Trainee Affiliates (2026-2027)

MD Student, Temerty Faculty of Medicine
University of Toronto
Joshua is a medical student at the University of Toronto. He previously completed his undergraduate degree in physiology at McGill University. His research focuses on improving population health in urologic cancers using AI, with broader research interests in health equity, health services, and meta-research. Joshua is committed to advancing the role of AI in medical research, innovation, and education, and is actively developing and supporting initiatives in these fields for clinicians, researchers, and trainees.

MD/PhD student, Institute of Health Policy, Management, and Evaluation
Temerty Faculty of Medicine, University of Toronto
Anglin holds an HBSc from the University of Guelph, an MSc from the University of Toronto, and is entering her fourth year of the University of Toronto MD/PhD program. Her previous studies have involved the development and validation of AI tools for precision medicine in cancer care, as well as the examination of how to best integrate healthcare solutions in a way that complements, rather than replaces, current workflows. In her PhD at SickKids, Anglin is excited to conduct clinical validation studies of machine learning technologies for early diagnosis and personalized therapy in childhood asthma.

PhD student, Department of Electrical and Computer Engineering
Faculty of Applied Science and Engineering, University of Toronto
Mahri is a PhD student in the Department of Electrical and Computer Engineering under the supervision of Dr. Ervin Sejdic, collaborating with Dr. Yana Yunusova from the Department of Speech-Language Pathology. Her research applies machine learning to uncontrolled, real-world video data to develop digital health assessment tools for remote monitoring of individuals with motor neuron disease. She is a recipient of the NSERC Postgraduate Scholarship-Doctoral (PGS-D).

Resident Physician, Division of Urology, Department of Surgery, Temerty Faculty of Medicine, University of Toronto
Jethro is a urology resident in the Surgeon Scientist Training Program at the University of Toronto. He completed a Master’s degree in Health Systems Artificial Intelligence, focusing on applying AI to improve prognostication in bladder cancer. His primary research interests revolve around the application of AI in urology and improving quality of AI studies. His AI work has been published in high-impact journals including The Lancet Digital Health, NEJM AI, JAMA Network Open, and npj Digital Medicine. He is currently an editorial board member at npj Digital Medicine and serves as an AI reviewer for several urological and digital health journals.

PhD candidate, Surgeon Scientist Training Program, Temerty Faculty of Medicine, University of Toronto
Ben Li is a vascular surgery resident and PhD candidate in the Surgeon Scientist Training Program at the University of Toronto. His research focuses on using machine learning to predict outcomes following major vascular surgery. As a member of T-CAIREM since 2021, Ben is excited to join the Education team as a Trainee Affiliate. With a strong interest in AI and medical education, Ben hopes to create meaningful learning opportunities for clinicians, researchers, and learners at all levels of training and practice.  

PhD student, Institute of Medical Science
University of Toronto 
Charlotte has a background in psychology and neuroscience and is now in her second year of her PhD in Medical Science at the University of Toronto. She enjoys working with both data and people, using research and machine learning to better understand mental health and brain disorders. She has also valued the human side of healthcare, both from the perspectives of clinicians and the patients they care for. She’s currently involved in clinical trials and is developing AI tools to help predict recovery for patients with subdural hematoma. Charlotte is also passionate about teaching and innovative education tools.

PhD Candidate, Clinical Epidemiology and Healthcare Research; Neurosurgical Resident, Division of Neurosurgery, Department of Surgery, University of Toronto
Armaan is a current fourth-year neurosurgical resident and PhD candidate interested in pediatric neurosurgery, spinal surgery, and trauma. His PhD studies at the Institute for Health Policy, Management, and Evaluation focused on better characterizing and predicting outcomes of neurotrauma. Armaan has been involved in a collaborative effort to optimize the provincial triage system for brain-injured patients by leveraging artificial intelligence-based clinical decision-support systems. He is passionate about understanding the intersection between AI solutions and health systems, with particular emphasis on implementation, bias, and risk.

MD Student, Temerty Faculty of Medicine
University of Toronto
Julie is a fourth-year medical student who holds a Bachelor of Science in biological physics from the University of Toronto. She has worked on a variety of projects, such as designing machine learning models to predict health outcomes, including dementia and radiation pneumonitis, as well as to segment pathology in medical imaging. She is also interested in using machine learning algorithms as mathematical analogues for biological processes. She is an education trainee affiliate at T-CAIREM and believes that training healthcare professionals to have a strong understanding of AI's strengths and weaknesses is critical to the future of medicine.

MSc, Health Systems Research, Institute of Health Policy Management and Evaluation, University of Toronto
As an MSc student and Project Manager at IHPME, Konrad works with health researchers, clinicians, developers, and system leaders to responsibly leverage algorithms in patient care. He is leading the development of new analytic tools to support surgical planning, systems to capture actionable information from text data, and methods to study the safety and reliability of medical artificial intelligence (AI) tools. Konrad has been a vocal advocate on algorithmic bias, and has given seminars on this topic at the Dalla Lana School of Public Health and AMS Healthcare. He holds a master's degree in public health epidemiology from the University of Toronto, with a specialization in global health. He is a former Laboratory Instructor at the Faculty of Medicine, NEST Fellow at the Faculty of Applied Science and Engineering, and Trainee under the AI for Public Health Research Training Platform. With a leadership background in quality improvement, patient safety, and institutional governance, he brings a systems perspective to the safe and responsible deployment of AI in healthcare.

PhD Student, Institute for the History and Philosophy of Science and Technology (IHPST), University of Toronto
Renée Andrea Sirbu is a PhD student at IHPST. Renée received her B.Sc. in Human Biology, Bioethics, and Philosophy at UofT and her MPH. in Health Policy and Public Health Modelling from Yale, before spending two years as a predoctoral researcher at Yale's Digital Ethics Center. Her research explores the intersection of mortality and digital technology, focusing on human-computer interaction, brain-computer interfaces, and AI in clinical settings.

PhD student, Institute of Biomedical Engineering
Faculty of Applied Science and Engineering, University of Toronto
Samantha is a PhD student in Biomedical Engineering, developing wearable devices for monitoring cardiovascular health. She holds a Bachelor of Applied Science in Engineering Science from the University of Toronto with a major in Biomedical Engineering and a minor in Artificial Intelligence. She has previously conducted research in synthetic biology, electrical engineering, machine learning, and psychiatry. Samantha is passionate about the intersection between biomedical engineering and artificial intelligence, and how to approach design in an ethical and equitable way. Outside of the lab, Samantha enjoys making pottery and exploring Toronto for new ice cream flavours.

MD/PhD student, Temerty Faculty of Medicine
University of Toronto
Julia is an MD/PhD student at the University of Toronto, with a background in Mathematics and Engineering from Queen’s University and a Master’s in Computer Science from U of T. Her work spans ultrasound deployment in low-resource settings, the development of surgical education tools, and the application of computer vision and machine learning to endoscopic imaging for improved procedural care. Julia is passionate about translational clinical engineering and ethical AI, which she sees as essential to advancing equitable, patient-centered care. She is committed to bridging the gap between technical innovation and clinical practice.

Radiology Resident Physician
Diagnostic Radiology Residency Program, University of Toronto
Kay Wu is a diagnostic radiology resident at the University of Toronto. She completed her MS in Biomedical Informatics at Harvard Medical School as a Frank Knox Memorial Fellow, where she developed deep learning models for lumbar spine MRI classification, advanced AI-assisted reporting, and curated global imaging datasets to support equitable model development. Her academic interests focus on the design, evaluation, and responsible implementation of AI in imaging, including workflow optimization, human-AI collaboration, and global healthcare delivery. Kay is also an artist and writer whose work reflects a passion for bridging clinical practice, technology, and humanistic care.

Neurosurgery Resident, PhD Student, Department of Surgery, University of Toronto
Dr. Leo Yefet is a neurosurgery resident and PhD student at the University of Toronto whose research bridges artificial intelligence and neuro-oncology, with a special focus on pediatric, adolescent, and young adult brain tumors. His work integrates molecular profiling, plasma-based biomarkers, and advanced imaging analytics to develop non-invasive tools that personalize treatment. Driven by a commitment to innovation and clinical impact, Dr. Yefet’s research seeks to overcome traditional barriers in precision medicine, ensuring that discoveries in molecular science are accessible across all care settings.