Find Academic Supervisors

Sylvester B. AboagyeView profile

Assistant Professor

• Wireless Communications
• Optical Communications
• Optimization Theory
• +3 more

Sylvester B. Aboagye is an Assistant Professor in the School of Engineering at the University of Guelph, Canada. He holds a Ph.D. in Electrical Engineering from Memorial University (supervised by Profs. Telex M. N. Ngatched and Octavia Dobre) and completed a postdoctoral fellowship at York University’s Next Generation Wireless Networks lab under Prof. Hina Tabassum. His research focuses on wireless and optical communications, optimization theory, machine learning, and emerging technologies like RIS and NOMA. He currently serves as an Associate Editor for the IEEE Open Journal of Communications Society and Editor for IEEE Communications Letters. Research interests include: Terrestrial/non-terrestrial integrated sensing-communication systems Physical layer security mechanisms Convex/non-convex optimization for resource allocation Applications of machine learning in wireless networks Recent publications emphasize energy-efficient spectrum partitioning, multi-band wireless networks, and optical STAR-RIS-aided VLC systems. His work bridges theoretical advancements with practical implementations in next-generation communication systems. Awards include the prestigious Governor General’s Gold Medal (2023), David Dunsiger Award, and Provost’s Postdoctoral Fellowship for Black and Indigenous Scholars. He advises undergraduate researchers like Zohaib Saghir and Riley Brownsdon through NSERC programs, and maintains active collaborations across academia and industry.

Joshua August SkorburgView profile

Associate Professor

• AI Ethics
• Bioethics
• Neuroethics
• +8 more

Joshua August Skorburg (Gus) is an Associate Professor of Philosophy at the University of Guelph and Co-Academic Director of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI). He also serves as a Faculty Affiliate at the One Health Institute. Skorburg joined the University of Guelph in 2020, previously holding roles at Duke University, including Adjunct Professor in the Fuqua School of Business and Postdoctoral Associate at the Kenan Institute for Ethics. Education: PhD in Philosophy, University of Oregon (2013–2017) MA in Philosophy, University of Toledo (2010–2012) BS in Journalism, Southern Illinois University Carbondale (2006–2009) Research Interests: Skorburg’s work spans applied ethics (AI ethics, bioethics, neuroethics, business ethics) and moral psychology (virtue theory, philosophy of cognitive science, experimental philosophy). Current projects focus on the ethical implications of generative AI in healthcare, virtue ethics, and the participatory turn in mental health research. He emphasizes interdisciplinary collaboration, integrating insights from psychology, neuroscience, and organizational behavior. Grants: 2024: MITACS Accelerate Grant ($60,000) for AI reducing clinical documentation burdens 2023–2025: New Frontiers in Research Fund ($250,000) for personalized microbial infection detection 2022–2025: OpenAI Grant ($1,000,000) for predicting moral judgments using AI Labs & Affiliations: Skorburg leads CARE-AI and collaborates with Duke University’s Fuqua School of Business. His research often addresses societal challenges at the intersection of technology, morality, and health.

Towhidul IslamView profile

Professor

• Consumer Choice
• Innovation Diffusion
• Machine Learning
• +6 more

Dr. Towhidul Islam is a Professor of Marketing and Consumer Studies at the University of Guelph's Gordon S. Lang School of Business and Economics. He holds a PhD in Management Science from Imperial College London and has held academic roles at institutions including the University of Sydney, Dalhousie University, and the University of Northern British Columbia. His research focuses on consumer decision-making, innovation diffusion, and the application of machine learning to predict behavior. He has authored over 100 scholarly works, including 48 peer-reviewed articles, with an h-index of 33. Education: PhD in Management Science (Imperial College London), MBA (Dhaka University), MSc in Telecommunications Engineering (Sofia University), and a MIT certificate in Machine Learning. Research interests emphasize causal inference, big data analytics, and choice experiments. His work bridges behavioral economics with technology, such as modeling renewable energy adoption and analyzing social media data for causal insights. Grants include SSHRC funding ($880,921 CAD), NIH/NCI collaborations ($3.18M USD), and leadership roles in multidisciplinary teams. He advises students across multiple universities and has served on 14 PhD and 25 MSc committees. Recognition includes University Research Chairs and editorial roles. He leads DataOrbit , advancing decision intelligence through applied research.

Martin EsterView profile

Professor

• Data Mining
• Machine Learning
• Causal Discovery
• +3 more

Martin Ester is a Professor of Computing Science at Simon Fraser University (SFU), Canada. He holds a Ph.D. from ETH Zurich (1990) and a Diplom from the University of Dortmund (1984). His research focuses on Data Mining, Machine Learning, Causal Discovery, and Biomedical Applications. He leads the Ester Lab, co-directs the Databases and Data Mining Laboratory, and is part of the Omics Data Science Research Cluster. Education: Ph.D. Computer Science (ETH Zurich), Diplom (University of Dortmund) Lab affiliations: Ester Lab, Databases and Data Mining Laboratory Key research areas: Causal pattern discovery, transfer learning, network analysis, and drug response prediction His work bridges machine learning and biomedical challenges, employing probabilistic graphical models and deep neural networks. Notable achievements include patient stratification algorithms and drug response prediction frameworks. He has advised numerous graduate students, including recent Ph.D. recipients Ali Arab, Amirreza Kazemi, and Mehrdad Mansouri. In 2023, he was named a Fellow of the Royal Society of Canada.

Connie KoView profile

Adjunct Professor

• LiDAR Data Processing
• 3D Object Detection
• Geographic Information Systems
• +4 more

Connie Ko is Adjunct Professor in the Faculty of Environmental and Urban Change at York University, where she serves as GIS/Remote Sensing Technician and Research Associate. Her research focuses on advancing LiDAR data processing techniques and developing AI applications for 3D object detection in environmental contexts. Education credentials: PhD in Earth and Space Science & Engineering (York University, 2014) MSc in Geography (York University, 2004) Bachelor in Environmental Science (York University, 2001) Research specializes in: AI-driven LiDAR analysis for environmental mapping 3D object detection methodologies Tree genus classification using geometric features Large-scale dataset creation for machine learning Publications demonstrate consistent focus on LiDAR applications (80% of works), particularly in forestry and hydrological contexts. Recent shift toward AI integration evident since 2018. Authored foundational datasets like YUTO Tree5000 and Yuto Semantic to support geospatial AI development. Teaches GIS/Remote Sensing techniques and mentors students in spatial analysis. Transformed GIS computer lab into collaborative learning hub supporting interdisciplinary environmental research.

Zhenhao LiView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Software Reliability
• +2 more

Zhenhao Li is an Assistant Professor at York University's School of Information Technology specializing in AI-powered software engineering tools. His research focuses on improving software development processes through artificial intelligence, with publications in premier venues including ICSE, ASE, TSE, and TOSEM. Education PhD in Computer Science, Concordia University (2022) BEng in Software Engineering, Harbin Institute of Technology (2017) Research Focus Dr. Li develops innovative AI techniques for software engineering challenges including code generation, program repair, vulnerability detection, and logging optimization. His work integrates machine learning with software development practices to enhance reliability and efficiency. Publication Trends Recent articles demonstrate a consistent focus on large language model applications in software engineering, particularly for code generation, logging optimization, and vulnerability management. Methodologies include empirical studies, tool development, and literature reviews. Awards ACM SIGSOFT Distinguished Paper Award (ICSE 2024) FRQNT Doctoral Research Scholarship

Tiago MestreView profile

Associate Professor

• Parkinson's Disease
• Huntington's Chorea
• Neurosciences
• +5 more

Tiago Mestre is an Associate Professor in the Department of Medicine at the University of Ottawa and a Neurologist at The Ottawa Hospital. He holds dual appointments as a Scientist in the Neuroscience Program at the Ottawa Hospital Research Institute (OHRI) and as a researcher at the uOttawa Brain and Mind Research Institute. His clinical practice focuses on advanced Parkinson's Disease (PD) management, including Deep Brain Stimulation and Duodopa therapies. His research spans clinical trials in movement disorders, drug development for non-motor PD symptoms, and optimizing clinical trial methodologies from preclinical to confirmatory stages. He leads studies on placebo effects in PD, PD clinical phenotyping, and predictive modeling for disease incidence. Mestre also pioneers the Integrated Parkinson Care Network (IPCN), a chronic disease management framework applicable to neurodegenerative disorders. Key affiliations include OHRI's Neuroscience and Clinical Epidemiology Programs (cross-appointed), and leadership in The Ottawa Hospital's Deep Brain Stimulation Program. His work emphasizes translational research collaborations across basic science and clinical domains.

Huapeng WuView profile

Professor

• Robotics
• Nuclear Engineering
• Control Systems
• +3 more

Huapeng Wu is a Professor at the University of Windsor specializing in robotics, nuclear engineering, and control systems. His research focuses on advanced robotics for fusion reactor maintenance, remote handling systems, and intelligent control algorithms. He has secured significant funding, including a $383k grant for cybersecurity in autonomous vehicles (2020). Key projects include development of the CFETR remote handling system, deformation modeling of fusion manipulators, and EEG-based human-machine interfaces. Research interests span parallel mechanisms, cognitive load monitoring, and bionic robotics. Notable contributions include: Design of vacuum vessel assembly robots with anti-vibration features Development of neural network-based fault prediction frameworks Integration of digital twin technology for fusion maintenance systems His work combines mechanical engineering principles with AI-driven solutions for complex industrial challenges. Current efforts emphasize improving safety and precision in nuclear fusion environments through innovative robotic systems and predictive modeling.

Hamid UsefiView profile

Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Hamid Usefi is a Professor of Mathematics and Statistics at Memorial University of Newfoundland, with a cross-appointment to the Department of Computer Science. He earned his PhD in Mathematics from the University of Western Ontario in 2006, followed by postdoctoral fellowships at the University of British Columbia and the University of Toronto. His research focuses on algebra, machine learning, cryptography, and their applications in bioinformatics, genetics, finance, and industry. He has organized sessions on machine learning at Canadian Mathematical Society meetings and serves as an editor for Nature: Scientific Reports . Dr. Usefi's research interests include deep learning, computer vision, and medical imaging, alongside theoretical algebraic studies. His work bridges pure mathematics and applied domains, with contributions to biomarker discovery, CO2 adsorption modeling, and disease prediction using genomic data. He has been recognized with an NSERC Postdoctoral Fellowship and a MITACS NCE Fellowship. As an educator, he has supervised numerous graduate and undergraduate students in mathematics, statistics, and computer science, mentoring over 20 students since 2010. His publications span journals like Computational Statistics & Data Analysis , Scientific Reports , and Journal of Algebra , demonstrating expertise in both theoretical and applied research. Awards: NSERC Postdoctoral Fellowship (UBC), MITACS NCE Fellowship (University of Toronto) Service: Guest Editor for Scientific Reports (2024 collection on population health modeling) Conferences: Co-organizer of CMS sessions on mathematics of machine learning (2021–2024)

Nachiket KapreView profile

Associate Professor

• Concurrent and Spatial Architectures
• Parallel Processing
• Heterogeneous Architectures
• +4 more

Nachiket Kapre is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada (2023–present). He previously held positions as Associate Professor (2016–2021) and on leave as Research Director at Xilinx Labs (AMD Research, Singapore, 2022–2023). Before that, he was an Assistant Professor at Nanyang Technological University (NTU), Singapore (2012–2016) and a Junior Research Fellow at Imperial College London (2010–2012). He earned a Ph.D. and two M.S. degrees from the California Institute of Technology (2010), and a B.E. from the University of Pune (2002). His research focuses on Concurrent and Spatial Architectures , Parallel Processing , and Communication-Centric Design , with a strong emphasis on FPGA-based acceleration for applications like machine learning, graph algorithms, and embedded systems. Key contributions include Hoplite NoC architectures and CaffePresso for deep learning acceleration. Notable awards include the FPT Best Paper Award (2024), TRETS Best Paper Award (2023), and the CASES 2016 Best Paper Award. He has led multiple grants, including NSERC Discovery Grants and A*STAR-funded projects. His advising spans 15+ students across PhD and MSc levels, contributing to FPGA toolflows, NoC design, and hardware acceleration. He has authored over 50 publications in top venues like FPGA, FPL, and ACM TRETS, and serves as a program chair for FCCM 2023. His work bridges theory and practice, emphasizing energy-efficient computing and reconfigurable systems.

Katherine DuncanView profile

Associate Professor

• Perception
• Cognition
• Cognitive Neuroscience
• +5 more

Katherine Duncan is an Associate Professor and Associate Chair, Undergraduate in the Department of Psychology at the University of Toronto's St. George campus. Her research focuses on the dynamic neurobiological mechanisms underlying memory formation and retrieval, particularly the interplay between hippocampal networks and neuromodulatory systems. Education: PhD in Psychology from New York University (hippocampal subfield specialization) Postdoctoral Research at Columbia University (hippocampus-dopamine interactions) Undergraduate Alumna of the University of Toronto Research Focus: The Duncan Lab investigates how slow neurochemical fluctuations and brain oscillations (particularly theta rhythms) temporally structure memory processes. Their work explores state-dependent learning, age-related changes in network specialization, and the clinical implications for attention disorders and neurodegenerative conditions. Scientific Recognition: Canada Research Chair renewal Glushko Senior Thesis Award (advised student Tien Yang) Banting Postdoctoral Fellowship (lab alumnus Alexandera Decker) NSERC and Ontario Graduate Scholarships (students Matt, Mila, Ethina Islam) Research Trends: Recent publications emphasize three key areas: 1) attentional modulation of memory encoding across developmental stages, 2) computational approaches to neural state segmentation in fMRI, and 3) rhythmic dynamics in memory acquisition across health and disease. These works bridge basic cognitive neuroscience with translational applications. Laboratory Team: The lab includes graduate students Mila Mac Neil, Thomas, Catalina, and Robyn, with alumni transitioning to prestigious positions like Tien Yang's PhD at the University of Michigan and Sanjivan Loganathan's Masters at Western University's Culham Lab. Dr. Connie Solidate currently contributes as visiting scholar.

Shengrong BuView profile

Associate Professor

• Smart Grids
• Wireless Network Security
• Deep Reinforcement Learning
• +3 more

Shengrong Bu is an Associate Professor at the Department of Engineering, Brock University, Canada. She holds a Ph.D. in Electrical and Computer Engineering from Carleton University and has held academic positions at the University of Glasgow and industrial roles at Huawei Technologies. Her research bridges smart grid communications, wireless network security, and deep reinforcement learning applications. Ph.D., Electrical & Computer Engineering, Carleton University MEng by Research, Electrical Engineering, University of Wollongong BEng, Mechanical Engineering & Automation, Huazhong University Research focuses on: Multi-vector energy microgrids and smart grid communications Deep reinforcement learning for network optimization Game theory applications in energy systems Big data analytics for grid resilience Articles show expertise in P2P energy trading, fog computing resource allocation, and wireless security in smart grid environments, with funding from EPSRC and NSERC. Awards include multiple IEEE best papers and prestigious research fellowships. She supervises Ph.D. and MSc students in energy systems research.

Jeff CluneView profile

Professor

• Artificial Intelligence
• Machine Learning
• Neuroevolution
• +5 more

Jeff Clune is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Canada, and a Canada CIFAR AI Chair at the Vector Institute. He also serves as a Senior Research Advisor at DeepMind. His work focuses on advancing artificial intelligence through evolutionary computation, neuroevolution, and open-ended systems. Clune's research explores topics such as continual learning, generative models (e.g., Genie), and automated scientific discovery. He is known for developing algorithms like Go-Explore and pioneering methods in neuroevolution to create robust and adaptable AI systems. His academic affiliations include the UBC Faculty of Science and collaborations with industry leaders like DeepMind. Clune teaches courses on machine learning and data mining, emphasizing practical applications and theoretical foundations. His awards include the SIGEVO Impact Award and recognition for outstanding publications in AI research. Notable projects include the development of the Go-Explore algorithm and contributions to AI safety and ethical frameworks. Clune's work bridges biological inspiration with machine learning, aiming to create systems that evolve and learn like natural organisms. Research interests span AI safety, generative AI, robotics, and scalable learning algorithms. He has published extensively on topics like neural network dynamics, open-endedness, and automated experimentation. Current projects include enhancing AI's ability to self-improve and exploring ethical implications of advanced AI systems.

Ali BashashatiView profile

Associate Professor

• Machine Learning
• Cancer Genomics
• Computational Biology
• +3 more

Dr. Ali Bashashati is an Associate Professor at the University of British Columbia (UBC), affiliated with the School of Biomedical Engineering and the Department of Pathology and Laboratory Medicine. He leads the Artificial Intelligence in Medicine (AIM) Lab, focusing on advancing healthcare through AI-driven solutions in computational pathology and omics data integration. His research combines machine learning, signal processing, and software engineering to address challenges in cancer diagnosis and treatment, particularly in breast, ovarian, and lymphoid cancers. Dr. Bashashati’s work emphasizes multi-modal data analysis, including histopathology imaging and genomics, to improve cancer subtyping and treatment stratification. He has pioneered frameworks like VOLTA for context-aware cell representation learning, enhancing AI models’ performance in histopathology analysis. His contributions to cancer genomics include studies on clonal evolution, tumor heterogeneity, and genomic instability mechanisms. He is the principal investigator of the NSERC CREATE MUSIC training program, a $1.65M initiative fostering next-generation biomedical engineers skilled in AI, biotechnology, and disease biology. This program integrates courses on medical imaging data analysis, applied machine learning, and multi-scale computing for healthcare innovation. Awarded the NSERC CREATE Grant, Dr. Bashashati’s lab collaborates with industry and academia to translate AI innovations into clinical practice, with over 200 peer-reviewed publications in top-tier journals like Nature and Nature Genetics. His team’s work spans from fundamental research to applied tools for precision oncology.

Vladimir MakarenkovView profile

Professor

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +6 more

Roles and Affiliations: Full Professor of Computer Science at Université du Québec à Montréal (UQAM), Director of the DESS in Bioinformatics program. Regular member of LaCIM and affiliate member of Mila (Quebec AI Institute). Served as Assistant Professor (2001–2006) and Associate Professor (2006–2011) before becoming Full Professor in 2011. Education: B.Sc./M.Sc. in Applied Mathematics and Computer Science, Moscow State University (1987–1992) DEA in Computer Science, University Paris V (1993–1994) Ph.D. in Computer Science and Mathematics, École des Hautes Études en Sciences Sociales (EHESS), Paris (1994–1997) Research Interests: Focuses on machine learning applications in bioinformatics, including clustering algorithms, phylogenetic analysis, and genomics. Specializes in CRISPR/Cas9 off-target prediction, SARS-CoV-2 origin analysis, and high-throughput screening (HTS) data correction. Develops tools like T-REX web server for phylogenetic analysis and DUNEScan for skin cancer uncertainty estimation. Integrates deep learning and reinforcement learning for clustering and medical diagnostics. Publications and Grants: Over 100 peer-reviewed articles spanning bioinformatics, AI, and computational biology. Key grants include projects on HTS data correction, phylogenetic networks, and machine learning for biomedical applications. Collaborates with institutions like Mila and Université de Montréal. Labs and Teams: Leads research teams at UQAM’s Department of Computer Science, focusing on interdisciplinary projects combining AI and life sciences. Maintains collaborations with international groups in computational biology and data science.