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Department of Pharmaceutical Outcomes & Policy College of Pharmacy
About Overview

DAISY 2024 Speaker Bios and Presentation Details

Access the Interactive Agenda page for a searchable list of event times -or- download the agenda from here: Full Brochure.pdf -or- Agenda only.pdf

(Please refresh this screen if you downloaded a pdf previously, as newer draft versions of this agenda reflect minor shifts in event timing before being uploaded as final on Sept 16, 2024.)

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Presentation Details by Event Type (chronological)

Speakers will be present onsite unless noted otherwise.

Keynote Speakers

  • Dr. Danielle Belgrave (GSK); Sept 9th, 9:00am – 10:00am [virtual]
    • Working smarter, not harder in drug development: AI assistants for accelerating biological discovery
  • Dr. Jason Moore (Cedars-Sinai); Sept 9th, 10:00am – 11:00am
    • Knowledge-Aware Automated Machine Learning for Prescriptive AI: The Tree-Based Pipeline Optimization Tool (TPOT)
    • A central challenge of using machine learning for predictive analytics is the many decisions that need to be made about which algorithms to use and what their optimal parameter setting should be. Automated machine learning (AutoML) seeks to simplify this process by using AI to optimize model selection. We developed the tree-based pipeline optimization tool (TPOT) as one of the first AutoML algorithms and open-source software packages. The advantage of TPOT is that it can build an entire machine learning pipeline with feature selectors, feature transformers, and classifiers all represented as directed acyclic graphs. Our current work has focused on informing TPOT model selection, evaluation, and interpretation using expert biological knowledge represented using an ontology in a graph database. We will demonstrate knowledge-guided AutoML for prescriptive AI in the context of risk prediction and drug repurposing for Alzheimer’s disease.
  • Dr. Anjana Susarla (Michigan State U); Sept 9th, 4:30pm – 5:30pm [virtual]
    • AI for Social Good: Video Analytics to Promote Health Literacy
    • Studies of health literacy in the United States, such as the National Assessment of Adult Literacy conducted in 2003, estimated that only 12% of adults had proficient health literacy skills. This talk examines how social media platforms such as YouTube widen such health literacy disparities by steering users toward questionable content.
  • Dr. Patrick Ryan (Janssen); Sept 10th, 8:45am – 9:45am
    • Building trust to enable Prescriptive AI:  Lessons from the Observational Health Data and Informatics community
    • Real-world evidence generated from electronic health records and administrative claims offer the potential to transform healthcare by enabling ‘prescriptive AI’, but challenges around data standards, methodological best practices, analytics development and clinical applications must be overcome to build trust to make this evidence actionable.  This talk will share recent experiences from OHDSI, an open science community that aims to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care.
  • Dr. Moontae Lee (U Illinois Chicago); Sept 10th, 2:45pm – 3:45pm
    • From Text to Reality: Frontiers of Large Language Models and Generative AI
    • Large Language Models (LLMs) and Generative AI (GenAI) have transformed the AI landscape. Relying on their capability to understand extensive contexts, groundbreaking innovations lie in tackling multiple tasks by a single formalism: generating contextually coherent and creatively diverse output completions given input prompts. This talk first begins with an overview of the recent advancements in Large Language Modeling, covering basic text completions to complex functions such as planning and reasoning. Next, the talk introduces a crucial application: generating codes given textual descriptions, which improves productivity and democratizes access to programming. In addition, the talk introduces LLMs as agents and judges for operating on and evaluating complex tasks. While briefly touching critical issues of Safety, Ethics, and Governance, the talk highlights ongoing and future research topics that push the frontiers of AI.

Theme Talks

  • Cancer AI: Dr. Yingqi Zhao (Fred Hutch Cancer Center); Sept 9th, 1:00pm – 1:45pm
    • Improve Fairness of Clinical Decision Rules under Data Shift with Application to Cancer Early Detection
  • Cancer AI: Dr. Ying Yuan (U Texas MD Anderson Cancer Center); Sept 9th, 1:45pm – 2:30pm
    • Frontiers of Innovation: Bayesian Adaptive Designs in Clinical Trials
    • Bayesian adaptive designs are increasingly being employed to enhance the efficiency and success rates of cancer clinical trials, gaining strong support from regulatory bodies. The FDA has initiated the Complex Innovative Design (CID) Program and the Bayesian Supplemental Analysis (BSA) Demonstration Project to encourage the incorporation of innovative adaptive designs in drug development. In this talk, I will present examples that illustrate the critical role of Bayesian adaptive designs in accelerating drug development. These examples include phase I and II trials, basket trials, synthetic controls, adaptive information borrowing, and dynamic treatment regimes.
  • Neuroscience AI: Dr. Aprinda Indahlastari Queen (UF); Sept 9th, 3:45pm – 4:30pm
    • Transcranial electrical stimulation (tES) is a promising intervention for mitigating cognitive decline and related mental health problems in aging populations. Previous research indicates that stimulation outcomes vary among individuals based on the current delivered to the brain. Person-specific, MRI-derived computational models can quantify the amount of current reaching the brain and predict tES effects more accurately. Applying machine learning to these models can identify the brain subregions most receptive to stimulation. Further, integrating AI into tES models can create personalized doses to potentially optimize stimulation outcomes, exemplifying prescriptive AI applications in neuroscience.
  • Business AI: Dr. Yiduo Shao (U Iowa); Sept 10th, 1:00pm – 1:45pm
    • Pathways to Human-AI Augmentation: How and When Employees Learn from Using AI Tools at Work
    • Augmentation-based artificial intelligence (AI) artifacts are increasingly incorporated into the workplace. The coupling of employees and AI tools, given their complementary strengths, expands and expedites employees’ access to information and affords important learning opportunities. However, existing research has yet to fully understand whether, how, and when employees could enjoy learning-based benefits from interacting with AI tools. This presentation will include two studies that address this gap. The first study is an empirical investigation of learning-based benefits and challenges experienced by call center representatives using AI tools on a daily basis. The second study is a theoretical piece that unpacks the episodic interactional dynamics between employees and AI tools in achieving mutual learning. These studies have implications for understanding AI augmentation from a learning-based perspective.
  • Industrial AI: Andy Lin (Mark III Systems – NVIDIA); Sept 10th, 1:45pm – 2:30pm
    • Building the AI Center of Excellence for the Era of LLMs and GenAI (“The Second Era”)
    • Join NVIDIA and Mark III (2024 NVIDIA Healthcare/Life Science Partner of the Year) to hear about how GenAI, LLMs, and Medical Imaging AI are reshaping research in healthcare, life sciences, and drug discovery, and how NVIDIA is helping to enable breakthroughs through advancements in accelerated computing and software tooling to empower scientists and researchers everywhere.

Panel Discussions

Event Organizers

2024 Workshop Sponsors

2024 DAISY AGENDA
2024 Speaker BIOS
2024 venue details

Participant Bios (alphabetical order by last name)

photos are included in the event brochure Full Brochure.pdf

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Saad Alam, PhD (AdventHealth)

Dr. Saad Alam is the corporate Director of Data Science and AI at AdventHealth, a leading faith-based health care system with nearly 50 hospital campuses in Florida and across the U.S. He has been with the company for nine years, starting as a Data Scientist in 2015. He holds a PhD in Economics, with a focus on health economics and industrial organization. Before joining AdventHealth, he was a professor of economics at the University of St Thomas, where he conducted research on health care quality and cost, microfinance banking, and improving access to health care for the underprivileged. He also served as the Principal Economist in the antitrust division of the Texas office of Attorney General, where he analyzed the competitive effects of hospital mergers, ACOs, and pharmaceutical cases, using economic and predictive modeling and econometric/statistical analysis.

As the corporate Director of Data Science and AI, Dr. Alam leads a team of data scientists, engineers, and analysts who develop and deploy AI/ML solutions for various domains within AdventHealth, such as finance, human resources, risk management, revenue cycle management, and operations. He is also involved in a committee of leaders responsible for designing and supervising the AI governance and ML infrastructure for the organization, ensuring the ethical, legal, and regulatory compliance of AI/ML applications. Dr. Alam’s vision is to leverage the power of data and AI to improve the health outcomes and experiences of patients, clinicians, and staff at AdventHealth.

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Adel Alrwisan, PhD (Saudi Food and Drug Authority)

Dr. Adel Alrwisan is an active researcher specializing in regulatory science, with a particular emphasis on use of real-world data in advancing drug safety and safeguarding public health. Serving as the Executive Director of the Research Department at the Saudi Food and Drug Authority (SFDA), Dr. Alrwisan led and supervised several regulatory science research initiatives that inform and shape the agency’s regulatory policies and frameworks. Dr. Alrwisan’s academic journey encompasses a Bachelor’s degree in Pharmaceutical Sciences from King Saud University, a Master’s degree in Clinical Pharmacology from the University of Aberdeen, and a Ph.D. in Pharmaceutical Sciences from the University of Florida. His doctoral research delved into the safety aspects of commonly used antibiotics, laying a strong foundation for his career at the intersection of science and regulation.

In his tenure at SFDA, Dr. Alrwisan has held pivotal roles that have significantly enhanced the organization’s regulatory capabilities. As Director of the Drug Safety & Risk Management Department, he was central to advancing post-marketing safety assessments and played a key role in establishing the National Pharmacovigilance Center. These contributions have been instrumental in bolstering drug safety monitoring and regulatory oversight within the Kingdom. Beyond his administrative roles, Dr. Alrwisan is actively engaged in the regulatory community through participation in various scientific and regulatory committees. His research interests focus on leveraging real-world data for regulatory decision-making, evaluating the effectiveness of risk minimization measures, and assessing the impact of regulatory policies on health outcomes.

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Danielle Belgrave, PhD (GSK) [virtual]

Danielle Belgrave is a Trinidadian-British computer scientist who uses statistics and machine learning to understand the progression of diseases. The core of her research focuses on probabilistic graphical modelling to understand heterogeneous phenomena using big data to understand causality mechanisms. She has extensive experience both in leading projects and teams and hands-on implementations of end-to-end solutions in statistical machine learning for healthcare. She also implements and develops a broad spectrum of supervised and unsupervised machine learning methods.

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Jiang Bian, PhD (UF)

Dr. Bian’s expertise and background serve an overarching theme: data science with heterogeneous data, information and knowledge resources. He currently serves as the Chief Data Scientist and Chief Research Information Officer for UF Health, Division Chief of Biomedical Informatics in Health Outcomes & Biomedical Informatics, Director of the Biomedical Informatics Program for the UF Clinical and Translational Science Institute (CTSI; https://www.ctsi.ufl.edu/about/ctsi-programs/biomedical-informatics/), Director of Cancer Informatics Shared Resource (and its eHealth Core program, http://bit.ly/36IBw5s, jointly supported by the UF CTSI), and the Chief Data Scientist for the OneFlorida+ Clinical Research Consortium (https://onefloridaconsortium.org/).

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Alan Brookhart, PhD (Duke)

M. Alan Brookhart, Ph.D. is a Professor in the Department of Population Health Sciences at Duke University. He is also an Honorary Professor at the University of Aarhus, Denmark and an Adjunct Professor at UNC Chapel Hill. Alan was on faculty at Harvard Medical School and UNC Chapel Hill prior to joining the faculty at Duke.

Alan has spent his career developing and applying epidemiologic and statistical methods for learning from real-world data. Substantively, his research has focused on understanding the effects of treatments and policies in complex and vulnerable patient populations, such as those with end-stage renal disease. In addition to his academic work, Alan co-founded two start-up companies: RxAnte, Inc, which uses predictive analytics to target healthcare quality improvement interventions to high-risk patients, and NoviSci, Inc, a healthcare data sciences company that builds computational and visualization tools to facilitate learning from real-world data.

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Emily Campion, PhD (U Iowa)

Emily D. Campion, PhD, is an Assistant Professor of Management and Entrepreneurship in the Tippie College of Business at the University of Iowa. Her research focuses largely on staffing procedures, how to leverage machine learning and natural language processing to improve these systems, and ways to mitigate employment discrimination. Her work has appeared in Journal of Applied Psychology, Personnel Psychology, Organization Science, Journal of Management, Human Resource Management, Leadership Quarterly, and Journal of Vocational Behavior. Emily is currently serving as an editorial board member for Journal of Applied Psychology and Personnel Psychology, and she recently co-edited a special issue on machine learning in selection for Personnel Psychology. Prior to academia, she was a daily reporter in Indiana and an AmeriCorps member in Washington, D. C. She earned her B.A. in Journalism from Indiana University and her Ph.D. in Organization and Human Resources from the University at Buffalo, The State University of New York.

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Daisy Chang, PhD (Michigan State U) [virtual]

Chu-Hsiang (Daisy) Chang is a professor at the Department of Psychology of Michigan State University. She received her Ph.D. in I-O psychology from the University of Akron. Her research interests include occupational health and safety and technology in the workplace. She was an associate editor for Applied Psychology: An International Review, Journal of Organizational Behavior, and Journal of Applied Psychology. She was the director of the Science of Organizations program at the National Science Foundation (NSF) in 2016-2018, and she continues to serve on grant review panels at various federal funding agencies.

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Serena Guo, PhD (UF)

Jingchuan (Serena) Guo is Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida (UF) College of Pharmacy. She received her MD from Peking University in Beijing, China and her PhD in Epidemiology from the University of Pittsburgh. After a one-year postdoctoral fellowship in the Center for Pharmaceutical Policy and Prescribing also at the University of Pittsburgh, in December 2020, Dr. Serena Guo joined the faculty of UF.

Dr. Serena Guo conducts research in pharmacoepidemiology and pharmacoinformatics, primarily focused on cardiometabolic diseases and neurodegenerative conditions (e.g., dementia) with the goals of promoting precision health and health equity. Her research draws on large real-world data (e.g., electronic health records and insurance claims data) and advanced analytics (e.g., AI/machine learning, causal-principled modeling, and geospatial analyses) to: 1) assess comparative effectiveness/safety and heterogeneous treatment effects (HTEs) of treatments and interventions, and 2) develop individualized and intelligent social risk management tools to be integrated into clinical care. She has published over 90 peer-reviewed manuscripts. Several articles have been published in top-tier journals, including Diabetes Care, Lancet and BMJ, and featured in media outlets, including Washington Post, NPR, and CNN. Her research programs and collaborations have been funded by the NIH, Veterans Affairs, CDC, FDA and the PhRMA Foundation.

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Yi Guo, PhD (UF)

Dr. Yi Guo is an Associate Professor for the Dept. of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida. He has a multi-disciplinary background in the analysis of real-world data such as those from electronic health records (EHRs) and administrative claims, experimental and observational study design, predictive modeling (e.g., statistical and machine learning), causal modeling, and analysis of patient-reported outcomes. Together, his areas of expertise serve an overarching research theme: generating real-world evidence to support healthcare decision-making for disease prevention and control. Under this overarching theme, his research areas and expertise can be divided into three key methodologies: (1) EHR-based phenotyping and risk stratification – the identification of sub-populations with certain conditions or at higher risk for diseases, (2) Causal modeling and inference – the examination of causal relationships and pathways in clinical research, particularly treatment studies, and (3) Patient-reported outcomes (PROs) in clinical and public health applications – the development, validation, assessment, analysis, and reporting of PROs among various populations, especially vulnerable populations.

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Moontae Lee, PhD (U Illinois Chicago)

Moontae Lee is an assistant professor of Information and Decision Sciences at the University of Illinois Chicago. Concurrently, he is the Director of the Advanced Machine Learning Laboratory at LG AI Research. His journey to Large Language Models started when he was invited to Microsoft Research Redmond as a visiting scholar on 2019. Then he continued to work multiple years as a consulting professor of the Deep Learning Group for the ambitious Universal Language Modeling projects. Moontae received his PhD in Computer Science from Cornell University working on representation learning and natural language understanding. He received his MS in Computer Science from Stanford University specializing in Artificial Intelligence. He holds a BS in Computer Science, BS in Mathematics, and BA in Psychology from Sogang University. Moontae has served as an area chair/senior program committee of NeurIPS, ICML, ICLR, ACL, NAACL, EMNLP, AAAI, AISTATS, and CVPR. Beyond the machine learning communities, his work was recognized to Operations Research and Management Information Systems by winning the best paper award at INFORMS 2017. His research in Computational Social Science won the research award in Amazon.

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Jenny Li, PhD (NSF-OAC)

Dr. Juan (Jenny) Li is an IPA program director of the Office of Advanced Cyberinfrastructure (OAC) under the Directorate of Computer and Information Science and Engineering (CISE). She manages the Learning and Workforce Development programs and the classroom part of the NAIRR program. She is also a professor of computer science at Kean University of New Jersey. Her research area is artificial intelligence (AI) and its applications in various fields, such as software engineering, cybersecurity, biotechnology, and education. Before joining academia, she led research projects in industrial research labs. She has published over 130 peer-reviewed articles and holds 20 patents. She received her Ph.D. from the University of Waterloo, Canada.

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Yongqiu Li (UF PhD student)

Yongqiu Li is a current graduate student in the Biomedical Informatics program at the University of Florida. Her publications can be found on Google Scholar, and her research on the “Impact of Cancer Screening on Advanced-Stage Diagnosis and Mortality among ADRD Patients” was recently presented orally at the HOBI Education Day (see feature article below).

HOBI Education Day showcases student success

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Andy Lin, VP Strategy/CTO (Mark III Systems – NVIDIA)

Andy currently serves as CTO/VP Strategy at Mark III Systems, an award-winning NVIDIA Elite Partner and 2024 NVIDIA Healthcare/Life Sciences Partner of the Year, with a focus on working with enterprises, research institutions, and universities on building out and co-piloting their AI, Modern HPC, and Digital Twin Centers of Excellence. Andy leads a unique, cross-functional team of data scientists, systems engineers, devs, DevOps/MLOps Engineers, 3D artists, and subject-matter experts, who work tightly together with researchers, scientists, data scientists, ML engineers, and technology operations teams to accelerate and move research and innovation forward everyday.

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Qi Liu, PhD, MStat (FDA-CDER) [virtual]

Qi Liu, Ph.D., M.Stat., FCP, is the Associate Director for Innovation & Partnership in the Office of Clinical Pharmacology (OCP)/ Office of Translational Sciences, CDER, FDA. She leads OCP’s innovative initiatives through strategic partnership. She has helped develop OCP’s portfolio on artificial intelligence/machine learning, real world evidence and digital health technologies, collaborating with internal and external experts. She helped establish the OCP Innovative Data Analytics Program and AI/ML review team, and currently serves as the lead. She had the experience leading OCP’s Physiological Based Pharmacokinetic Modeling and Simulation Oversight Board and co-leading Biologics Oversight Board. She is also on the executive board of CDER’s Quantitative Medicine Center of Excellence. She was a co-lead initiating the Real-Time Oncology Review and Assessment Aid Pilot Programs. During her career at the FDA, she also contributed to over 200 NDA/sNDA reviews, 20 BLA/sBLA reviews, and numerous IND reviews to support drug development. She worked on working groups for FDA guidance documents and Manual of Policies & Procedures development. She is an Associate Editor of Clinical Translational Science and on the editorial board of five scientific journals. Before joining FDA, Dr. Liu was a senior pharmacokineticist at Merck & Co. Inc. She obtained her Ph.D. degree in Pharmaceutics and a concurrent Master’s degree in Statistics from the University of Florida in 2004. In addition, she has a Master’s degree in Pharmaceutics and a Bachelors’ degree in Clinical Pharmacy from West China University of Medical Sciences.

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Songqi Liu, PhD (NSF-SES)

Dr. Songqi Liu is a professor of management at the Robinson College of Business, Georgia State University and a rotating Program Director at the U.S. National Science Foundation (NSF). His research has appeared in premier management and psychology journals, including Academy of Management Journal, Journal of Applied Psychology, and Psychological Bulletin, and covered areas including newcomer social networks and innovation, job search and employment, adaptive and maladaptive coping of individuals and teams, and work-family interface. His work has been widely recognized, as exemplified by two grant awards from NSF and various additional awards including the Society for Industrial and Organizational Psychology (SIOP) William A. Owens Scholarly Achievement Award, SIOP Small Grant Award, and Personnel Psychology Best Article Award. Dr. Liu received his doctoral degree in Industrial and Organizational Psychology from the University of Maryland, College Park. He is currently serving as a consulting editor for the journal Work, Aging and Retirement, co-editing a special issue of Personnel Psychology on employee social networks and networking, and serving on multiple journal editorial boards.

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Jason Moore, PhD (Cedars-Sinai)

Jason Moore is Chair of the Department of Computational Biomedicine at Cedars-Sinai Medical Center in Los Angeles, CA. He came to Cedars-Sinai in 2021 from the University of Pennsylvania where he was the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also served as Senior Associate Dean for Informatics and Chief of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics. He came to Penn in 2015 from Dartmouth where he was Director of the Institute for Quantitative Biomedical Sciences. While at Dartmouth he founded their bioinformatics core facility and built the university’s first campus-wide high-performance computer system. Prior to Dartmouth he served as Director of the Advanced Computing Center for Research and Education at Vanderbilt University where he launched their first high-performance computer. He has a Ph.D. in Human Genetics and an M.S. in Applied Statistics from the University of Michigan. He leads an active NIH-funded research program focused on the development of artificial intelligence and machine learning algorithms for the analysis of complex biomedical data. One application area is understanding how demographic, genetic, physiologic, and environmental factors interact to influence risk of common diseases such as cancer, cardiovascular disease, and neuropsychiatric diseases. He is the author of the widely used multifactor dimensionality reduction (MDR) method and software that is the leading resource for discovering genetic interactions. His work has been communicated in more than 600 peer-reviewed paper, book chapters, and editorials. In addition to an active research program, Dr. Moore is committed to undergraduate and graduate education. He has trained more than 100 students and postdocs and has founded several interdisciplinary training programs. He is an elected fellow of the American Association for the Advancement of Science (AAAS), an elected fellow of the American College of Medical Informatics (ACMI), an elected fellow of the International Academy for Health Sciences Informatics (IAHSCI), an elected fellow of the American Statistical Association (ASA), an elected member of the International Statistics Institute (ISI), and was selected as a Kavli fellow of the National Academy of Sciences. He is currently Editor-in-Chief of the open-access journal BioData Mining.

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Mattia Prosperi, MEng, PhD (UF)

In his administrative role as Associate Dean for AI and Innovation at University of Florida, College of Public Health and Health Professions, Dr. Prosperi’s mission is to expand artificial intelligence infrastructure, training, research and expertise capacity in public health and health professions. As Professor in the Dept. of Epidemiology, he pursues AI solutions for bettering individual and population health, and to go beyond. By capitalizing on his computer science engineering background together with epidemiology, he leads his team to develop original algorithms, models, and usable applications. They leverage machine learning with a critical eye on causality, working on multi-domain, multi-layer big data—socioeconomic, ecological, clinical, and pathogen genomics. He is committed to a multi-cultural, equal opportunity educational and professional environment. He fosters Master’s and PhD students to create a workforce that will excel in the next-generation data science. He is an organizer of the “International Bioinformatics Workshop on Virus Evolution and Molecular Epidemiology”, editor of “BMC Medical Informatics and Decision Making” and of “Global Health Research and Policy”. He is also a member of the Association for Computing Machinery (ACM), and Fellow of the American Medical Informatics Association (FAMIA).

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Aprinda Indahlastari Queen, PhD (UF)

Dr. Aprinda Indahlastari Queen is an Assistant Professor in the Department of Clinical and Health Psychology, with a background in biomedical engineering. With extensive experience in computational neuroscience research, Dr. Queen specializes in using the finite element method (FEM), neuroimaging, machine learning, and image processing tools to predict the effects of biomedical devices, such as the neuromodulation therapy called transcranial electrical stimulation (tES). Her prior research includes publishing the largest tES computational models in older adults. Her current work aims to create a robust platform for predicting the efficacy of non-invasive neuromodulation modalities at the intersection of neuroscience and aging.

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Ghulam Rasool, PhD (Moffitt Cancer Center)

Ghulam Rasool, PhD, is an Assistant Member in the Department of Machine Learning with a secondary clinical appointment in the Department of Neuro-Oncology at the H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL. He holds an Assistant Professor position in the Department of Oncological Sciences and a courtesy faculty appointment in the Department of Electrical Engineering at the University of South Florida. Before joining Moffitt, he was an Assistant Professor in the Department of Electrical and Computer Engineering at Rowan University. His current research focuses on building trustworthy multimodal computer vision and natural language processing models for various applications across the cancer continuum, from screening to risk assessment and outcome prediction. His research efforts are funded by the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Moffitt Cancer Center. He received the 2023 Junior Researcher Award in Quantitative Sciences at Moffitt Cancer Center.

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Leo Russo, PhD (Pfizer)

Leo J. Russo, PhD, has been a practicing epidemiologist for nearly 30 years, with the last 23 in the pharmaceutical industry. He has spent time in big (GSK, J&J, Pfizer) and mid-size (Shire) pharma and has brought the best practices from each stop into his current role. Leo leads Pfizer’s Global Medical Epidemiology group within the Worldwide Medical and Safety organization. His academic training began in the field of statistics, in which he holds a Bachelor of Science degree from The Ohio State University. Leo went on to complete his M.S. and PhD degrees in epidemiology at Case Western Reserve School of Medicine. He believes epidemiology is a highly creative field and a catalyst for innovation. It is a mindset and perspective that can be leveraged across many areas such as drug candidate selection, drug safety, machine learning, digital health, clinical trial diversity, health equity, and health systems quality.

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Patrick Ryan, PhD (Janssen)

Patrick Ryan is Vice President, Observational Health Data Analytics at Janssen Research and Development, where he is leading efforts to develop and apply analysis methods to better understand the real-world effects of medical products. He is an original collaborator in Observational Health Data Sciences and Informatics (OHDSI), a multi-stakeholder, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics. He served as a principal investigator of the Observational Medical Outcomes Partnership (OMOP), a public-private partnership chaired by the Food and Drug Administration, where he led methodological research to assess the appropriate use of observational health care data to identify and evaluate drug safety issues.

Patrick is also Assistant Professor, Adjunct at Columbia University and collaborator in Observational Health Data Sciences and Informatics (OHDSI), a multi-stakeholder, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics. He received his undergraduate degrees in Computer Science and Operations Research at Cornell University, his Master of Engineering in Operations Research and Industrial Engineering at Cornell, and his PhD in Pharmaceutical Outcomes and Policy from University of North Carolina at Chapel Hill. Patrick has worked in various positions within the pharmaceutical industry at Pfizer and GlaxoSmithKline, and also in academia at the University of Arizona Arthritis Center.

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Marsha Samson, PhD, MPH, MSHSA (FDA-CDER) [virtual]

Marsha Samson manages key initiatives in the FDA Center for Drug Evaluation and Research (CDER) related to the use of artificial intelligence (AI) in the development of drugs. She applies her broad research interests and experiences to a variety of AI-related topics, including considerations for human-led governance, accountability, and transparency; quality, reliability, and representativeness of data; and model development, performance, monitoring, and validation. Additionally, she routinely conducts rigorous reviews of the peer-reviewed literature to support CDER initiatives (e.g., advancing innovative approaches, diversity in clinical trials) and applies her scientific expertise to respond to urgent requests. Previously, Dr. Samson worked as a Career Epidemiology Field Officer (CEFO) at the CDC, where she served as the subject matter expert assigned to the District of Columbia’s Health Department. Dr. Samson earned her MPH/MSHSA from Barry University and her PhD in epidemiology from the University of South Carolina. She completed her postdoctoral training at both Georgetown University in cancer biology and at CDC as an Epidemic Intelligence Service (EIS) officer in applied epidemiology. Dr. Samson is a Lieutenant in the U.S. Public Health Service and received her Regulatory Affairs Certification (RAC) for medical devices and pharmaceutical drugs to further support CDER and the Office of Medical Policy.

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Yiduo Shao, PhD (U Iowa)

Yiduo Shao is an Assistant Professor of Management and Entrepreneurship at the Tippie College of Business. Prior to joining Tippie, she earned her Ph.D. in Management from the University of Florida and B.S. in Psychology from Peking University. Her research interests include workforce aging and age diversity, virtual and remote work, and employee-AI interactions. Her research has been published in leading management outlets, such as Journal of Applied Psychology, Personnel Psychology, and Journal of Management.

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Chelsea Song, PhD (Indiana U)

Dr. Chelsea Song is an Assistant Professor of Organizational Behavior and Human Resource Management at the Kelley School of Business, Indiana University. Her research aims to improve the fairness and effectiveness of recruitment and personnel selection practices. They focus on workplace diversity, individual differences, person-environment fit, and big data and machine learning methods. Dr. Song’s research has been published in prestigious journals such as the Journal of Applied Psychology, Personnel Psychology, and Journal of Personality and Social Psychology, among others. Her work has been featured in popular outlets such as Forbes. Dr. Song’s works have been recognized with the AOM Research Methods Division Early Career Award and the HumRRO Meredith P. Crawford Fellowship in I-O Psychology. Her research has received support from the National Institute of Health and the Department of Defense. Dr. Song currently serves on the editorial boards of the Journal of Applied Psychology and Organizational Research Methods.

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Tianjun Sun, PhD (Rice U)

Dr. Tianjun Sun is an Assistant Professor of Industrial-Organizational Psychology and Quantitative Methods in the Department of Psychological Sciences at Rice University School of Social Sciences. Before joining Rice, Dr. Sun was an Assistant Professor at Kansas State University for three years. Dr. Sun received her Ph.D. in Psychology from University of Illinois Urbana-Champaign in 2021. Prior to the doctorate, she also obtained from Illinois her Bachelor’s and Master’s degrees in both Psychology and Statistics. Dr. Sun’s research primarily focuses on personnel selection, individual differences, psychometrics, and using advanced technology and quantitative methods to enhance staffing decisions, improve candidate/employee experiences, and solve organizational problems. Dr. Sun is actively publishing in reputable and high-impact journal outlets, and her projects have received support from the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Society for Industrial and Organizational Psychology (SIOP) Foundation. Dr. Sun was recognized as a Rising Start by the Associate for Psychological Science (APS) and has received a series of awards from SIOP, the American Psychological Association (APA), and the Academy of Management (AOM). Dr. Sun served as an Editorial Fellow 2023-2024 and now is a Contributing Editor at Journal of Applied Psychology and is on the Editorial Boards of Organizational Research Methods, Human Performance, and Journal of Business and Psychology. On the applied side, Dr. Sun has broad experience working in consulting, testing, and tech industries, as well as in areas of people analytics, learning and assessment, and talent management.

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Anjana Susarla, PhD (Michigan State U)

Anjana Susarla is the Omura-Saxena Professor of Responsible AI at the Eli Broad College of Business at Michigan State University. She earned an undergraduate degree in Mechanical Engineering from the Indian Institute of Technology, Chennai; a graduate degree in Business Administration from the Indian Institute of Management, Calcutta; and Ph.D. in Information Systems from the University of Texas at Austin. Her work has appeared in several academic journals and peer-reviewed conferences such as Academy of Management Conference, Conference on Knowledge Discovery and Data Mining, Information Systems Research, International Conference in Information Systems, International Conference in Learning Representations, Journal of Biomedical Informatics, Journal of Management Information Systems, Management Science and MIS Quarterly. She has served on and serves on the editorial boards of Information Systems Research and MIS Quarterly. She has been interviewed in, had her research quoted and op-eds published in several media outlets such as the Associated Press, BBC, Fast Company, Fox News, National Public Radio, NBC, Newsweek and Washington Post. Her research has been funded by prestigious organizations such as the National Institute of Health (NIH).

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Jeffery Talbert, PhD (U Kentucky)

Jeffery Talbert, PhD is Professor and Division Chief for Biomedical Informatics at the University of Kentucky College of Medicine. He serves as Director of the Institute for Biomedical Informatics and Associate Director of the UK Center for Clinical and Translational Science. His research specialization is public health informatics focused on the intersection of evidence-based policy and health care outcomes. Current research projects focus on big data approaches to improve substance use disorder outcomes and Medicaid policy interventions.

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Mo Wang, PhD (UF)

Dr. Mo Wang is a University Distinguished Professor and the Lanzillotti-McKethan Eminent Scholar Chair at the Warrington College of Business at University of Florida. He is also the Associate Dean for Research and Strategic Initiatives, Department Chair of the Management Department, and the Director of Human Resource Research Center at University of Florida.

Mo’s research is well recognized and regarded by major federal funding agencies. His research program has been supported with more than $5M from NIH, NSF, CDC, and various other research foundations and agencies. Mo is an elected Foreign Member of Academia Europaea (M.A.E) and a Fellow of AOM, APA, APS, and SIOP. He was the Editor of The Oxford Handbook of Retirement and an Associate Editor for Journal of Applied Psychology (2010-2020) and currently serves as the Editor-in-Chief for Work, Aging and Retirement. He was the President of Society for Occupational Health Psychology (2014-2015) and the Director for the Science of Organizations Program at National Science Foundation (2014-2016). He currently serves the Presidential Track for Society for Industrial-Organizational Psychology (2021-2024).

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Almut Winterstein, PhD (UF)

Almut Winterstein is Distinguished Professor and the Dr. Robert and Barbara Crisafi Chair for Medication Safety in the Department of Pharmaceutical Outcomes and Policy, Affiliate Distinguished Professor in Epidemiology, and the founding Director of the Center for Drug Evaluation and Safety at the University of Florida. Since 2019, she also serves as director of the Consortium for Medical Marijuana Clinical Outcomes Research, a state-funded consortium of 9 universities in Florida. Dr. Winterstein’s research interests center on the post-marketing evaluation of medications in pediatrics and pregnancy, infectious disease and psychiatry and the evaluation of policy surrounding medication use using real-world data. As expert in drug safety, she has chaired the Food and Drug Administration’s Drug Safety and Risk Management Advisory Committee from 2012-2018. Recognizing her contributions in pharmacoepidemiology, Dr. Winterstein was inducted as a fellow of the International Society of Pharmacoepidemiology in 2013 and served as president of the society from 2019-2020. In 2022, she was inducted in the Academy of Science, Engineering and Medicine in Florida. Dr. Winterstein received her pharmacy degree from Friedrich Wilhelm University in Bonn, Germany and her PhD in Pharmacoepidemiology from Charité, Humboldt University in Berlin.

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Ying Yuan, PhD (U Texas MD Anderson Cancer Center)

Ying Yuan is the Bettyann Asche Murray Distinguished Professor and Deputy Chair of the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. Dr. Yuan is internationally renowned for his pioneering research 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 adopted by medical research institutes and pharmaceutical companies. Among these, the BOIN design, developed by Dr. Yuan’s team, is a groundbreaking oncology dose-finding method recognized by the FDA as a fit-for-purpose drug development tool. Dr. Yuan is also an elected Fellow of the American Statistical Association and the lead author of two seminal 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.

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Yingqi Zhao, PhD (Fred Hutchinson Cancer Center)

Yingqi Zhao received her PhD in biostatistics from the University of North Carolina, Chapel Hill in 2012. She is currently an Associate Professor at Fred Hutchinson Cancer Research Center. Her research focus includes methodologies for personalized medicine, dynamic treatment regimes, observational studies and machine learning. Specific applications of her work include cancer treatment and prevention, health care delivery for complex type II diabetes patients and childhood obesity surveillance. Her work in personalized medicine is particularly notable for these applications, which has been the basis for much subsequent work on developing biomarker-based treatment rules. With the goal of improving patient outcomes, Dr. Yingqi Zhao’s work focuses on developing novel statistical and machine learning methods for personalized medicine, dynamic treatment regimes, disease screening and surveillance, clinical trial design, and electronic health records. Dr. Zhao collaborates with the Fred Hutch–based Statistics and Data Management Center for the SWOG Cancer Research Network and Early Detection Research Network.

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Li Zhou, MD, PhD (Harvard)

Li Zhou, MD, PhD, FACMI, FIAHSI, FAMIA, is a Professor of Medicine at Harvard Medical School and a Lead Investigator at the Brigham and Women’s Hospital. She earned her PhD in Biomedical Informatics from Columbia University. She currently directs the MTERMS lab at Mass General Brigham. Her research has focused on allergy information documentation, management, and drug-allergy decision support in the electronic health record systems.  Throughout her career, her research has also focused on natural language processing (NLP), machine learning, knowledge representation and clinical decision support. She has developed technological innovations in NLP and machine learning that have enabled rapid processing and data mining of clinical documentation in support of decision making and patient safety. She is an Associate Editor for the International Journal of Medical Informatics (IJMI), the Journal of General Internal Medicine (JGIM), and BMJ Health & Care Informatics. She serves as a Board Director of the American Medical Informatics Association (AMIA). She is an elected fellow of the American College of Medical Informatics (ACMI) and of the International Academy of Health Sciences Informatics (IAHSI). 

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Hao Zhu, PhD (FDA-CDER) [virtual]

Dr. Hao Zhu is the director of the Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center of Drug Evaluation and Research, U.S. Food and Drug Administration. Dr. Zhu received his Ph.D. in pharmaceutical sciences and Master in statistics from the University of Florida. He started his career in modeling and simulation teams in Johnson & Johnson and Bristol-Myers-Squibb. He joined FDA as a pharmacometrics reviewer more than 17 years ago. Dr. Zhu has been a clinical pharmacology team leader for more than 6 years and a QT-IRT scientific lead for 2 years. Then he became the deputy director at the Division of Pharmacometrics. His division reviews the pharmacometrics related submissions and supports pharmacometrics-related policy development.

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2024 Workshop Sponsors

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