Find Academic Supervisors

Mark CoatesView profile

Professor

• Machine Learning
• Signal Processing
• Artificial Intelligence
• +3 more

Mark Coates is a Professor in the Department of Electrical & Computer Engineering at McGill University, specializing in Machine Learning and Signal Processing. His research focuses on learning in networks, robust algorithms, and applications such as breast cancer detection, sensor networks, and communication systems. He holds a B.E. from the University of Adelaide and a Ph.D. from the University of Cambridge. His postdoctoral work at Rice University furthered his expertise in distributed systems and signal processing. Education: B.E. in Computer Systems, University of Adelaide, Australia (1995) Ph.D. in Engineering, University of Cambridge, UK (1999) Research Interests: Machine Learning and Bayesian Inference Particle Filtering and Sequential MCMC Graph Convolutional Neural Networks Microwave Breast Cancer Detection Distributed Estimation and Tracking Coates leads a research group exploring cutting-edge technologies in signal processing and AI, with notable contributions to graph-based learning, particle flow algorithms, and healthcare applications. His work bridges theoretical foundations and practical implementations, including software tools for particle filtering and network analysis.

Andrew TritesView profile

Professor

• Marine Mammal Ecology
• Pinniped Biology
• Conservation Physiology
• +3 more

Andrew Trites is a Professor and Director of the Marine Mammal Research Unit at the University of British Columbia's Institute for the Oceans and Fisheries, with cross-appointments in the Department of Zoology. He leads a comprehensive research program on marine mammal ecology, including captive studies, field research, and simulation modeling of pinnipeds and cetaceans. Trites has studied marine mammals for over 30 years, focusing on Steller sea lions, northern fur seals, harbor seals, and killer whales. His work integrates ecology, nutrition, physiology, and behavior to address conservation challenges and human-wildlife conflicts. Current projects investigate diving physiology, foraging ecology, population dynamics, and anthropogenic impacts on marine mammals. Honors include the President's Award for Public Education through Media (2025), King Charles III Commemoration Medal (2025), and Timothy R. Parsons Medal (2018) for distinguished contributions to ocean sciences. Trites has published over 240 scholarly works and advises governmental agencies on marine conservation policy.

David RolnickView profile

Assistant Professor

• Machine Learning
• Climate Change
• Environmental Science
• +3 more

David Rolnick is an Assistant Professor and Canada CIFAR AI Chair at McGill University's School of Computer Science and Mila – Quebec AI Institute. His research focuses on applying machine learning to address climate change challenges, including biodiversity monitoring, land use classification, climate modeling, and materials discovery. He co-founded Climate Change AI and leads initiatives like the NSF-NSERC Global Center on AI and Biodiversity Change (ABC). Rolnick's work emphasizes developing transfer learning and meta-learning techniques to handle sparse data in climate-related applications, such as predicting energy demand and simulating cloud formations. He has been recognized with the Sloan Research Fellowship and MIT Technology Review's '35 Innovators Under 35' distinction. Education: Postdoctoral research at the University of Pennsylvania (NSF Mathematical Sciences Postdoctoral Fellow), PhD from MIT (NSF Graduate Research Fellow). Research interests span AI applications in climate science, including energy systems optimization, satellite imagery analysis for environmental monitoring, and causal representation learning in climate data. His lab explores ethical AI deployment, particularly mitigating negative applications like oil exploration while addressing energy consumption concerns in ML. Key projects include ClimART (a climate radiative transfer benchmark), OpenForest (a forest monitoring data catalog), and collaborations on fusion energy modeling. Awards include the AI2050 Fellowship for transformative research impact. Awards: Sloan Research Fellow, AI2050 Fellow, MIT TR35 Grants: NSF-NSERC Global Center, Climate Change AI initiatives Labs/Teams: McGill Climate AI Group, Mila collaborations

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

Xi Chen is an Adjunct Professor at the School of Computer Science, McGill University (2020–2023). He holds a PhD from McGill University, and M.Eng. and B.S. degrees in Electronic Engineering from Shanghai Jiao Tong University. Currently, he is a Lead AI Researcher at Palona AI, focusing on agentic AI systems. Research Interests : Chen’s work spans AI domains including federated learning, machine learning foundations, computer vision, 5G/6G communication systems, autonomous driving, and smart IoT. His research emphasizes practical applications of AI in communication networks and edge computing environments. Key themes include model efficiency, domain adaptation, and privacy-preserving techniques. Publications : His recent work explores federated unlearning (CVPR 2025), query-efficient federated learning (ICML 2025), and domain generalization via Hessian alignment (ICCV 2023). He has received Best Paper Awards for contributions to vehicular localization (IEEE MASS 2019) and 5G edge networks (Globecom 2021). Awards & Contributions : Recognized for innovations in federated learning and wireless systems, including foundational papers on beam alignment and network optimization. Active in conference TPC roles (AAAI, NeurIPS, INFOCOM) and industry collaborations with Huawei, Samsung, and Nuance. Labs & Projects : Leads the VSmart vehicular testbed and Aerial sensing project, advancing device-free localization and smart IoT applications. Involved in industry-driven initiatives like AI-5G and AI-IoT at Samsung AI Center.

Wenbo HeView profile

Assistant Professor

• Wireless Sensor Networks
• Mobile and Pervasive Computing
• Security and Privacy
• +3 more

Wenbo He is an Assistant Professor in the School of Computer Science at McGill University. Previously, he held academic positions at the University of Nebraska-Lincoln (EE department, 2010-2011) and the University of New Mexico (CS department, 2008-2010). He earned his Ph.D. from the MONET Group at the University of Illinois at Urbana-Champaign in 2008. His research focuses on Wireless Sensor Networks, Mobile and Pervasive Computing, Security and Privacy, Cloud Computing, and Cyber-Physical Systems. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2008) Research Interests: Designing secure and efficient protocols for wireless networks Privacy-preserving data aggregation in sensor networks Energy-efficient systems and fault-tolerant architectures Adversarial analysis in mobile and cloud environments Awards: Best Paper Award at ACM Conference WiSec'11 Best Paper Award at IEEE Transactions on Industrial Informatics (2008) Mavis Memorial Fund Scholarship Award (2006) C. W. Gear Outstanding Graduate Award (2007) Vodafone Fellowship (2005-2008) NSF TRUST Fellowship (2007 and 2009) Advising & Grants: While specific grants are not listed, his research has been supported by prestigious fellowships such as the NSF TRUST and Vodafone. He has advised/co-authored numerous papers with colleagues and students in networking and security domains. Labs/Teams: His work integrates interdisciplinary approaches, often collaborating with teams focused on cyber-physical systems and real-time computing.

Mohit BhardwajView profile

Researcher

• Multi-messenger Astronomy
• RF Electronics and Communication
• Applied Statistics
• +3 more

Mohit Bhardwaj is a McWilliams Postdoctoral Researcher in the Department of Physics at Carnegie Mellon University (CMU), specializing in multi-messenger astronomy and radio transient studies. His research focuses on Fast Radio Bursts (FRBs), particularly their origins, host galaxies, and multi-wavelength follow-up. He is a core member of the CHIME/FRB collaboration, leading efforts to analyze FRB data from the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Bhardwaj holds a PhD in Physics from McGill University (2022), an M.Sc. (Hons.) in Physics from Birla Institute of Technology and Science (2017), and a B.E. in Electrical and Electronics Engineering from the same institute (2017). His work bridges astrophysical instrumentation, signal processing, and computational methods. Education: PhD in Physics, McGill University (2022) M.Sc. (Hons.) Physics, BITS Pilani (2017) B.E. Electrical & Electronics Engineering, BITS Pilani (2017) Research Interests: Mohit’s work centers on FRB progenitor theories, host galaxy associations, and the design of radio observatories. He develops algorithms to analyze CHIME’s unprecedented FRB catalog, focusing on polarization properties, temporal structures, and multi-messenger counterparts. Recent projects include studying repeating FRBs in globular clusters and low-luminosity dwarf galaxies, as well as designing real-time alert systems like frb-voe. Key Contributions: He co-led the discovery of nearby FRBs (e.g., FRB 20240114A) and their host galaxy associations. His technical work includes optimizing CHIME’s outrigger systems and improving FRB localization precision via VLBI. He has also explored connections between FRBs and phenomena like supernovae and neutron star mergers. Labs/Teams: Active in the CHIME/FRB collaboration, leveraging its interferometric capabilities for high-time-resolution studies. Collaborates with institutions globally to pursue multi-wavelength observations and theoretical modeling of FRB environments.

Tasneem DarwishView profile

Assistant Professor

• Intelligent Network Management
• Satellite Networks
• Software Defined Networks (SDNs)
• +6 more

Tasneem Darwish is an Assistant Professor in the Department of Computer Science at Saint Francis Xavier University. Their research focuses on intelligent and autonomous network management, including software-defined networks (SDNs), network function virtualization (NFV), satellite networks, 5G/6G technologies, vehicular ad hoc networks (VANETs), IoT, blockchain, and network security. Their work emphasizes future network architectures and protocols for emerging technologies like LEO satellites and edge computing. Research interests include optimizing network performance through graph-based frameworks, latency reduction in distributed systems, and secure communication protocols. They have contributed to interdisciplinary areas such as smart transportation systems via vehicular networks and high-altitude platform stations (HAPS) for enhanced connectivity. Publications highlight trends in satellite-based communication infrastructure, self-evolving network management systems, and challenges in integrating emerging technologies into existing frameworks. Their work bridges theoretical advancements with practical implementations in next-generation networks. No scientific awards or grants are explicitly mentioned in the provided texts. Advising responsibilities and lab affiliations remain unspecified.

• Healthcare Operations
• Business Analytics
• Operations Research
• +3 more

Majid Taghavi is an Associate Professor at the Sobey School of Business, Saint Mary's University, with adjunct roles at Dalhousie University’s Department of Industrial Engineering and School of Health Administration. His expertise spans healthcare operations and business analytics, focusing on optimization models in healthcare logistics, machine learning applications, and operations research. Education: Ph.D. in Management Science (McMaster University, 2015), M.Sc. in Industrial Engineering (2009), B.Sc. in Applied Mathematics (2003) His research integrates operations research techniques with healthcare challenges, including vaccine distribution optimization, hospital discharge prediction, and palliative care workforce planning. He also explores sustainable transportation solutions like electric vehicle charging infrastructure planning. Recent work emphasizes machine learning for healthcare decision-making, such as identifying risk factors for hospital readmissions and predicting patient no-shows in emergency departments. His interdisciplinary approach bridges technical models with real-world healthcare systems. Awards: None explicitly listed in provided texts Advising and grants: No specific students or grants mentioned, but his roles imply active research collaboration. He contributes to policy-oriented studies like alternate level of care reviews and palliative care workforce planning.

Bruce MinakerView profile

Professor

• Vehicle Dynamics
• Suspension Systems
• Electric Powertrains
• +3 more

Bruce Minaker is a Professor at the University of Windsor's Faculty of Engineering, specializing in Mechanical, Automotive, and Materials Engineering. His research focuses on vehicle dynamics, suspension systems, and electric powertrain technologies. He is renowned for developing innovative suspension designs, including a patented multilink front suspension for motorcycles. His work integrates advanced control systems and optimization techniques to enhance vehicle performance and safety. Minaker leads collaborative projects with institutions like Coventry University, advancing software tools for teaching vehicle dynamics. He has mentored students in high-profile competitions, such as the Electric Vehicle Grand Prix, where his teams achieved top rankings. His academic contributions include co-authoring a textbook on vehicle dynamics and pioneering open-source simulation tools for automotive education. Key research areas include active suspension systems, multibody dynamics, and the application of genetic algorithms for electric vehicle optimization. His work addresses challenges in ride quality, handling, and energy efficiency, with a focus on practical applications in both traditional and electric vehicles. Minaker's research group has produced over 40 peer-reviewed articles since 2003, covering topics from tire modeling to linearization methods for complex mechanical systems. His interdisciplinary approach bridges mechanical engineering, control systems, and computational methods, making him a leading figure in automotive dynamics research.

Francesco BiondiView profile

Professor

• Human Factors Engineering
• Automotive Safety
• Cognitive Workload Assessment
• +4 more

Francesco Biondi is a Professor in the Department of Kinesiology at the University of Windsor's Faculty of Human Kinetics. His research focuses on driver behavior, automated vehicle systems, cognitive workload assessment, and workplace ergonomics. He leads the Human Systems Lab, collaborating with industry partners like Tesla and the Windsor Police Service on projects addressing driver distraction, semi-autonomous vehicle safety, and traffic zone analysis. Notable collaborations include studies on distracted driving in school zones and the effects of museum visits on mental clarity. Biondi has received federal research grants and contributed to over 50 peer-reviewed articles. His work emphasizes human-machine interaction challenges in automated systems, including legal liability implications and driver training requirements. Education and Training: Specialization in Human Factors Engineering Advanced training in Ergonomics and Biomechanics Research Interests: Automated vehicle human factors Cognitive workload measurement Driver distraction mitigation Workplace cognitive ergonomics Technology-assisted rehabilitation Recent Article Trends: Focus on real-world validation of automated driving systems, validation of cognitive workload metrics (e.g., pupil size tracking), and interdisciplinary approaches to vehicle automation safety. His team has pioneered camera-based monitoring systems for manufacturing environments and developed novel blink detection algorithms for driver monitoring. Labs/Teams: Leads the Human Systems Lab at UWindsor, collaborating with engineering faculty on cross-disciplinary projects through the WE-Spark Health Institute.

• Artificial Intelligence
• Political Science
• Democracy
• +3 more

Jean-François Gagné is a Lecturer in the Department of Political Science at the University of Montreal's Faculty of Arts and Sciences. He holds a PhD in Political Science from the University of Montreal (2013). His research focuses on comparative analysis of national artificial intelligence strategies and the impacts of technologies on democracy. He is affiliated with the Centre d'études et de recherches internationales (CÉRIUM) and is an associated member of the Centre d'études d'Asie de l'Est (CÉTASE). Previously, he was a researcher at the Raoul-Dandurand Chair in Strategic and Diplomatic Studies and a political analyst at Exportation et Développement Canada. His research projects include 'Les visions du futur de l'intelligence artificielle' (2021-2023), examining socio-technical imaginaries among AI entrepreneurs, and a postdoctoral fellowship supporting Sylvain Munger's work (2020-2023). He has been funded by organizations like the Secrétariat des programmes interorganismes and the Fonds de recherche du Québec - Société et culture (FQRSC). Gagné's publications span hybrid regimes, rentier states, and resource conflicts, with work appearing in journals like Studies in Comparative International Development and Politique et Société . He currently advises on programs and teaches at the University of Montreal while contributing to the Montréal Declaration for Responsible AI's algorithmic governance initiatives.

Ian McLeodView profile

Professor

• Time Series Analysis
• Wavelet Methods
• Data Visualization
• +4 more

Ian McLeod is a Professor in the Department of Statistical and Actuarial Sciences at Western University. He holds a Ph.D. from the University of Waterloo (1977) and conducts research in time series analysis, wavelet methods, data visualization, neural networks, and statistical computing. His work spans traffic safety, climate modeling, and clinical epidemiology, demonstrating interdisciplinary applications of statistical methodologies. His research focuses on developing advanced statistical tools for real-world problems, including traffic legislation impact assessment, climate change analysis, and medical diagnostics. Publications frequently involve collaborative studies with healthcare and environmental science researchers. McLeod has developed specialized R packages for time series analysis and contributed to methodological advancements in ARMA modeling and unit root testing. His recent work includes stroke classification systems and climate impact simulations using multivariate resampling techniques.

Jamileh YousefiView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Decision Support Systems
• +3 more

Jamileh Yousefi is an Assistant Professor in Business Analytics at the Shannon School of Business, Cape Breton University (CBU) since 2019. She holds a Ph.D. in Computer Science from the University of Guelph (2018) and an MSc in Computer Science from Concordia University. Her research focuses on artificial intelligence and machine learning applications in decision exploration systems, particularly emphasizing the explanatory power of AI. Her work spans health analytics, transportation logistics, and data classification methodologies. Dr. Yousefi’s research interests include healthcare disparities analysis (e.g., colorectal and lung cancer studies), freight transportation frameworks, and neuro-fuzzy classifier optimization. She has contributed to advancements in machine learning for skewed datasets and semantic-based question-answering systems. Notably, her 2006 work earned a Best Paper Award for contributions to semantic question reformulations. Her academic contributions bridge technical innovation with practical applications in business analytics, healthcare, and logistics. She actively publishes in interdisciplinary journals and conferences, reflecting her dual expertise in computer science and business informatics.

S. Andrew GadsdenView profile

Associate Professor

• Artificial Intelligence
• Cognitive Systems
• Control Systems
• +9 more

Dr. S. Andrew Gadsden is an Associate Professor and Associate Chair (Graduate Studies) in the Department of Mechanical Engineering at McMaster University, where he leads the Intelligent and Cognitive Engineering (ICE) Laboratory. He holds a BEng/Mgt and PhD from McMaster University, with postdoctoral experience at the Centre for Mechatronics and Hybrid Technology. His research focuses on cognitive systems, control theory, and machine learning applications in smart systems. He has held academic roles at the University of Guelph and the University of Maryland, and collaborates with NASA, the US Army Research Laboratory, and other institutions. His research aims to enhance public trust in cognitive systems through robust estimation strategies and physics-based machine learning integration. He has received prestigious awards, including the NASA Robert H. Goddard Award (2021), SPIE Rising Researcher Award (2019), and Ontario Early Researcher Award (2018). He is a Fellow of ASME and Senior Member of IEEE. Dr. Gadsden teaches courses such as Smart Systems (MECH ENG 4SS3) and supervises graduate students. His lab hosts over 300 peer-reviewed publications and actively engages with industry via startups and advisory roles. Current research includes aerospace instrumentation (air-LUSI project), energy systems, and cognitive robotics.

Roni KatzirView profile

Associate Professor

• Linguistics
• Computational Linguistics
• Cognitive Science
• +4 more

Prof. Roni Katzir is an Associate Professor in the Department of Linguistics and a member of the Sagol School of Neuroscience at Tel Aviv University. His research focuses on the intersection of mathematical/computational methods and linguistic cognition, investigating how humans represent, learn, and use language knowledge for inference and discourse. He holds a BSc in Mathematics from Tel Aviv University and a PhD in Linguistics from MIT. Education: BSc in Mathematics, Tel Aviv University PhD in Linguistics, Massachusetts Institute of Technology (MIT) His key research areas include computational linguistics, cognitive science, and formal semantics. Notable contributions address the computational modeling of grammar induction, the evaluation of large language models against human linguistic cognition, and the application of Minimum Description Length principles in neural networks. He leads the Computational Linguistics Lab and undergraduate program at TAU and teaches in the Adi Lautman Interdisciplinary Program for Outstanding Students. Recent work critically examines Large Language Models (LLMs), arguing they fail to replicate core aspects of human linguistic competence. His research also explores neural network generalization for grammar induction and the typological stability of logical operators. Prof. Katzir has advised numerous collaborative projects and leads initiatives in computational phonology and semantics. His labs focus on explanatory adequacy in language learning and neurosymbolic AI integration.