Dr. Hanreceived her PhD in Applied Mathematics from Purdue University (West Lafayette, USA) in 2018 under the supervision of Prof. Zhilan Feng. She is now an IDRC-sponsored postdoctoral fellow working on COVID-19 transmission modeling in Africa.
Mathematical epidemiology, Dynamical systems
Dr. Zahra Movahedi Nia is a postdoc at York University. Her research interests include data modeling, system identification, data mining, machine learning, and deep learning. She is currently working on modeling and identifying factors of economic damage caused by COVID-19 pandemic using machine learning and data analyzing techniques.
Dr. Xu holds a Ph.D. in Mining Engineering from the University of British Columbia, complemented by a cross-disciplinary background in Machine Learning, Minerals Processing, and Mechanical Engineering. He has seven years of research experience in ore sorting integrated with Machine Learning and Data Analysis.
Artificial Intelligence (AI) in Environmental Applications; Methane Emissions Detection and Quantification
Dr. Gelan Ayana is Provost’s Postdoctoral Fellow of the Artificial Intelligence and Mathematical Modeling Lab (AIMM Lab) at the Dalla Lana School of Public Health in the department of Public Health Sciences, University of Toronto, working on developing AI tools for community-based disease surveillance in low-resource settings. Prior to that, he was a Postdoctoral Researcher in the department of Medical IT Convergence Engineering at Kumoh National Institute of Technology, a position he assumed shortly after completing his doctoral studies in Medical IT Convergence Engineering from the same institution in the Republic of Korea in 2023. Dr. Ayana earned his Bachelor of Science and Master of Science in Biomedical Engineering with high honors from Jimma University in 2015 and 2018, respectively. Dr. Ayana’s research interests encompass AI for health, health informatics, community health, disease modeling, disease surveillance, public health data governance and policy, digital health, global health, and health disparity research. He utilizes mathematical and computational tools for an in-depth analysis of public health data and the development of innovative solutions for healthcare challenges to informing policy. He holds memberships in several prestigious organizations, including the IEEE, EMBS, AACR, EACR, ASLM, Black in AI, AIGH, AI4PEP, ACADIC, and others. Gelan also contributes as a guest editor and session chair for major conferences and as an expert reviewer for Grand Challenges Africa. With a strong academic background, including postdoctoral research, he actively participates in mentoring graduate students and international fellows.
AI for health, health informatics, community health, disease modeling, disease surveillance, public health data governance and policy, digital health, global health, and health disparity research.
Dr. Abbas Yazdinejad is a Postdoctoral Scholar at Artificial Intelligence and Mathematical Modeling lab (AIMMlab) at the Dalla Lana School of Public Health at the University of Toronto, ON, Canada. His research primarily focuses on Artificial Intelligence and Machine learning for critical applications. He hold a Ph.D. in Computational Sciences from the University of Guelph, and bachelor’s and master’s degrees in Computer Engineering, with specializations in Autonomous Cybersecurity, Applied AI and ML in cybersecurity, Privacy-Preserving ML, Federated Learning (FL), Internet of Things (IoT), Industrial IoT (IIoT), Data Security and information management, and software-defined networks (SDN).
Artificial intelligence (AI), Machine learning (ML), Federated Learning (FL), Autonomous Cybersecurity,
Blockchain Security, IoT/IIoT, Software-Defined Networking (SDN)