Meet T-CAIREM's 2026 Trainee Rounds presenters!

The T-CAIREM 2026 Trainee Rounds highlight innovative and outstanding research at the intersection of Artificial Intelligence (AI) and health across Canada.

These top applicants were chosen from an open call to post-secondary educational institutions across Canada. These 10 researchers will have the opportunity to present their research before a panel of AI in health leaders in the summer for a special award. 

The Trainee Rounds consist of biweekly one-hour virtual presentations. At each seminar, two trainees will give 20-minute presentations on their research, followed by 10 minutes of Q&A and discussion. Topic areas can include any research at the intersection of AI and health. See when all five virtual sessions of this year's Trainee Rounds will take place on our Events page. We hope you can attend!

See when the Trainee Rounds presentations will take place! 

Leen Alzebdeh

Leen Alzebdeh is a graduate student researcher at the University of Alberta. She is developing predictive ML models to predict blood glucose in hospitalized patients using electronic health records, with the goal of alerting staff to upcoming dysglycemia and enabling timely, personalized clinical decision support. She has hands-on experience with time-series prediction, handling missing and irregular clinical data, and “opening” black box AI for physicians using interpretability techniques such as SHAP. Building on this foundation, her collaborators are now extending their work to create an AI insulin recommender that utilizes the blood glucose predictor as a virtual patient simulator. This work aims to further automate insulin decision support, thereby reducing demand on healthcare staff, lowering hospital expenses, and improving patient outcomes. Beyond her work, she's interested in bed-to-bedside translation: interpretable, transparent, and trustworthy algorithms that aim to improve patient care. She is also interested in clinical decision support, time-series prediction, and human-centred AI. 
PRESENTATION TOPIC: Predicting Inpatient Blood Glucose for Cardiovascular Surgery Patients with T2D Using EHR Data

Austin A. Barr

Austin A. Barr is a medical student at the University of Calgary and co-founder of the school’s AI society. His research focuses on generating 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 online, sharing developments on the integration of novel technologies in healthcare: @alvie_barr
PRESENTATION TOPIC: Diffusion-Generated Synthetic Neuroimaging Enables Transfer Learning for Anatomical Landmark Localization in Low-Data Settings

David Chen

David Chen is a first-year radiation oncology resident at the University of British Columbia. He completed his MD at the University of Toronto and BMSc at Western University. His research interests include cancer bioinformatics and applications of artificial intelligence for clinical decision-making and patient care in oncology. Through his research, David aims to leverage big data and artificial intelligence to generate evidence-based conclusions in medicine. He is currently designing AI tools to support clinical trial screening and conduct, automate evidence synthesis in systematic reviews, evaluate and improve the reporting completeness of research, as well as translate and summarize complex healthcare information into patient-friendly and useful formats. Outside of medicine and science, David is involved in community arts initiatives, including musicals and fashion shows, and recently discovered an unlikely interest in spin classes. 
PRESENTATION TOPIC: Development of an Agentic Multi-LLM System to Support the Informed Consent Process in Clinical Trials

Ganlin Feng

Ganlin Feng is a second-year PhD student in Computer Science at Western University under the supervision of Dr. Pingzhao Hu, and holds a BSc in Computer Science from the University of Waterloo. Her research lies at the intersection of computer vision and medical AI, with a focus on rare disease recognition from facial images, a setting characterized by extreme data scarcity and high clinical variability. Her work involves building reproducible benchmark datasets, as well as developing deep learning approaches and synthetic data generation for fine-grained classification tasks. More recently, she has been exploring agentic AI systems for medical reasoning, where multiple agents collaborate to analyze visual and clinical signals and generate structured diagnostic hypotheses. By combining vision-centric approaches with learning- and agent-based methods, she aims to improve the reliability and interpretability of AI-assisted diagnosis in medical imaging.
PRESENTATION TOPIC: Adaptive Agentic AI to Enhance Clinical Diagnosis in Paediatric Rare Diseases

Marco Istasy

Marco V. Istasy is a fourth-year medical student and researcher at the Temerty Faculty of Medicine, University of Toronto and an incoming resident in the Division of Neurosurgery, University of Ottawa. Prior to that, he obtained his Honours Bachelor of Science with a specialization in Neuroscience and a Master of Applied Science with the Department of Biomedical Engineering, both at the University of Toronto. His research interests lie at the intersection of artificial intelligence and medical hardware innovation, with a primary focus on developing technological solutions to optimize clinical outcomes for neurosurgical patients. Most recently, his work was recognized with the Young Investigator Award from the Canadian Brain Tumour Consortium.
PRESENTATION TOPIC: Machine learning identifies prognosticators of intracranial metastatic disease in patients with breast or lung cancer

Mahri Kadyrova

Mahri Kadyrova is a PhD researcher 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 PGS-D scholarship.
PRESENTATION TOPIC: Deep Learning–Based Tongue Segmentation and Motion Analysis in Motor Neuron Disease Using Uncontrolled Videos

Dilakshan Srikanthan

Dilakshan Srikanthan is an MD/PhD candidate in Translational Medicine at Queen's University, supervised by Dr. Parvin Mousavi and Dr. John Rudan. His doctoral research focuses on applying deep learning to glioblastoma detection, characterization, and prognostication across the clinical care continuum. His work spans two domains: intraoperative mass spectrometry, where he developed cross-cancer self-supervised learning and uncertainty estimation methods for real-time tumour margin detection using REIMS, and the applications of foundation models in computational pathology for glioblastoma characterization and prognostication. Before Queen's, he trained at the Hospital for Sick Children and University of Toronto under Drs. James Rutka and Cynthia Hawkins, where he investigated immunotherapeutic approaches to diffuse midline gliomas during his Master's. Dilakshan is a recipient of the CIHR Vanier Scholarship, and the Ontario Graduate Scholarship. Aside from this, Dilakshan enjoys playing soccer, running, training in Muay Thai, and spending time with family and friends. 
PRESENTATION TOPIC: Spatial Interfaces Between Histological Regions Predict Glioblastoma Prognosis Beyond Composition

Valentina Tamayo Velasquez

Valentina Tamayo is a PhD candidate at the Institute of Medical Science, University of Toronto, under the supervision of Dr. Bernard Le Foll and Dr. Andrea Waddell. Her research focuses on development, implementation, and adoption strategies for integrating artificial intelligence (AI) and machine learning (ML) technologies in mental healthcare settings, with a particular emphasis on patient safety and outcomes. Her thesis project examines the development and implementation of an ML model, the Predictive Risk Identification for Mental Health Events (PRIME) tool, to predict mental health-specific adverse events, alongside co-design and stakeholder engagement for AI response strategies. She also holds an Honours Bachelor of Science degree from the University of Toronto (Neuroscience, Psychology, and French Literature and Language Studies).
PRESENTATION TOPIC: Bringing AI to Psychiatry: Developing and implementing an AI prediction and alerting tool for psychiatric adverse events.

Ariana Walji

Ariana (she/her) is a Research Trainee at University Health Network and an MSc candidate at the University of Toronto. Her research focuses on facilitating the clinical integration of AI decision support tools into the operating room, with an emphasis on real world usability, workflow alignment, and improving patient safety outcomes. She completed her undergraduate studies in Medical Science at Western University where she graduated Honours with Distinction.

Ariana has extensive research experience on a global scale. She previously worked as a summer research student at Princess Margaret Hospital in Toronto, contributing to machine learning projects aimed at improving radiation treatment outcomes for patients with oropharyngeal cancer and breast cancer. During her time in Amsterdam, where she completed a research minor in cardiovascular disease, Ariana led a project evaluating the clinical readiness of machine learning–based risk prediction tools for in-hospital cardiac arrest. She has travelled to Nairobi, Kenya, where she supported initiatives focused on destigmatizing mental health and improving mental health literacy in the community.

Beyond academia, Ariana is passionate about mentorship and women’s empowerment, with ongoing involvement in organizations such as Hygiene4Her and Girls Who Lead. She enjoys sustainable living and is an avid coffee enthusiast who loves trying new beans from around the world. 
PRESENTATION TOPIC: Optimizing AI Implementation for Surgery: Recommendations for Infrastructure and Deployment in the Operating Room

Elnaz Ziad

Elnaz Ziad is a PhD candidate in Mechanical and Industrial Engineering at the University of Toronto and a researcher at Princess Margaret Cancer Centre. Her research aims to improve care for older adults with cancer. Specifically, she is developing a machine learning–based model to predict treatment-induced toxicities for this vulnerable population at the time of their first treatment. By enabling accurate and scalable automated prediction, her work seeks to address important limitations of current toxicity prediction tools. In addition to her research, she pursues her passion for teaching as a Teaching Assistant, supporting students throughout their learning journey. Her other interests include community building, event organization, mentorship, and creating supportive spaces for learning and connection.
PRESENTATION TOPIC: Geriatric oncology toxicity risk estimation after treatment (GO-TREAT): Machine learning models to predict adverse events in older adults with cancer.