2025 Trainee Rounds seminars
Meet this year's Trainee Rounds presenters
Ghadir Ali
Ghadir Ali is a PhD student at the Electrical and Computer Engineering Department, University of Toronto, specializing in Natural Language Processing (NLP) for healthcare applications. Her research focuses on AI-assisted clinical documentation, improving efficiency, accuracy, and usability in clinical contexts.
Her Master’s degree in Communication and Information Technology (Informatics) is from Nile University, Egypt, where she worked on AI models for lung disease detection, integrating radiology and genomics to enhance explainability while preserving predictive performance. She also holds a Bachelor’s degree in Systems and Biomedical Engineering from Cairo University, Egypt.
Beyond research and academia, she has 3+ years of industrial experience in AI development, in addition to mentoring and teaching professional experience. She has been recognized with multiple awards and fellowships for her contributions to medical AI. She is passionate about explainable AI, clinical decision support systems, and interdisciplinary research at the intersection of AI and healthcare. SEMINAR DATE: May 28, 2025, 12pm to 1pm ET
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Suraj Bansal
Suraj Bansal is a first-year medical student at the University of Ottawa and a researcher in Dr. John Dick’s lab at the Princess Margaret Cancer Centre. As an aspiring clinician-scientist, he is passionate about leveraging single-cell multi-omics, machine learning, and experimental approaches to study disease heterogeneity and develop targeted therapies for acute leukemias. Currently, he is building ATLAS-AML, a cloud-based bioinformatics pipeline designed for transcriptomic meta-analysis in blood development and leukemia. Additionally, Suraj is passionate about making cancer research and bioinformatics accessible to his peers. He is co-organizing the 2025 StemCellTalks Toronto conference and leading a series of AI workshops and symposiums for medical students at the University of Ottawa. Beyond his research, Suraj enjoys exploring local coffee shops, playing squash, and strumming his guitar.
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Austin A. Barr
Austin A. Barr is a medical student at the University of Calgary and current President of the school’s AI Society. His research focuses on the generation of high-fidelity synthetic clinical data. Prior to medical school, Austin served as a Program Officer at the Canadian Institute for Advanced Research (CIFAR), where he supported the Pan-Canadian AI Strategy. He has a background in clinical research, having worked at Sunnybrook Hospital’s Research Institute, and holds a BSc from McMaster University. During his time at McMaster, he contributed to the design, implementation, and teaching of an introductory AI course. Austin is also active on Twitter, sharing developments on the integration of novel technologies in healthcare: @alvie_barr.
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Kejah Bascon
Kejah Bascon is a PhD candidate in Mechanical Engineering at the University of Toronto’s Robotics Institute. She completed her undergraduate degree in Cognitive Psychology at Carleton University in Ottawa, followed by a Master’s degree in Neuroscience. Her research interests include neurosurgical robotics, and she has been recognized as a recipient of the Indigenous and Black Engineering and Technology (IBET) Momentum Fellowship.
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Sidrah Laldin
Sidrah Laldin is a first-year cardiac surgery resident at the University of Manitoba. Since her medical school training at McGill University, she has been interested in researching and developing consumer-centered, data-driven applications in healthcare using design-thinking methodologies. She is particularly interested in applying artificial intelligence-based methodologies to improve surgical outcomes for cardiac surgery patients. Before her training in medicine, she was a computer scientist with a focus on computer vision and imaging, which prompted her current research using AI in cardiovascular imaging for perioperative care.
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Wanjin Li
Wanjin (Jennifer) Li is a second-year PhD student in Epidemiology at McGill University. She holds an MSc in Public Health from McGill and a BMSc in Medical Sciences from Western University. Her research focuses on optimizing blood donation policies to improve donor safety and blood supply management through advanced Machine Learning and simulation models.
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Yuxi Long
Yuxi Long is a first-year PhD student in the Computer Science program at Western University, under the supervision of Dr. Pingzhao Hu. Her research interests focus on bioinformatics, particularly the application of artificial intelligence (AI) in medical imaging. She is currently working on the diagnosis and interpretable analysis of RA thermal images and the development of a foundation model for hand X-ray images to improve automated diagnostics and clinical decision-making.
Yuxi earned both her Bachelor’s and Master’s degrees in Computer Science from Northwestern Polytechnical University, China. During her graduate studies, she conducted research on Deep Learning for enhancer identification, enhancer-promoter interaction prediction, and cancer diagnosis and mutation prediction using H&E images, contributing to advancements in computational biology and precision medicine.
After completing her Master’s degree, Yuxi gained three years of industry experience as a Recommendation Algorithm Engineer at a leading technology company, where she focused on ranking and re-ranking strategies for optimizing AI-driven recommendation systems.
Through her research, Yuxi aims to develop AI-driven tools that enhance medical diagnostics, improve disease prediction, and contribute to more accurate, early-stage detection of complex conditions. By integrating Machine Learning and medical imaging, she hopes to advance precision medicine and ultimately improve patient outcomes and global healthcare accessibility.
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Faezeh Lotfi Kazemi
Faezeh Lotfi Kazemi is a PhD candidate in Experimental Medicine at McGill University under the supervision of Dr. Matthias Friedrich, and has a background in biomedical engineering and computer science. Her research focuses on developing advanced AI algorithms for analyzing oxygenation-sensitive cardiac MRI (OS-CMR). Along with her labmates, she is developing new imaging modalities to enhance patient safety by reducing the need for contrast agents. OS-CMR leverages breath-hold maneuvers instead of traditional contrast injections, providing a safer alternative for patients with renal impairment or other contraindications. Her role involves designing and implementing Deep Learning models to analyze these novel MRI sequences, detect hidden pathological patterns, and explore the use of GANs to synthesize new imaging modalities. By integrating AI-driven insights into clinical workflows, her goal is to advance non-invasive cardiac diagnostics and improve patient outcomes.
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David Mikhail
David Mikhail is a second-year medical student and MSc student at the University of Toronto. His research explores applications of Deep Learning in ophthalmology and surgical education, with a focus on retinal disease detection, glaucoma screening, and developing objective methods to assess cataract surgery proficiency.
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David Pellow
David Pellow is a Schmidt AI in Science postdoctoral fellow in computer science at the University of Toronto. His research interests are in Machine Learning for healthcare and biology. As part of the Transplant AI Initiative at UHN, his research focuses on modelling long-term cardiovascular risk in post-liver transplant patients. He also works on optimizing immunosuppressive drug dosing during the immediate post-transplant hospitalization. Prior to U of T, David was a postdoctoral fellow at the Weizmann Institute of Science, working on modelling and analyzing disease outcomes in biobank data. His doctoral research at Tel Aviv University focused on computational genomics. SEMINAR DATE: May 28, 2025, 12pm to 1pm ET
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