Elham is an Assistant Professor at the Faculty of Health 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. 

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 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. 

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 is a Machine Learning and Bioinformatics Research Intern 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.

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.