Education Team Trainee Affiliates (2025-2026)

Abhishek is currently completing a Master of Health Informatics from the University of Toronto and a Bachelor of Science in Health Sciences from the University of Toronto Mississauga. He has served as an advisor to a Member of the Legislative Assembly, contributing to health policy initiatives and community engagement.  Furthermore, he has served as a Hepatitis C Screening Coordinator for vulnerable populations across British Columbia and has attended national conferences like the HEROES Hep C Summit. He is passionate about using digital health and evidence-based strategies to address health inequities and drive meaningful policy change.

MD/PhD student; Institute of Health Policy, Management, and Evaluation; Temerty Faculty of Medicine
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.

MSc Candidate, Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto
Ariana holds an Honours Bachelor of Medical Sciences and is currently pursuing her MSc at the University of Toronto. Her thesis focuses on optimizing the integration of AI into the operating room. She is currently leading a clinical trial to assess the clinical impact of AI decision-support tools for safe dissections in laparoscopic cholecystectomy. Ariana’s passion for AI in healthcare began during her undergraduate studies, where she completed two summer projects using AI to optimize radiation treatment outcomes in cancer patients. Ariana is passionate about advancing the clinical translation of AI through thoughtful design, rigorous evaluation, and interdisciplinary collaboration.

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.

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.  
ResearchGate

MSc candidate, Health Systems AI; Resident Physician, Division of Urology, Department of Surgery, Temerty Faculty of Medicine
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 Fellow at npj Digital Medicine and serves as an AI reviewer for several urological and digital health journals.

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. 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.

MD Student, Temerty Faculty of Medicine, University of Toronto
Julie is a third-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 different 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, such as cell signalling. She is an education trainee affiliate at T-CAIREM and believes that training healthcare professionals with a strong understanding of the strengths and weaknesses of AI is critical for the future of medicine.

Master's of Public Health Student, Division of Epidemiology, Dalla Lana School of Public Health
Konrad is a graduate epidemiology student and a 2024 AI for Public Heath Trainee researching the use of NLP models to improve population health. During his previous undergraduate studies, he focused his research on better incorporating patient perspectives in healthcare decision-making, leading technical and knowledge mobilization projects on this topic. He is a former laboratory instructor at the Faculty of Medicine and has contributed to projects at the Engineering Hatchery, School of Cities, and Institute for Biomedical Engineering. Since 2021, he has helped organize nine quality improvement and patient safety projects with healthcare institutions in the Greater Toronto Area.

Dr. Leo Yefet is a neurosurgery resident and PhD candidate 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.

PhD Candidate with Dental Public Health, Temerty Faculty of Medicine, University of Toronto
Mohammad holds a Doctor of Dental Surgery (DDS) and an MSc in Medical Sciences, and he is now completing the final year of a combined PhD and Dental Public Health specialty at the University of Toronto. He has developed machine‑learning–based diagnostic and prognostic models using structured data and has applied causal ML to uncover heterogeneous treatment effects in head and neck cancer. His work also spans unstructured imagery, including oral radiographs and photographs, where he develops deep‑learning computer‑vision models using CNNs and Transformer architectures. Mohammad is committed to democratizing AI in healthcare and expanding public access to these technologies.

PhD student, Institute of Biomedical Engineering, Faculty of Applied Science and Engineering
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.