnesota, Twin Cities, in 2012. Ning’s research is on Artificial Intelligence (AI) and Machine Learning with applications in drug discovery, health care and e-commerce. Specific applications include new molecule generation and drug candidate prioritization for drug discovery, drug repurposing for Alzheimer’s disease, cancer drug selection for precision medicine, information retrieval from electronic medical records (EMRs), and EMR analysis. Ning is a Fellow of the American Medical Informatics Association.

Arlei Silva

Generative Models for Graph Data: Challenges and Opportunities

Arlei Silva, Assistant Professor of Computer Science, Rice University

Abstract of Presentation

Abstract: Graphs are a powerful framework for modeling complex systems, such as social, biological, communication, and infrastructure networks. Generative models for graphs have a long history in network science, starting with random graph models in the 50s. In the last few decades, network science has led to several advancements toward generating graphs that reproduce properties seen in the real-world (e.g. degree distributions, clustering). However, network science models, such as Preferential Attachment, are able to generate only the graph topology and their limited number of parameters lacked enough flexibility to capture more than a handful of properties. More recently, deep generative models for graphs have achieved promising results, learning graph models directly from data by extending ideas from computer vision to graph domains. The most successful case studies for these models have been for molecular graphs and, to a lesser extent, code generation. Still, graph generative models failed to achieve the same success as their vision and language counterparts. In this talk, we will discuss some of the key challenges for graph generative models and how modern results from language and vision, such as transformers and diffusion, can help us in addressing these challenges. In particular, we will use physical graphs (e.g. mesh discretizations) and cybersecurity to motivate new research directions on graph generative models.

Bio: My research focuses on developing algorithms and models for mining and learning from complex datasets, broadly defined as data science, especially for data represented as graphs/networks.

I’m particularly interested in problems motivated by computational social science, infrastructure, and healthcare. The tools that I apply to address these problems include machine learning, network science, graph theory, linear algebra, optimization, and statistics.

I got a Ph.D in Computer Science from the University of California, Santa Barbara, advised by Ambuj Singh, where I was also a postdoctoral scholar. Before that, I got a B.Sc and M.Sc degrees in Computer Science from Universidade Federal de Minas Gerais, in Brazil, advised by Wagner Meira Jr. I’ve also been a visiting scholar at the Rensselaer Polytechnic Institute, hosted by Mohammed J. Zaki.

ModeratorJing Liu, Executive Director, Michigan Institute for Data Science, University of Michigan

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Conversations on Policy, Ethics, and Generative AI

August 28, 2023, 1 PM EST / 12 PM CST / 10 AM PST


Marjory Blumenthal, International Computer Science Institute


Marjory S. Blumenthal is a connector of people and ideas and a leader of multi-disciplinary collaborations.  As Principal of MSBlumenthal, LLC she works on science and technology policy issues with universities and nonprofits. In addition, she is a Special Government Employee at the Institute of Museum and Library Services, and she is a Senior Policy Researcher (adjunct) at RAND.

In April 2016 Marjory became the Director of the experimental Science, Technology, and Policy Program at RAND, subsequently holding ad hoc leadership roles and leading or participating in a wide range of research. Her continuing RAND work on automated vehicle safety–an illustration of how artificial intelligence can have physical impacts–has influenced government, industry, and civil society stakeholders.  Between May 2013 and April 2016 Marjory served as Executive Director of the President’s Council of Advisors on Science and Technology (PCAST) within the White House Office of Science and Technology Policy, developing and managing PCAST’s analytical program and addressing how systems engineering can improve health care, challenges in protecting privacy in the context of big data, new directions for cybersecurity, how information technology can improve education, the implications of new technologies for cities, and more. Previously she was Associate Provost, Academic at Georgetown University, developing academic strategy, strengthening the sciences and the overall research program, and promoting innovation in areas from international engagement to teaching and learning.

Before starting at Georgetown, Marjory was the founding Executive Director of the National Academies’ Computer Science and Telecommunications Board (CSTB), where she produced over 60 influential books and reports that addressed the full range of information technologies–including artificial intelligence–and their societal impacts. Marjory did her undergraduate work at Brown University and her graduate work at Harvard University, both in interdisciplinary programs. She has served on numerous advisory bodies and received multiple awards over the course of her career.

Merve Hickok

Merve Hickok, Responsible Data and AI Advisor, Michigan Institute for Data Science; President, Center for AI & Digital Policy; Founder,


Merve Hickok is the Responsible Data and AI advisor for MIDAS as well as the founder of She is a globally renowned expert on AI policy, ethics and governance. Her contributions and perspective have featured in the Guardian, CNN, Forbes, Bloomberg, Wired, Scientific American, Politico, Protocol, Vox, The Economist and S&P. Her research, training and consulting work focuses on the impact of AI systems on individuals, society, public and private organizations – with a particular focus on fundamental rights, democratic values, and social justice. She provides consultancy to C-suite leaders, and training services to public and private organizations on Responsible AI development, due diligence and governance. She also teaches data ethics at University of Michigan, and serves as a Board member in multiple organizations.

Merve is the President and Research Director at Center for AI & Digital Policy, deeply engaged in global AI policy and regulatory work. The Center educates AI policy practitioners and advocates across 60+ countries, advises international organizations (such as European Commission, UNESCO, the Council of Europe, OECD).

Merve has provided testimony to the US Congress, State of California Civil Rights Office, New York City Department of Consumer and Worker Protection, Detroit City Council, and many global organizations interested in AI policy and ethics. 

Merve also works with several non-profit organizations globally to advance both the academic and professional research in this field for underrepresented groups. She has been recognized by a number of organizations – most recently as one of the 100 Brilliant Women in AI Ethics™ – 2021, and as Runner-up for Responsible AI Leader of the Year – 2022 (Women in AI).

Previously, Merve held various senior roles in Fortune 100 companies for more than 15 years.

Dr. Jagadish

Moderator: H. V. Jagadish, Director, Michigan Institute for Data Science; Edgar F Codd Distinguished University Professor of Electrical Engineering and Computer Science, Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, Professor of Electrical Engineering and Computer Science, College of Engineering, University of Michigan


H. V. Jagadish is the Director of the Michigan Institute for Data Science, Edgar F. Codd Distinguished University Professor, and Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor. Before his professorship, he was Head of the Database Research Department at AT&T Labs. Dr. Jagadish’s research focuses on two themes: the usability of database systems, query models and analytics processes to inform decision-makers, especially with big and heterogeneous data that go through many transformations; data equity systems that center around issues of representation, diversity, fairness, transparency, and validity. Dr. Jagadish is an elected ACM Fellow and AAAS Fellow. His many academic scholarship roles include establishing the ACM SIGMOD Digital Review and founding the Proceedings of the Very Large Database Endowment (PVLDB), serving on the boards of the Computing Research Association (CRA) and the Very Large Database Endowment.

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An Under the Hood Look at Generative AI: Potentials and Pitfalls

August 31, 2023, 3 PM EST / 2 PM CST / noon PST


Generative AI – What Could go wrong?

David Evan Harris, Senior Research Fellow, International Computer Science Institute

Rada Mihalcea

Moving beyond one-size-fits-all in generative AI

Rada Mihalcea, Janice M Jenkins Collegiate Professor of Electrical Engineering and Computer Science; Director, Michigan Artificial Intelligence Laboratory; University of Michigan

Moderator: Sarah Stone, Executive Director, eScience Institute, University of Washington

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University of Washington eScience Institute