Find Academic Supervisors

Jennifer RasoView profile

Assistant Professor

• Administrative Law
• Social Protection Law
• Algorithmic Decision-Making
• +3 more

Dr. Jennifer Raso is an Assistant Professor at McGill University’s Faculty of Law, where she specializes in administrative law, socio-legal studies, and the intersection of technology with governance. Her research explores how algorithmic decision-making and data-driven systems impact procedural fairness and justice in public institutions. She holds a SJD from the University of Toronto and practiced law in Toronto before joining academia. Education: SJD, University of Toronto Faculty of Law (2018) LLB, University of Victoria Faculty of Law (2007) Research Interests: Administrative Law in Digital Contexts AI Governance and Ethical Implications Algorithmic Decision-Making in Social Assistance Her work has been funded by SSHRC and the Endeavour Fellowship. Recent publications address digital welfare systems, responsible AI, and bureaucratic accountability. She leads a SSHRC-funded project on the digital transformation of social assistance laws in Canada. Awards: 2018 Best English Article, Canadian Law and Society Association 2016 Richard Hart Prize, Cambridge Public Law Conference Professional Experience: Assistant Professor, University of Alberta Faculty of Law (2018–2022) Postdoctoral Fellow, UNSW Law (2017–2018) Legal Counsel, City of Toronto (2008–2012) Current Projects: Focus on digital governance frameworks, AI in administrative justice, and socio-legal analysis of front-line decision-making in welfare systems.

Wu LinView profile

Research Fellow

• Machine Learning
• Numerical Optimization
• Approximate Inference
• +3 more

Wu Lin is a Research Fellow specializing in computational aspects of differential, geometric, and algebraic structures in machine learning. Their research focuses on geometric methods for numerical optimization and approximate inference, particularly natural-gradient (NG) descent techniques. They investigate adaptive optimization methods, structured optimization, and Bayesian approaches in deep learning. Their work emphasizes practical and numerically-stable algorithms, including memory-efficient implementations and second-order methods. Research interests include exploiting hidden structures and symmetries in ML models, with emphasis on optimization challenges and statistical inference. Key contributions include foundational papers on structured NG descent, Riemannian gradient methods, and variational inference techniques. Their blog series provides accessible introductions to natural-gradient concepts. Publications span top venues like ICML and AI&Stats, addressing topics from curvature learning in neural networks to scalable Bayesian methods. While no institutional affiliation is explicitly stated in the provided text, their work is associated with Toronto, Canada. No awards or grant information is provided here.

• single-molecule biophysics
• intrinsically-disordered proteins
• GPCRs
• +4 more

Claudiu Gradinaru is an Interim Chair and Professor in the Department of Chemical and Physical Sciences at the University of Toronto Mississauga, part of the Faculty of Arts & Science. He holds a Ph.D. in Physics from VU University Amsterdam and has conducted postdoctoral research at Leiden University and Sandia National Laboratories. His research focuses on single-molecule biophysics, intrinsically disordered proteins (IDPs), and G protein-coupled receptors (GPCRs), leveraging advanced microscopy and computational techniques. Education: B.Sc. in Physics (University of Bucharest, 1995), Ph.D. in Physics (VU University Amsterdam, 2001). Roles: Interim Chair, Principal Investigator of the Gradinaru Biophysics Lab, and educator in molecular biophysics, optics, and classical mechanics. Research Interests: His lab employs ultrasensitive optical techniques (e.g., single-molecule fluorescence spectroscopy) to study conformational dynamics of IDPs and GPCRs in live cells. Key areas include: Structural ensembles of IDPs using NMR, SAXS, and smFRET. GPCR oligomerization and signaling dynamics. Development of computational methods for ensemble modeling. Advising: Gradinaru mentors numerous graduate and undergraduate students, including PhD candidates Xiaohan Zhou (GPCR dynamics) and Maria Shemeteva (GPCR signaling), and MSc students Aiyan Brown (smFRET analysis) and Xinge Liu (IDP complexes). His lab has produced over 40 publications since 2001, with recent work featured in Biophysical Journal , Journal of the American Chemical Society , and Nucleic Acids Research . Labs & Instruments: The Gradinaru Lab houses custom-built microscopes for multiparameter single-molecule fluorescence, fluorescence correlation spectroscopy, and TIRF microscopy. Collaborations span institutions in Canada, the U.S., the U.K., and Australia.

Orland HoeberView profile

Professor

• Interactive Information Retrieval
• Information Visualization
• Human-Computer Interaction
• +3 more

Dr. Orland Hoeber is a Professor and Head of the Department of Computer Science at the University of Regina. He holds a Ph.D. in Computer Science from the University of Regina, and his research focuses on developing visual and interactive tools for information exploration in digital libraries and mobile search environments. His work bridges information retrieval, human-computer interaction, and data visualization to support complex search tasks. Education: Ph.D. Computer Science, University of Regina (2007) M.Sc. Computer Science, University of Saskatchewan B.Sc. Mathematics, University of Saskatchewan Dr. Hoeber's research examines how people interact with information systems, with particular emphasis on visual interfaces that support exploratory search behaviors. His lab investigates novel approaches to representing search results, understanding mobile search interruptions, and analyzing social media data for sport analytics. Recent work explores cross-device search patterns in academic contexts and interface designs that facilitate serendipitous discovery. His publications demonstrate a consistent focus on human-centered approaches to information system design, with recent work examining multimodal evaluation techniques and search behavior in digital humanities. Article keywords frequently include information retrieval, visualization, and interactive systems, reflecting his interdisciplinary approach. Dr. Hoeber actively mentors graduate students and postdoctoral fellows in his research group, supervising projects on exploratory search behavior, information visualization, and user interface evaluation. He has served as Associate Head (Graduate) and currently leads the Department of Computer Science.

Samira SadaouiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Dr. Samira Sadaoui is a Full Professor at the University of Regina's Department of Computer Science. She holds a PhD and MSc in Computer Science from the University of Nancy I, France. Her research is supported by NSERC and MITACS grants, focusing on artificial intelligence applications in real-world environments. Dr. Sadaoui's research spans: Adaptive machine learning systems for evolving data streams Fraud detection algorithms in financial and auction systems Evolutionary optimization methods for feature selection Dimensionality reduction techniques for high-dimensional data Reinforcement learning approaches for combinatorial problems Her recent publications demonstrate strong emphasis on: Concept drift detection in noisy environments (2022-2024) Evolutionary computation for feature engineering (2021-2023) Real-world applications in fraud detection and service monitoring (2017-2022) Teaching responsibilities include: CS115: Object-Oriented Design CS230: Fundamentals of Computer Science III CS372: Software Engineering Methodology CS476: Software Development Project CS872: Advanced Software Engineering

Natalia NikolovaView profile

Professor

• Microwave and millimeter-wave imaging and detection
• Inverse problems
• Computational electrodynamics
• +6 more

Natalia K. Nikolova is a Professor and Canada Research Chair (Tier 2) in High-frequency Electromagnetics at the McMaster University Faculty of Engineering, Department of Electrical and Computer Engineering. Author of Introduction to Microwave Imaging (Cambridge University Press, 2017) Develops cognitive radar systems for concealed weapon detection and microwave scanners for breast cancer screening Commercialization with Patriot One Technologies Ltd. Education: Dipl. Eng. (Radioelectronics), Technical University of Varna, Bulgaria (1989) Ph.D. (Electrical Engineering), University of Electro-Communications, Tokyo, Japan (1997) NSERC Postdoctoral Fellowship (1998-1999) Research Focus: Microwave and millimeter-wave imaging systems for biomedical and security applications through computational electromagnetism and inverse scattering analysis. Her Electromagnetic Vision Laboratory (founded 1999) develops high-speed sensor arrays and computer-aided design tools. Scientific Contributions: Elevated to IEEE Fellow (2011) for sensitivity analysis methods Delivered 50+ international lectures as Distinguished Microwave Lecturer (2010-2013) Holder of 7 patents in microwave imaging technology

Yong Hoon KimView profile

Associate Professor

• Connected and autonomous vehicles (CAVs)
• Intelligent transportation systems (ITS)
• Advanced traveler information systems
• +2 more

Yong Hoon Kim is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Windsor, Canada. He specializes in intelligent transportation systems with a focus on connected and autonomous vehicles (CAVs), traffic flow modeling, and cybersecurity in transportation networks. Education : BSc & MSc in Civil Engineering from University of Seoul, South Korea PhD in Civil Engineering from Purdue University, West Lafayette, IN, USA Research Interests : Connected and autonomous vehicles (CAVs) integration in urban environments Intelligent transportation systems (ITS) and traffic flow optimization Cybersecurity challenges in vehicle-to-vehicle communication networks Mobility data privacy and advanced traveler information systems Recent Publication Trends : His recent work (2023) explores vehicle malware modeling, collision-free traffic simulation, and mobility data analysis, while earlier studies focus on CAV infrastructure planning and V2V communication dynamics.

• Statistical Signal Processing
• Pattern Recognition
• Machine Learning
• +3 more

Dr. Miroslaw Pawlak is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba’s Price Faculty of Engineering. He holds a Ph.D. and D.Sc. in Computer Engineering from Wrocław University of Technology, Poland. As a Professional Engineer registered in Manitoba, he has held visiting positions at multiple institutions worldwide, including the University of Ulm and University of Göttingen as an Alexander von Humboldt Foundation Fellow. His research focuses on statistical signal processing, machine learning, nonparametric modeling, and system identification. He authored/co-authored influential books like Nonparametric System Identification (Cambridge University Press, 2010) and over 100 peer-reviewed publications. Dr. Pawlak has served as an Associate Editor for journals such as Pattern Recognition and IEEE Transactions on Information Theory , and was recognized in Stanford University’s World’s Top 2% Scientists list. Education: 1985: Ph.D. in Computer Engineering, Wrocław University of Technology 1999: D.Sc. in Computer Engineering, Wrocław University of Technology Research Interests: Dr. Pawlak specializes in advanced signal processing techniques, including nonparametric system identification, statistical analysis of stochastic signals, and machine learning applications in engineering systems. His work bridges theoretical foundations (e.g., operator theory with Hermite polynomials) and practical implementations (e.g., wind farm modeling and power system stability prediction). Publications: His recent work emphasizes nonparametric methods for system testing (e.g., Hammerstein systems) and interdisciplinary applications like power grid oscillation prediction using LASSO algorithms. He frequently collaborates with international researchers to advance signal processing theory and energy systems modeling. Awards: World’s Top 2% Scientists (Stanford University, 2021/2022) Professional Contributions: Dr. Pawlak’s editorial roles and visiting fellowships demonstrate his global influence. He has advised numerous graduate students and contributed to key textbooks shaping the field of system identification.

Qingjin PengView profile

Professor

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +5 more

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

Masoud AsadzadehView profile

Associate Professor

• Water Resources Engineering
• Integrated Water Management Modelling
• Watershed Modelling
• +4 more

Dr. Masoud Asadzadeh is an Associate Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on water resources engineering, hydrologic modeling, and optimization techniques applied to environmental systems. He holds a Ph.D. from the University of Waterloo and has extensive certifications in teaching and learning. His educational background includes degrees from Isfahan University of Technology (B.Sc.), Amirkabir University of Technology (M.Sc.), and the University of Waterloo (Ph.D.). Additional certifications include a Teaching and Learning Certificate (2019) and a Fundamentals of University Teaching Certificate (2011). Research Interests: His work emphasizes water resources systems analysis using optimization, sensitivity, and uncertainty tools. Specific areas include hydrologic modeling of nutrients/water quality, reservoir operation optimization, and algorithm development for multi-objective problems. Recent projects involve climate change impacts on hydropower systems, isotope tracer-based calibration, and integrated water management. Publications: His recent work spans advancements in multi-objective optimization, hydrologic model calibration, and climate change impact studies. Notable contributions include frameworks for model comparison (Nelson-MiP project) and innovative calibration techniques. Advising/Grants: While currently without graduate openings, his past work includes mentoring students on optimization and modeling projects. His research is supported by grants focusing on environmental systems analysis and water resource sustainability. Labs/Teams: Active in interdisciplinary teams addressing large-scale hydrological challenges, particularly in Canadian prairie and northern Manitoba basins.

Nicolas HudonView profile

Assistant Professor

• Process control and simulation in chemical engineering
• Systems control and automation
• Industrial systems engineering
• +4 more

Nicolas Hudon is an Assistant Professor in the Department of Chemical Engineering at Queen's University, associated with Smith Engineering and the Faculties & Schools. His research focuses on physics-based modeling, estimation, optimization, and control of nonlinear chemical process systems, particularly integrating non-equilibrium thermodynamics into process systems engineering. Education: B.Ing. (2002) and M.A.Sc. (2004) from Polytechnique Montréal, PhD (2010) from Queen's University. Postdoctoral work included University of New South Wales (2010-2012) and Université catholique de Louvain (2012-2015). Research interests include process control, systems engineering, applied mathematics (calculus of variations), and mathematical physics. His work aims to develop distributed control strategies for chemical systems, with applications in tokamak reactors, multiphase systems, and sustainable processes. Affiliations: Canadian Engineering Education Association, American Society for Engineering Education, Canadian Society for Chemical Engineering, IEEE, and SIAM. Teaches courses like CHEE 905 (Advanced Thermodynamics) and CHEE 210 (Thermodynamics of Energy Conversion). Publications emphasize control design for thermodynamic systems, port-Hamiltonian modeling, and educational strategies in chemical engineering problem-solving.

Ken ClarkView profile

Associate Professor

• Physical sciences
• Astronomical and space sciences
• Dark energy
• +3 more

Ken Clark is an Associate Professor and Graduate Chair in the Department of Physics, Engineering Physics and Astronomy at Queen's University, affiliated with the McDonald Institute and TRIUMF. He holds a B.Sc. from the University of Toronto, M.Sc. from Trent University, and Ph.D. from Queen's University. His research focuses on dark matter detection via the PICO experiment and neutrino studies through the IceCube collaboration at the South Pole. He leads construction efforts for the PICO 500 detector and serves as analysis coordinator for the IceCube upgrade. Clark's work bridges particle astrophysics, neutrino physics, and cosmic ray detection. Education: B.Sc., University of Toronto M.Sc., Trent University Ph.D., Queen's University Research Interests: Dark matter detection (PICO experiment), neutrino physics (IceCube collaboration), astroparticle physics, and cosmic ray interactions. Clark collaborates with institutions like the University of Alberta, Northwestern University, and Fermilab. His recent projects include advancing bubble chamber technology and optimizing IceCube's sensitivity to dark matter interactions. He also explores neutrino cross-section measurements and the proposed PINGU sub-detector for enhanced detection capabilities. Joint appointments at TRIUMF provide access to cutting-edge detector development resources. Awards and grants are not explicitly listed, but his work reflects significant contributions to international collaborations. Students and lab teams are engaged in PICO and IceCube experiments, though specific advisees are not detailed here. His research aims to address fundamental questions about the universe's structure and dark matter's role.

• IoT
• Smart Cities
• Digital Twins
• +5 more

Anwar Hossain is an Associate Professor at the School of Computing, part of the Faculty of Arts and Science at Queen's University. His research focuses on IoT, smart cities, healthcare technology, and machine learning applications. He has contributed to advancements in digital twins, edge computing, and railway defect detection using AI-driven methodologies. His work integrates ethical considerations with technical innovation, particularly in healthcare systems and infrastructure maintenance. Research interests span interdisciplinary areas such as multimodal AI for behavioral therapy, generative models for defect analysis, and privacy-preserving data aggregation in smart environments. His publications highlight trends in predictive healthcare systems, IoT-enabled infrastructure monitoring, and cognitive frameworks for urban systems. Notable contributions include frameworks for intelligent edge processing in smart cities and digital twin applications for mental well-being. His articles emphasize practical solutions for challenges in public health, railway safety, and urban sustainability. While no awards are explicitly listed, his extensive publication record reflects academic and industry relevance. Dr. Hossain collaborates on projects involving healthcare CPS (Cyber-Physical Systems), IoT security, and adaptive algorithm recommendation. His work bridges theoretical research with real-world implementations, contributing to both academic discourse and technological innovation in his field.

John LeonardView profile

Professor

• John Milton Studies
• Renaissance/Early Modern Literature
• Literary Criticism
• +3 more

John Leonard is a Professor in the Department of English and Writing Studies at Western University. He holds a BA from Trinity College, Cambridge (1980), an MA from McMaster University (1981), and a PhD from Trinity College, Cambridge (1986). His research focuses on John Milton’s poetry, including editing Paradise Lost for Penguin Classics and authoring Faithful Labourers , a two-volume reception history of Paradise Lost . He teaches across literary periods, emphasizing Renaissance/early modern literature and introductory courses for undergraduates. Leonard’s work integrates close textual analysis with historical and cultural contexts. His publications span Milton’s poetics, reception history, and interdisciplinary topics like disability studies in literature. He maintains an active research agenda, with recent contributions to debates on embodiment, linguistic ambiguity in Paradise Lost , and the intersection of NLP systems with literary analysis. Office: University College 3423 | Contact: 519-661-2111 ext. 85809 | Email: jleonard@uwo.ca

• Robotics
• Human-Robot Interaction
• Control Systems
• +5 more

Brandon J. DeHart is an Adjunct Assistant Professor at the University of Waterloo. He is affiliated with the university's academic faculty and can be contacted at brandon.dehart@uwaterloo.ca or at location E5 3108. His research focuses on robotics, particularly humanoid robots, control systems, and roboethics. He has contributed to advancements in dynamic balance, gait metrics, and human-robot interaction. His work spans topics like legged mechanism design, sensor networks in art installations, and automated content generation systems. His research interests include the development of humanoid robots capable of child-friendly interactions, robust control strategies for uncertain environments, and ethical considerations in robotic design. He has explored technical challenges like foot placement estimators for balance recovery and momentum-based gait generation in bipedal systems. Notable contributions include methodologies for tracking visitor engagement in large art installations and frameworks for interconnecting cellular automata across hardware and software. His publications reflect a blend of robotics engineering, AI applications, and interdisciplinary approaches to technology design.