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May 13, 2026  |  11:00am - 12:00pm

What Goes Wrong in Bioinformatics: Batch Effects, Models, and Misleading Signals 

Type
LMP seminar series
Tag(s)
Disruptive Innovation, Impactful research

As part of our LMP Seminar Series we are delighted to welcome our speaker:

Jesse Gillis, PhD
Associate Professor, Department of Physiology
James B. Bassingthwaighte Chair in Integrative Physiology
University of Toronto

Talk title: What Goes Wrong in Bioinformatics: Batch Effects, Models, and Misleading Signals 

Hosted by

CLAMPS

How to join

The event will be in person, no need to register. Students and trainees must attend in person.

For faculty members who need to attend remotely, please register to receive the zoom link. Registration must be received by noon on Tuesday. 

If you have any questions, please contact lmp.chairadmin@utoronto.ca for more details.

Wednesday, May 13, 2026

11 am - 12 pm

MSB 2170

Medical Sciences Building
University of Toronto
1 King’s College Circle
Toronto, ON  M5S 1A8 

Details are sent to the LMP community in the Friday events bulletin.

Speaker bio: Jesse Gillis, PhD

Jesse Gillis is an Associate Professor in the Department of Physiology and Donnelly Centre at the University of Toronto and holds the James B. Bassingthwaighte Chair in Integrative Physiology. His research focuses on computational genomics and systems biology, with an emphasis on single-cell and spatial transcriptomics, cross-dataset integration, and the development of methods for robust biological inference from large-scale data.

Prior to joining the University of Toronto in 2022, he was a faculty member at Cold Spring Harbor Laboratory from 2012 onward. His work has contributed to major collaborative efforts including the Brain Initiative Cell Atlas Network, where he has played a leadership role in developing analytical frameworks for large-scale brain cell atlas data.

The Gillis lab develops computational approaches to address fundamental questions in cellular identity, gene regulation, and developmental biology, with a particular focus on reproducibility and the interpretation of complex genomic datasets.

Jesse Gillis