Meet Our Trainees

PROJECT TITLE: Study on the implementation and the regulation of Digital Therapeutics (DTx) in the Canadian health care system regarding health equity. Guidelines and best practices from models developed by the most advanced countries in this field.

Léo is a PhD candidate in pharmaceutical sciences and public health in cotutelle between University of Montreal and Simon Fraser University. His research focuses on the implementation of Digital Therapeutics (DTx) in healthcare in Canada ; by identifying and extracting guidelines, good practices and prerequisites from the most advanced countries regarding DTx implementation. Léo is also member of the NIH Common Fund’s Bridge to Artificial Intelligence – Voice as a Biomarker of Health (Bridge2AI) program, with the mission to develop a large-scale, diverse and ethically sourced voice database linked to other health biomarkers to help diagnose diseases.

Xinyang is a PhD student in Biostatistics at the Dalla Lana School of Public Health, University of Toronto. His research interests focus on the development of dynamic prediction model that fully incorporates changes in the longitudinal predictors, with the ability to account for within-cluster correlation in complex multilevel data.

PROJECT TITLE: Optimizing Interprofessional Primary Care Team Configurations in Ontario: Using Administrative Data and Machine Learning to Predict Optimal Team Configurations for Different Patient Panels

Pablo is a Postdoctoral Fellow at the Institute of Health Policy, Management and Evaluation (IHPME) at the University of Toronto, working with Dr. Walter Wodchis. His research examines the impact of diverse interprofessional primary care team configurations on outcomes across various patient panel populations. Using machine learning techniques, Pablo aims to untangle complex high-dimensional data involved in interactions between primary care teams and populations. Ultimately, he aims to identify effective team configurations while identifying inequities in care access and improvement of health outcomes.

Ingrid is a doctoral candidate in Epidemiology at the Dalla Lana School of Public Health at the University of Toronto. Ingrid has extensive experience as a statistician with the British Civil Service, and as a research scientist with the World Trade Center Health Registry in New York City. She holds a BSc in Natural Science from the University of Calgary, a BEd from the University of Toronto and a MSc in Health & Society: Social Epidemiology from the University College London. Through her PhD research, Ingrid will utilise machine learning methods to develop a theoretically informed social risk measure, and evaluate the effect of social risk clustering on multimorbidity in Ontario.

PROJECT TITLE: HiveBridge: Fostering Collaborative AI Model Training Using a Low-Code Platform

Omid is a PhD student in Health Informatics at the University of Toronto, with over two decades of experience in software engineering. He is currently focused on leveraging AI and ML to enhance healthcare through patient-level data collaboration while ensuring robust privacy protections. He has led innovative startups and projects and is now working on an open-source platform democratizing AI development in healthcare. His research includes analyzing the dynamics of data sharing in the Canadian AI landscape, aiming to enhance collaboration and innovation across the field. Omid is passionate about applying cutting-edge technologies to solve real-world challenges.

PROJECT TITLE: Exploring AI-Powered Digital Decoding of Unhealthy Online Gaming and Gambling

Kamyar recently completed his B.Sc. in Computer Engineering at Sharif University of Technology and will be pursuing a Master’s in Health Systems Research with a focus on Artificial Intelligence at the University of Toronto this Fall. He is deeply passionate about Deep Learning and its transformative applications, particularly in Public Health. Kamyar’s current research focuses on leveraging AI to detect unhealthy gambling behavior and analyze the marketing strategies employed by gambling companies. His work aims to make a significant impact in the intersection of technology and health, driving forward innovations that can benefit society.

Steve H. Lee is a PhD candidate in Epidemiology and Biostatistics at Western University in London, Ontario, Canada. He holds a Master of Science in Epidemiology and Biostatistics also from Western University, and a Master of Arts in Quantitative Methods in Social Sciences with a concentration in Economics from Columbia University in the City of New York. He has a wide range of research interests including mental health services research, cluster analytic methods, Bayesian data analysis, social network analysis, natural language processing, and health economics.

PROJECT TITLE: Enhancing Osteoarthritis Predictive Models: Developing and Validating Deep Learning Techniques for Anomaly Detection and Synthetic Data Generation in Administrative Health Datasets

Hassan is a PhD candidate in Community Health Sciences at the University of Manitoba, affiliated with the Manitoba Centre for Health Policy and the George & Fay Yee Centre for Healthcare Innovation. His research focuses on generating realistic synthetic data from administrative health data using generative AI and statistical methods to create equitable risk prediction models across population sub-groups. With a background in Computer Engineering and AI, Hassan has over five years of research and work experience in data mining and natural language processing.

PROJECT TITLE: Optimizing and automating the probabilistic linkage of large-scale administrative health and social data to enhance the equity, diversity, and inclusion of study participants in linked data analyses, and protect data privacy

Barret Monchka is currently pursuing a PhD in Community Health Sciences at the University of Manitoba, where his research focuses on enhancing the quality of linked administrative data. Barret also serves as a data analyst at the George & Fay Yee Centre for Healthcare Innovation where his research activities include automating and optimizing probabilistic record linkage, employing deep learning techniques for medical image interpretation, and efficiently annotating large sets of images collected by longitudinal health studies. Barret’s interests in artificial intelligence are centered on model validation, strategies for training fair and unbiased models, and enhancing external generalizability. He holds a Bachelor of Computer Science and an MSc in Community Health Sciences, both from the University of Manitoba.

PROJECT TITLE: Developing Personalized Lifestyle Recommendations for People at Risk of Developing Type 2 Diabetes Mellitus

Mohammad Noaeen is a Data Science Institute (DSI) Postdoctoral Research Fellow at the Dalla Lana School of Public Health, Institute of Health Policy, Management and Evaluation, University of Toronto. He received his PhD in Software Engineering from the University of Calgary, with expertise in AI, data science, system control and modeling, and optimization. Supported by DSI and AI4PH, his current research aims to enhance early diabetes detection and prevention by integrating multidimensional data through advanced data science methods, grounded in a well-articulated model of predictive, prescriptive, preventive, personalized, and participatory (P5) medicine. Mohammad’s research centers on health informatics, health data science, health equity, digital health, environmental health, and intelligent transportation systems.

Gemma Postill is an MD/PhD student at UofT under the co-supervision of Dr. Laura Rosella and Dr. Barbara Haas. Her thesis focuses on using machine learning to anticipate the long-term outcomes of older adults after traumatic injury. Gemma completed her undergraduate at Western University in One Health, which inspired her to become a clinician-scientist by highlighting the intersection of research and medicine as well as the need to take a systems-level approach to healthcare given the many factors that influence health. Gemma is also the student education co-lead with Temerty Center for Artificial Intelligence Research and Education in Medicine (T-CAIREM). 

Yu Shi is a third-year PhD student in Biostatistics at the University of Toronto. Her research focuses on developing and applying advanced machine learning and statistical techniques to address challenges related to out-of-distribution data with their applications to health research.

He Zhu is currently a PhD student at McGill University. He is deeply passionate about exploring electronic health records and single-cell genomic data, driven by his fervour for advancing data-driven healthcare solutions. His current research mainly focuses on mitigating the covariate shift challenges within healthcare-focused federated learning environments.

PROJECT TITLE: Investigating ensemble methods to improve risk estimates for chronic disease in Canadian primary care: an equity-based case study with diabetes complications prognostic prediction models

Jason is an epidemiology and biostatistics PhD student based at the Centre for Health Informatics at the University of Calgary. His research focuses on evaluating and improving prediction models using statistical and machine learning methods to advance their implementation in clinical care and decision-making. Jason is fascinated by the powerful insights that can be derived from health data and the methods used to unlock such impactful knowledge, including artificial intelligence. He holds a master’s degree in epidemiology and biostatistics from Western University and has worked extensively in analysing health care data from diverse settings, including the Canadian Institute for Health Information.

PROJECT TITLE: A public health solution to inequitable (in)fertility care: Exploring the opportunities and ethical considerations of using AI technology (in)fertility care

Esther is a second year PhD student in Social and Behavioural Health Sciences at the Dalla Lana School of Public Health, University of Toronto. She completed a master’s in Health Science (Bioethics) at the University of Toronto and an Honours bachelor’s in Health Science (specialization) at Western University as a Western Scholar. Esther’s research interests lie at the intersection of women’s health, innovative technologies, and bioethics. As part of her doctoral research, Esther will be exploring the ethical considerations in the use and design of AI-enabled digital and reproductive technologies in (in)fertility care.

PROJECT TITLE: Guiding Ethical Design and Usage of Voice-Based Virtual Conversational Agents in Digital Mental Healthcare: Fostering Responsible AI Therapeutic Support

“Zoha is currently in the second year of her Master of Science in Health Sciences program at Simon Fraser University. As a member of the Voice as a Biomarker Consortium, a U.S. National Institutes of Health-funded initiative under the Bridge to Artificial Intelligence (AI) program, her thesis focuses on proactively creating a consensus-driven normative governance framework for the ethical design and implementation of voice-based AI-powered virtual conversational agents (VCAs) utilized for monitoring, diagnostic, and therapeutic purposes.
Zoha is exhilarated to be joining the AI4PH Health Research Training Platform (HRTP) where she hopes she will learn from and grow amongst adroit members, as well as build capacity in designing and implementing innovative and equitable AI in the public health sector.

PROJECT TITLE: Determinants of vaping cessation among youth e-cigarette users

Anasua is a PhD student at Institute of Medical Science, University of Toronto. She has completed a Master’s in Family and Community Medicine with a Collaborative Specialization in Addiction Studies from Dalla Lana School of Public Health. She also has a MBBS degree and served as a physician for five years in Bangladesh. Affiliated with Ontario Tobacco Research Unit, she also has multiple tobacco and substance use related publications in high-impact journals. Her PhD research involves machine learning-based analysis of longitudinal data to identify predictors of vaping cessation among youth e-cigarette users as well as building an algorithm for a personalized vaping cessation smartphone app.

PROJECT TITLE: Leveraging AI and Mixed Effects Models to Understand SARS-CoV-2 Immunological Responses Amongst People with HIV: A Canadian Public Health Perspective

Chapin Korosec completed his PhD with Nancy Forde at Simon Fraser University where he focused on modelling and engineering of artificial molecular motors. He is currently an NSERC postdoctoral fellow with Jane Heffernan, co-supervised by Sajjad Ghaemi (National Research Council of Canada), at York University working to understand within-host disease dynamics and immunological responses to vaccine and infection through data-driven mathematical modelling and machine learning approaches.

PROJECT TITLE: High-dimensional Mediation Analysis Methods for Identifying Causal Pathways from COVID-19 Related Policy Changes to Radiotherapy Adverse Events

Zijin is a biostatistics PhD student at the Dalla Lana School of Public Health, University of Toronto. He completed his MMath degree in statistics from the University of Waterloo. His research interests focus on integrating ML/AI methods into the causal inference framework and developing Bayesian statistical methodologies for clinical or public health applications.

PROJECT TITLE: Critical Analysis of a Population Segmentation Method and the Associated Health Equity Implications

Jo-Ann Osei-Twum is a PhD candidate at the Dalla Lana School of Public Health, University of Toronto where she specializes in epidemiology with a focus on equitable and effective applications of artificial intelligence and data science for population health.

PROJECT TITLE: How to promote equitable sleep care among people experiencing homelessness: An AI-enabled person-centred computer vision-based solution

Behrad Taghibeyglou is a Ph.D. candidate in biomedical engineering at the University of Toronto. He has a broad educational background, holding dual bachelor’s degrees in biomedical engineering and electrical engineering from Amirkabir University of Technology in Iran and a master’s degree in biomedical engineering from Sharif University of Technology. Behrad’s research focuses on the intersection of biomedical signal processing, machine learning, and computer vision to enhance health outcomes. His current Ph.D. thesis centers on developing digital technologies to monitor sleep in individuals from low socioeconomic status

PROJECT TITLE: Automatic Cyberbullying Detection in the Text-Based Platforms

Mohamad is an MSc student in the department of Computer Science at the University of Regina. His primary research area is developing prediction and classification models using Machine Learning and Deep Learning algorithms. He has collaborated with researchers in various disciplines, including insurance, healthcare, public health, social media, and computer networks. In his recent study, he is leading a project on developing Natural Language Processing for information retrieval and sentiment analysis in text-based services.

PROJECT TITLE: Agent-based modelling of Post COVID-19 Condition (PCC) onset and trajectory using Canadian research data

Erin is a PhD candidate in Epidemiology at the University of Ottawa. Supervised by Dr. Julian Little and AI4PH mentor Dr. Doug Manuel, Erin is applying an agent-based approach to model onset and trajectory of Post COVID-19 Condition (PCC) overtime using clinical and lab-based Canadian research data. Erin is also a data analyst with the Public Health Agency of Canada’s Population Health Modelling Unit, managed by Dr. Rojiemiahd Edjoc.

PROJECT TITLE: Normativity and agency in the development of an AI technology for health: a design ethics case study

Joseph is a PhD student at the Institute of Health Policy, Management and Evaluation at the University of Toronto where his research is supported by a Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship. His research examines the ethics and politics of digital health innovation, with an emphasis on the norms and values that characterize AI design and development processes. In 2021-22 Joseph held the Benita M. Warmbold Fellowship in Health System Innovation at Women’s College Hospital in Toronto, and a Graduate Fellowship in Ethics of AI at the University of Toronto’s Centre for Ethics. In 2018 he was selected as an inaugural scholar in AI Ethics and Health by the University of Toronto’s Joint Centre for Bioethics.

PROJECT TITLE: Towards incorporating accelerometer and Global Positioning System data into population health research: application of Bayesian machine learning algorithm and formalization of selective daily mobility bias.

Hiroshi is a post-doctoral fellow working with Dr. Daniel Fuller at the University of Saskatchewan. His research interest is to develop novel measurements describing lifestyle risk factors, such as physical activity and dietary patterns, using the digital streams of population behaviours generated by location-coded wearable devices and consumer transactions. He obtained a PhD in Epidemiology from McGill University, where he developed novel neighbourhood measures of food purchasing using grocery transaction data in Montreal.

PROJECT TITLE: Designing a computer vision machine learning algorithm to identify hip fractures: a retrospective case study

Carolyn is currently completing her MSc HSR in Health Systems Artificial Intelligence, as well as a Collaborative Specialization in bioethics at the Joint Centre of Bioethics at the University of Toronto. Prior to this, she completed her bachelor’s degree in bioethics and cognitive science at the University of Toronto. Carolyn’s research explores the process of designing an algorithm for use in clinical care, as well as the perceived utility of resources intended to help designers assess the social and ethical impacts of the decisions they make during the algorithm design process.

PROJECT TITLE: Utilizing artificial intelligence to reduce health disparities and promote equity among marginalized populations in the post-COVID era.

Shaina is a CIHR Health System Impact Fellow. Her post-doctoral fellowship is co-funded by the CIHR Institute of Population and Public Health (CIHR-IPPH)/Equitable AI and Public Health Ontario. Her host institute is Public Health Ontario and academic institute is Dalla Lana School of Public Health, University of Toronto. She has a PhD in Computer Science, specialized in AI, natural language processing and deep neural networks. She is also a seasoned data scientist, a scientific editor and a reviewer in peer-reviewed journals. Her research interests are in computer linguistics, social media, and in developing novel language models with a focus in biomedicine and recommender systems.

PROJECT TITLE: Effects of unstructured text data quality on the extraction of chronic disease information from electronic medical records

Joykrishna Sarkar is a PhD student in the Department of Community Health Sciences, Rady Faculty of Health Sciences, at the University of Manitoba in Winnipeg. He is also a data analyst at the Manitoba Centre for Health Policy and affiliated with the George & Fay Yee Centre for Healthcare Innovation. He has extensive experience in analyzing health administrative and survey data and has developed advanced skills in the application of statistical methods for health research. His PhD research focuses on the effects of unstructured text data quality on the extraction of chronic diseases from electronic medical records using natural language processing techniques.

PROJECT TITLE: Understanding, predicting, and preventing mortality from deaths of despair in Canada: a population-based approach

Calvin is a PhD candidate in Epidemiology at the Dalla Lana School of Public Health, University of Toronto. He completed his MSc in Epidemiology and Biostatistics at Western University, and has extensive experience using large datasets to study the epidemiology of musculoskeletal disorders. For his doctoral research, Calvin will leverage machine learning methods and detailed data on social determinants of health to advance the prediction of cause-specific mortality.