Elham is an Assistant Professor at the Faculty of Health and is also cross-appointed to the Lassonde School of Engineering (EECS) at York University. She is a faculty affiliate at Vector Institute and cross-appointed with the Institute of Health Policy, Management, and Evaluation (IHPME) at the University of Toronto. Prior to joining York University, she was an Applied Machine Learning Scientist, Health Lead at Vector Institute. Her innovations in machine learning and health have always been inspired by complex health problems and have positively impacted Ontario's healthcare system. Over the past 14 years, her work portfolio and research agenda have been dedicated to the adoption of ML and Deep Learning technologies into real-world needs in order to accelerate the pace of scientific discovery, improve evidence-based decision-making, and increase healthcare operational efficiency.
Postdoctoral Fellows
Abeer Badawi, PhD
Dr. Abeer Badawi is a postdoctoral researcher at York University and Vector Institute, supported by the Connected Minds Scholarship. She earned her PhD in Computer Engineering at Ontario Tech University. She received the Ontario Graduate Scholarship and a nomination for the best thesis award for her work on AI-driven prediction of neuropsychiatric symptoms in dementia patients. She has experience in AI research and healthcare applications since 2016 and collaborated with leading organizations such as UHN, Ontario Shores Hospital, University of Toronto, Vector Institute, and Kids Help Phone. Her research focuses on digital biomarkers, machine learning, large language models, responsible AI, and mental health LLM systems evaluation for healthcare and mental health applications. Dr. Badawi is also a lecturer in computer engineering department at Ontario Tech University. Aside from academia, Dr. Badawi also worked in the industry as a machine learning specialist at Vector Institute and a Data scientist at BetterCart Inc.
Graduate Students
Pedram Noghani Ardestani, PhD Candidate
Pedram is a Ph.D. student in Health System Management and Health Data Analytics at York University. He holds a B.Sc. in Biomedical Engineering, an M.Sc. in Economics, and an M.Sc. in Systems Science - Health Systems. With over 10 years of experience working as a Methodologist and Health Data Analyst in the Canadian healthcare system, he has gained extensive knowledge in healthcare data analysis. He is particularly interested in exploring the potential of ML and AI and their implications for improving healthcare systems and patient outcomes. His research topic is identifying high-risk patients for the Geriatric Emergency Management (GEM) program using ML.
Shayan Emzad,
MSc student
Shayan is a Master’s student in Computer Science at York University (Lassonde School of Engineering (EECS)) with a background in Computer Engineering from Sharif University. His research lies at the intersection of foundation models and health.
Negin Baghbanzadeh, MSc candidate
Negin is a Master’s student in Computer Science at York University (Lassonde School of Engineering (EECS)) with a background in Computer Engineering. Her research specializes in multimodal learning and generative models for medical data. By integrating various data types, she develops advanced models capable of performing a wide range of tasks, including prediction, segmentation, and report generation, aiming to enhance the accuracy and utility of machine learning in medical applications.
Nathan Kabore,
MA student
Nathan Kabore is a Masters Student at York University in health systems management and health data analytics. He has a background in Health studies, having done a HBs at York University with a minor in Health informatics. He is looking at leveraging machine learning to target the absence of race based data and it's impact on health disparities among racialized people.
Juan Medina, MSc
Juan is a student in the MSc Health Systems Research (Health Systems Artificial Intelligence Emphasis) program at the University of Toronto. He has a multidisciplinary background in Economics, the Science in Society Program (Sociology, Neuroscience, Health & Technology Studies), and Quantitative Methods. His research focuses on leveraging machine learning towards the development of more equitable policy solutions.
Sarah Taleghani, MA
Sarah Taleghani is currently pursuing an MA in Health and is specializing in Health System Management and Health Data Analytics at York University. Her research interests primarily revolve around using artificial intelligence to revolutionize healthcare and improve health outcomes for individuals of all ages. Previously, she worked as a research assistant at Sunnybrook Health Sciences Centre on a project related to ovarian cancer, another area of her interest. Sarah was also a staff writer for York University's community newspaper, Excalibur, and enjoys writing about topics related to health and science.
Noor Qaiser, MA
Noor Qaiser is currently pursuing a Masters in Health with a specialization in Health System Management and Data Analytics at York University. Her research interests surround intertwining intersectionality and health outcomes with a particular focus on marginalized communities. She is extremely passionate about identifying means to alleviate community hardship particularly within and adjacent to the healthcare sector. Noor is enthusiastic to be able to work with like minded individuals and collaborate on projects that draw insights and recommendations from research findings utilizing data analytics.
ML Research Associates
Yasaman Parhizkar, MASc
Yasaman Parhizkar is a Research Intern at the i4Health lab, specializing in multimodal representation learning and contrastive learning for health applications. She completed her master's degree in computer engineering at York University in August 2023. Her master's thesis focused on predicting the responses of salamander retinal ganglion cells to visual stimuli using an interpretable graph-based method (read the paper here). Yasaman is driven by the opportunity to improve healthcare quality and the general beauty of statistical models.
Mansoure Jahanian, MASc
Mansoure is an applied Machine Learning specialist focusing on predicting biomarkers for long COVID using clinical and genetic data. This project is a collaboration between the i4Health lab and the CANCOV study at UHN. Previously, she earned her master's degree in Computational Neuroscience from Western University. Her research during this time explored the intersection of computer science and neuroscience, specifically delving into the neural dynamics of rapid target presentation in the human brain through the application of machine learning techniques. Mansoure's interest lies in leveraging machine learning for advancements in healthcare.
Collaborators
Dr. Allan Kember
Allan Kember is an entrepreneur, clinician-scientist, and senior resident physician in the Department of Obstetrics and Gynaecology at the University of Toronto. Growing up in an autobody shop, Allan learned to work with his hands from a young age. He trained and practiced as a mechanical engineer prior to completing medical school. For his recent master’s thesis, Allan built datasets and deep learning models to detect and quantify sleep position, behaviors, and physiology in pregnancy in an effort to equip sleep-in-pregnancy researchers with AI-powered data collection and analysis tools. Allan is the President and CEO of Shiphrah Biomedical Inc.
Dr. Shaina Raza currently serves as an Applied Machine Learning Scientist with a focus on Responsible AI at the Vector Institute of Artificial Intelligence, located in Toronto, Canada. She holds a Ph.D. in Computer Science, where her research centered around Recommender Systems and Fairness in Machine Learning. Her research portfolio is diverse, covering areas such as Recommender Systems, ML Fairness, Public Health Equity, and Ethical AI. Before assuming her current position, Dr. Raza enriched her experience as a Postdoctoral Fellow at Public Health Ontario and the Dalla Lana School of Public Health. She held the esteemed position of CIHR HSIF award holder during this time. Over the years, Dr. Raza has made several contributions to her academic field. She continues her commitment to understanding and innovating at the crossroads of technology and societal welfare.
Lab Alumni
Henry Huang. (Mitacs Summer Intern) Title of Project: to use computer vision to improve pregnancy outcomes