See the Campus Event Calendar for details about upcoming seminars
Day and Time: Fridays from noon to 1 pm central time unless otherwise noted.
Location: Morgridge Hall Seminar Room – 7560 unless otherwise noted.
Zoom: https://uwmadison.zoom.us/j/96633729112
Passcode: 621125
Further details should be available about a week before the seminar.
Upcoming Seminars:
September 25:
Speaker: Evgeny Shlevkov, Lieber Institute for Brain Development
Title: From Genetics To Therapeutics: Mapping Druggable Mechanisms in Brain Disorders
Poster: 0925-Evgeny-Shlevkov-poster
Abstract: In my talk, I will illustrate how integrating pharmacology with advanced cellular technologies—including high-content imaging, iPSC modeling, and machine learning—complements human genetics in the quest to map druggable cellular signaling nodes in neurodegenerative and neurodevelopmental disorders. I will present how the combination of genetics and cell biology guides the nomination and prioritization of drug targets, how high-content imaging can be used to identify druggable mechanisms driving disease-associated cellular phenotypes, and how machine learning can help identify convergent phenotypes in iPSC models derived from patients. These methods and concepts can be applied across indications, offering a powerful platform for the translational investigation of brain disorders.
October 2: David Brundage, School of Veterinary Medicine, UW-Madison
October 9: Yin Li, Biostatistics & Medical Informatics, UW-Madison
October 16: Muxuan Liang, MD Anderson
October 23: Zhenhua Lin, National University of Singapore: LOCATION CHANGE: 1524 Morgridge Hall
October 30: Guanhua Chen, Biostatistics & Medical Informatics, UW-Madison
November 6: Zhengzheng Tang, Biostatistics & Medical Informatics, UW-Madison
November 13: Tom Louis, Johns Hopkins University
November 27: NO SEMINAR
December 4: Jiwei Zhao, Biostatistics & Medical Informatics, UW-Madison
Past Seminars:
September 11
Speaker: Andreas Velten, UW-Madison Dept of Biostatistics & Medical Informatics
CHANGE OF LOCATION: Morgridge Hall Room 1524
Title: Of Photons and Pixels: How the transition to single photon pixels changes imaging
Poster: 0911-Andreas-Velten-poster
Abstract: Cameras and microscopes make measurements of photons to form images or extract other information from a scene. We now have access to various types of single photon or quanta cameras that are able to directly convert individual photons to digital information. This results in fundamental changes in what cameras can do, and how they should be designed.
This presentation shows examples from our recent research to illustrate how the difference between classical and quantum light substantially changes how we might approach designing a camera and what cameras can do. Our examples include low-light and high dynamic range imaging as well as imaging of scintillator fluorescence for radiation detection.
September 18
Speaker: Daifeng Wang, Biostatistics & Medical Informatics, UW-Madison
Title: AI-Driven Single-Cell Analysis: From Disease Mechanisms to Therapeutic Discovery
Poster: 0918-Daifeng-Wang-poster
Abstract:
Emerging single-cell genomics provides an unprecedented view of cellular and molecular heterogeneity across diseases and clinical phenotypes, yet translating these complex data into mechanistic and therapeutic insight remains challenging. This talk presents an AI-driven computational framework for single-cell analysis that integrates multimodal, graph-based, and physics-informed learning to connect genes, cell functions and dynamics, disease phenotypes, and repurposed drugs. First, cooperative multi-view integration and interpretable model explanation prioritize molecular features and higher-order interactions across single-cell and spatial modalities for Alzheimer’s disease (AD). Next, graph learning approaches refine and associate cell subtypes with AD phenotypes, including cognitive resilience, neuropsychiatric symptoms, and pathological progression, reveal personalized molecular diversity, and identify network-based repurposed drugs for neuropsychiatric disorders. Finally, physics-informed AI models infer continuous dynamics of gene expression, cell populations, and perturbation responses. Multi-agent reinforcement learning further reveals virtual cell environments that represent cellular dynamics and interactions. Together, these methods advance interpretable, mechanistic single-cell analyses for disease modeling, patient stratification, and drug discovery.
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