Sessions For: School of Public Health
3 sessions available from January 14, 2026 to March 11, 2026
Unlock your potential with the Student Success programming series at the School of Public Health!
Designed for all students (undergraduate through PhD), this engaging series covers essential academic and wellbeing topics tailored to help you flourish inside and outside the classroom. Join us for interactive sessions on time management strategies, understanding group dynamics, recognizing and overcoming burnout, navigating major decisions with discernment, embracing your authentic strengths, and fostering overall well-being. Whether you're looking to boost your academic performance or enhance your personal growth, our workshops provide practical tools, meaningful insights, and a supportive community to help you succeed during your journey at SPH. All students are welcome!
Designed for all students (undergraduate through PhD), this engaging series covers essential academic and wellbeing topics tailored to help you flourish inside and outside the classroom. Join us for interactive sessions on time management strategies, understanding group dynamics, recognizing and overcoming burnout, navigating major decisions with discernment, embracing your authentic strengths, and fostering overall well-being. Whether you're looking to boost your academic performance or enhance your personal growth, our workshops provide practical tools, meaningful insights, and a supportive community to help you succeed during your journey at SPH. All students are welcome!
4 sessions available from January 14, 2026 to March 16, 2026
Come join Lauren Czarnowczan, Student Programs Specialist, from the University of Michigan School of Public Health Practice Team in collaboration with the IDEAS for Health Equity Team, and Patty Krause, Community Health Analyst, from the Washtenaw County Health Department for a tour and conversation to learn more about governmental public health and how it works in the community!
There will be four different opportunities to join this winter semester, with additional opportunities to learn more about the experience of staff or former intern. See below for each indicated opportunity.
Please select only one tour date as seats are limited.
Wednesday, January 14 from 2:30 - 4PM - Mini outbreak activity Friday, January 30 from 2:00 - 3:30PM - Internship sharingFriday, February 20 from 11:30AM - 1PM - WCHD staff info sharing Monday, March 16 from 11:30AM - 1PM - WCHD staff info sharing
This is a great opportunity to learn more about local public health and to network!
Space is limited to 10 seats per tour. You will need to provide your own transportation to the health department located at 555 Towner St., Ypsilanti, MI 48198. The health department is accessible with TheRide bus, with a stop located directly outside. Parking is also free.
There will be four different opportunities to join this winter semester, with additional opportunities to learn more about the experience of staff or former intern. See below for each indicated opportunity.
Please select only one tour date as seats are limited.
Wednesday, January 14 from 2:30 - 4PM - Mini outbreak activity Friday, January 30 from 2:00 - 3:30PM - Internship sharingFriday, February 20 from 11:30AM - 1PM - WCHD staff info sharing Monday, March 16 from 11:30AM - 1PM - WCHD staff info sharing
This is a great opportunity to learn more about local public health and to network!
Space is limited to 10 seats per tour. You will need to provide your own transportation to the health department located at 555 Towner St., Ypsilanti, MI 48198. The health department is accessible with TheRide bus, with a stop located directly outside. Parking is also free.
1 session on January 22, 2026
The University of Michigan Department of Biostatistics is pleased to host Tianxi Cai, PhD (Harvard University), recipient of the 2025 Jeremy Taylor Outstanding Research Mentor Award, for a featured academic seminar on Thursday, January 22 at 3:30 p.m.
Dr. Cai is an internationally recognized leader in statistical learning, risk prediction, and the integration of electronic health records with genomic and clinical data. Her lecture will draw on her pioneering work in translational data science and precision medicine, reflecting both her methodological impact and her deep commitment to mentoring the next generation of statistical scientists.
A reception will follow the seminar. The reception is open only to those who attend the lecture.
Toward Durable AI in Healthcare: Generalizable Learning from Imperfect EHR Data
Electronic Health Record (EHR) data offers a promising foundation for real-world evidence, yet its utility is often severely limited by the reality of fragmented, imperfect data and significant heterogeneity across health systems. These inherent data flaws create major bottlenecks in generating evidence efficiently, often resulting in fragile models that are highly susceptible to data shift and rapid aging. Consequently, the challenge lies not just in accessing data, but in efficiently transforming these messy, disparate sources into reliable, enduring AI solutions.
This presentation outlines a comprehensive strategy to overcome these limitations and derive robust clinical insights from imperfect data. We will discuss how representation learning can address data sparsity and fragmentation by extracting stable latent features from incomplete patient histories. To tackle system heterogeneity and ensure model longevity, we introduce robust transfer learning frameworks designed to immunize algorithms against distributional shifts. Furthermore, we demonstrate how leveraging knowledge networks can bridge gaps in fragmented data by grounding models in broader biomedical context. Complementing these structural approaches, we touch upon the use of Large Language Models (LLMs) to identify clinical outcomes not directly available in structured fields, solving the problem of unobserved endpoints. By integrating these diverse methodologies, we aim to establish a blueprint for efficiently building AI ecosystems that remain reliable and durable despite the complexities of real-world healthcare data.
Dr. Cai is an internationally recognized leader in statistical learning, risk prediction, and the integration of electronic health records with genomic and clinical data. Her lecture will draw on her pioneering work in translational data science and precision medicine, reflecting both her methodological impact and her deep commitment to mentoring the next generation of statistical scientists.
A reception will follow the seminar. The reception is open only to those who attend the lecture.
Toward Durable AI in Healthcare: Generalizable Learning from Imperfect EHR Data
Electronic Health Record (EHR) data offers a promising foundation for real-world evidence, yet its utility is often severely limited by the reality of fragmented, imperfect data and significant heterogeneity across health systems. These inherent data flaws create major bottlenecks in generating evidence efficiently, often resulting in fragile models that are highly susceptible to data shift and rapid aging. Consequently, the challenge lies not just in accessing data, but in efficiently transforming these messy, disparate sources into reliable, enduring AI solutions.
This presentation outlines a comprehensive strategy to overcome these limitations and derive robust clinical insights from imperfect data. We will discuss how representation learning can address data sparsity and fragmentation by extracting stable latent features from incomplete patient histories. To tackle system heterogeneity and ensure model longevity, we introduce robust transfer learning frameworks designed to immunize algorithms against distributional shifts. Furthermore, we demonstrate how leveraging knowledge networks can bridge gaps in fragmented data by grounding models in broader biomedical context. Complementing these structural approaches, we touch upon the use of Large Language Models (LLMs) to identify clinical outcomes not directly available in structured fields, solving the problem of unobserved endpoints. By integrating these diverse methodologies, we aim to establish a blueprint for efficiently building AI ecosystems that remain reliable and durable despite the complexities of real-world healthcare data.
2 sessions available from January 28, 2026 to February 11, 2026
A series of workshops, panels, and presentations to help the Michigan Public Health community grow in their leadership skills and abilities.