Hosted on Wednesdays, 4:00-5:00 pm.
See below for seminar dates and speakers. Seminars will be recorded and updated to our YouTube channel.
Data Science Institute
Center for Computational Molecular Biology
Computational Biology Seminar Series
The CCMB seminar series brings together colleagues from across the world to speak on recent research developments in computational biology, fostering robust conversations and diverse connections for our faculty and students.
Computational Biology Seminar Series
The CCMB seminar series brings together colleagues from across the world to speak on recent research developments in computational biology, fostering robust conversations and diverse connections for our faculty and students.
Fall 2026 Seminar Schedule
| Date | Speaker | Seminar Details |
| September 23 | Ashley Conard, Senior Researcher, Microsoft Research; Ph.D. Computer Science and Computational Biology (CCMB Alum) Bio: Dr. Conard builds interpretable AI and human‑in‑the‑loop platforms that support scientists and clinicians in diagnosing and treating rare diseases and cancer. With experience across both wet and dry labs, she designs AI methods from temporal, multi‑omic, and clinical text data that reveal actionable biological insights and enable expert‑driven in silico hypothesis testing. Ph.D. in Computer Science and Computational Biology from Brown University, advised by Dr. Erica Larschan, Dr. Lorin Crawford, and Dr. Charles Lawrence, supported by an NSF GRFP, with a Postdoctoral fellowship in Biostatistics also at Brown. Previously a Fulbright Research Scholar. Bachelor's in Computer Science and Biochemistry from DePauw University, where she founded a farm supported by Dr. Jane Goodall. CCMB Faculty Host: Erica Larschan | Learning meaningful biology with XAI: from evidence to prediction and design Biomedical data provide unprecedented opportunities to understand complex biological systems, but predictive performance alone does not tell us whether a computational model has learned meaningful biology or produced knowledge that scientists and clinicians can use. My research focuses on closing this gap by building interpretable, biologically grounded AI methods and human-in-the-loop platforms that integrate diverse biomedical data, reveal what information drives their conclusions, and enable experts to interrogate and test what models learn. Across this work, I am particularly interested in how biological knowledge and context should inform models, how we rigorously distinguish meaningful biological signal from prediction alone, and how computational insights can support better scientific and clinical decision-making. Increasingly, my research asks how we can move beyond understanding and prediction toward action: using what models learn to determine what to test next, prioritize promising interventions, and ultimately design biological function. In this talk, I will review both my research and how I got to Microsoft Research from Brown. I will share a few insights I have found useful along my journey and look forward to a discussion. |
| October 7 | Yu (Zoe) Zhu and Jessica Patricoski-Chavez
| CCMB Student Presentations Zoe’s Talk Title: NovAST: A Deep Learning Framework for Automated Label Transfer and Novel Cell Type Discovery in Spatial Transcriptomics Jessy’s Talk Title: Multimodal Deep Learning for Early Recurrence Prediction in Glioma and Lung Adenocarcinoma. |
| October 21 | Speaker TBA | Title & Abstract |
| November 4 | Speaker TBA | Title & Abstract |
| November 18 | Pei Wang, Mt. Sinai School of Medicine Bio: Bio: Dr. Pei Wang is a Professor of Genetic and Genomic Sciences at Icahn School of Medicine at Mount Sinai. She obtained her B.S. in Mathematics from Peking University, China, in 2000; and her Ph.D. in Statistics from Stanford University in 2004. Between 2004-2013, Dr. Wang served as a faculty in Program of Biostatistics, Fred Hutchinson Cancer Center and Department of Biostatistics, University of Washington, Seattle, WA. In Oct 2013, Dr. Wang joint Icahn Medical School at Mount Sinai to lead an integrative proteogenomic research program. Dr. Wang’s research has focused on developing statistical and computational methods for analyzing complex biomedical data including mass-spectrometry based proteomics data, as well as high-dimensional network inference. She is a Principal Investigator of the NCI CPTAC (Clinical Proteomic Tumor Analysis Consortium) that aims to understand the molecular basis of cancer through large-scale proteome and genome analysis. CCMB Faculty Host: Zhijin Wu | Network inference and integrative omics study In contemporary proteogenomic cancer studies, a single tumor is often analyzed with more than ten different proteogenomic profiling strategies, ranging from genetics, transcriptomics, proteomics, and post-translational modifications. In this talk, I will discuss the principal needs and challenges of multi-omics integration, with a focus on the application of network models in proteogenomic studies. Analyses of recent CPTAC proteogenomic datasets illustrate practical workflows, tool usage, and performance considerations in real-world data contexts. |
| December 2 | Speaker TBA | Title & Abstract |