Byte-Sized Biology: How AI Decodes Human Health Bit-by-Bit
13th LMPSU conference
Speaker biographies
See below for speaker information. We will update as we receive more information!
Dr. Andrew Evans MD, PhD, FACP, FRCPC
Presentation Title: Implementation of Artificial Intelligence in Pathology: Plenty of Promise - Plenty of Work Ahead to Make it Happen
Dr. Evans is Chief of Pathology and Medical Director of Laboratory Medicine at Mackenzie Health and an Associate Professor at the University of Toronto. He is a consultant in genitourinary pathology and an internationally recognized expert in digital pathology with over 20 years of experience in the use of the technology for patient care purposes.
Mackenzie Health transitioned to complete digital pathology for surgical pathology in 2021 and is currently in the process of integrating AI tools into routine diagnostic workflow.
Prior to moving to Mackenzie Health in 2020, Dr. Evans was Director of Telepathology/Digital Pathology at University Health Network from the inception of the program in 2004. This program successfully introduced digital pathology into routine patient care for frozen sections, consultation, quality control/assurance and primary diagnosis. Since 2010, he has been a member of numerous College of American Pathologists committees and guideline development efforts related to digital pathology and is currently a member of the CAP Artificial Intelligence Committee and Council for Informatics and Pathology Innovation.
Dr. Anna Goldenberg PhD
Presentation Title: TBC
Dr. Anna Goldenberg is a professor in the departments of Computer Science and Laboratory Medicine and Pathobiology. She is the program director of the MScAC concentration in Artificial Intelligence in Healthcare. She is the Research Co-Lead, Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM).
She is a Varma Family Chair in Biomedical Informatics and Artificial Intelligence at SickKids Research Institute, as well as a CIFAR AI Chair at the Vector Institute. She co-chairs AI in Medicine initiatives at both UofT and SickKids. Dr. Goldenberg trained in machine learning at Carnegie Mellon University with a postdoctoral focus in Computational Biology and Medicine. The current focus of her lab is on developing and deploying machine learning models to healthcare. Dr Goldenberg’s lab is strongly committed to creating responsible AI to benefit patients across various conditions.
Dr. Michael Brudno
Presentation Title: Healthcare data in the age of AI
Michael Brudno is a Professor in the Department of Computer Science at the University of Toronto and the Chief Data Scientist at the University Health Network (UHN). He is also a faculty member at the Vector Institute for Artificial Intelligence and the Scientific Director of HPC4Health, a private computing cloud for Ontario hospitals.
Michael’s primary area of interest for his research is the development of computational methods for the analysis of clinical and genomic datasets, especially the capture of precise clinical data from clinicians using effective user interfaces and its utilization in the automated analysis of genomes. His work focuses on the capture of structured phenotypic data from clinical encounters, using both refined user interfaces, and mining of unstructured data (based on machine learning methodology), and the analysis of omics data (genome, transcriptome, epigenome) in the context of the structured patient phenotypes.
Michael received a BA in Computer Science and History from UC Berkeley and later received his Ph.D. from the Computer Science Department of Stanford University, working on algorithms for whole-genome alignments. He completed a postdoctoral fellowship at UC Berkeley and was a Visiting Scientist at MIT. He is the recipient of the Ontario Early Researcher Award and the Sloan Fellowship, as well as the Outstanding Young Canadian Computer Scientist Award.
Dr. Muhammad Mamdani, PharmD, MA, MPH
Presentation Title: The Application of Artificial Intelligence in Healthcare
Dr. Mamdani is Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Faculty of Medicine Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM).
Dr. Mamdani’s team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency.
Dr. Mamdani is also Professor in the Department of Medicine of the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana School of Public Health. He is also an Affiliate Scientist at IC/ES and a Faculty Affiliate of the Vector Institute.
In 2024, Dr. Mamdani’s team received the national Solventum Health Care Innovation Team Award by the Canadian College of Health Leaders. Previously, Dr. Mamdani was named among Canada’s Top 40 under 40. He has published over 500 studies in peer-reviewed medical journals. Dr. Mamdani obtained a Doctor of Pharmacy degree (PharmD) from the University of Michigan (Ann Arbor) and completed a fellowship in pharmacoeconomics and outcomes research at the Detroit Medical Center. During his fellowship, Dr. Mamdani obtained a Master of Arts degree in Economics from Wayne State University with a concentration in econometric theory. He then completed a Master of Public Health degree from Harvard University with a concentration in quantitative methods.
Dr. Phedias Diamandis MD, PhD, FRCPC
Presentation Title: 'PHARAOH: A collaborative crowdsourcing platform for Phenotyping And Regional Analysis Of Histology’
Dr. Diamandis completed his combined MD/PhD and residency training in neuropathology at the University of Toronto. He is a Neuropathologist at the University Health Network and a Scientist at Princess Margaret.
His research focuses on using chemical biology, deep learning and mass spectrometry-based proteomics to resolve phenotype-level heterogeneity in different brain and glioblastoma niches.