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What shapes a career in public health? Join A. David Paltiel for “Where Did I Go Right?”, a personal look at his path from management consulting to public health research. Known for using mathematics and economics to inform health policy, Paltiel brings the person behind the models to this Journey Lecture.

Abstract: Though hospitals are rapidly adopting AI tools, this adoption has not consistently translated into better outcomes, raising a question: what are these tools for, and how do they change care? This talk argues that real-world impact hinges on aligning the design and evaluation of clinical decision support with intended use. In the first part (“the what”), I discuss how focusing on clinical goals and decision support can reorient development toward downstream objectives (e.g., reducing sepsis mortality), rather than predictive metrics alone. In the second part (“the how”), I show how model performance is often shaped by human interaction and clinical workflow, and why development and evaluation must account for how clinicians interpret and act on predictions to understand when models help.

About the speaker

Jenna Wiens, PhD, is Professor of Computer Science and Engineering at the University of Michigan, where she leads the Machine Learning for Data-Driven Decisions research group. She works with clinicians to develop machine learning methods that support clinical decision-making.

Join Jenna Wiens, PhD, Professor of Computer Science and Engineering at the University of Michigan, for a conversation about building a career at the intersection of machine learning and healthcare. Dr. Wiens leads the Machine Learning for Data-Driven Decisions research group, working with clinicians to develop methods that support clinical decision-making. The session will begin with reflections on her career journey, followed by a moderated conversation and audience Q&A. Undergraduate and graduate students, postdocs, and early-career researchers in data science, computer science, statistics, and related fields are encouraged to attend and bring their questions.

This session will introduce MarketScan, a large-scale healthcare claims database available to Yale researchers, and explore how it can be used to support health and clinical research. Participants will learn about the populations and data available in MarketScan, see examples of research applications, and gain a practical understanding of how to request and access the data at Yale.

Topics will include

• Overview of MarketScan and its applications in healthcare research
• Available populations, data types, and key variables
• Examples of research questions and studies using claims data
• How to request MarketScan access through YBIC DataMed
• Accessing and navigating Yale’s secure data environment
• Available resources and research support

About the speaker

Yujia Zhou is a Data Analyst at Yale University supporting biomedical and clinical research using electronic health records, healthcare claims, and other large-scale research databases. She holds an M.S. in Biomedical Informatics and an MBBS, bringing together clinical training and expertise in data science.

Her work includes cohort development, data harmonization, cloud computing, and research support through DataMed. She has experience working with MarketScan, Epic, OMOP, and other real-world healthcare data sources.

Join Gregg Gonsalves, Lee Kennedy-Shaffer, and Bhramar Mukherjee for a conversation about the political choices underlying statistical research and their implications for public health. Explore how priorities shape the questions researchers ask, whose experiences are captured in data, and how evidence is interpreted and used in policy, with attention to the relationship between statistical rigor, social responsibility, and health equity.

How can we use agentic AI tools to improve the quality and efficiency of research work while keeping the researcher in control? In this talk I’ll present a case study of how I use agentic tools for my research and how to benefit from these tools without sacrificing ownership and understanding of the work.

About the speaker

I’m an Assistant Professor at the Danish Centre for Health Economics (DaCHE) and a Visiting Fellow at the Danish Institute for Advanced Study (DIAS) at the University of Southern Denmark, where I study the complex relationships between population health, healthcare expenditures, and disease burden.

My research uses large-scale health data and quantitative methods to investigate real-world health policy challenges and to understand how we can better allocate resources and improve population outcomes. Before my PhD, I spent four years at the Institute for Health Metrics and Evaluation (IHME) as a Research Scientist, contributing to burden of disease and health spending research that informs health policy worldwide.

Join Melinda Irwin for “From Personal Loss to Scientific Impact,” a Journey Lecture exploring the personal side of a career in public health. A leader in cancer prevention and survivorship research, Irwin studies how exercise and nutrition can improve outcomes for people with cancer. Hear the story behind the science.

Melinda Irwin will be introduced by her trainee, Leah S. Puklin, PhD, MPH, a Postdoctoral Research Fellow in the Department of Chronic Disease Epidemiology at the Yale School of Public Health.

Chocolate chip cookies, Dr. Irwin’s favorite treat, will be served.