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Samuel Jean BassettoView profile

Associate Professor

• Industrial Engineering
• Artificial Intelligence
• Cognitive Science
• +4 more

Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.

Carolina OsorioView profile

Professor

• Transportation Science
• Operations Research
• Artificial Intelligence
• +4 more

Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.

Beata GorczycaView profile

Professor

• Environmental Engineering
• Water and Wastewater Treatment
• Solid/Liquid Separation
• +5 more

Dr. Beata Gorczyca is a Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. Her research focuses on potable water treatment, with expertise in solid/liquid separation, chlorine disinfection by-products control, and fractal analysis of materials. She leads a research group collaborating with Canadian water utilities like Portage la Prairie and Pembina Valley Water Co-op. Education: PhD in Chemical Engineering (2000, University of Toronto), M.Sc. in Civil Engineering (1992, University of Toronto), B.Sc. in Geological Engineering (1986, AGH University, Poland). Active roles: Member of the Particle Specialist Group at the International Water Association, keynote speaker at conferences. Research interests include water purification processes, membrane filtration, and bioremediation. Her work addresses challenges in high-DOC and high-hardness water treatment, with contributions to nanofiltration fouling mechanisms and microbial remediation solutions. She has supervised numerous graduate students and is involved in advancing water treatment technologies through interdisciplinary collaborations.

Christoph FreiView profile

Professor

• Quantitative Finance
• Risk Management
• Mathematical Economics
• +4 more

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.

Bradley BuchsbaumView profile

Associate Professor

• Cognitive Neuroscience
• Working Memory
• Functional Neuroimaging
• +5 more

Bradley Buchsbaum is an Associate Professor and Senior Scientist at the Rotman Research Institute, Baycrest , Toronto, Canada. His research focuses on cognitive neuroscience, particularly on working memory, episodic memory, and functional neuroimaging using fMRI. PhD in Cognitive Science (2003) from University of California, Irvine BSc in Bio-Psychology (1997) from University of California, Santa Barbara His lab investigates how memories are stored, represented, and reactivated in the brain, combining functional neuroimaging, eye-tracking, and computational modeling. Key areas include multivariate statistics, machine learning applications in neuroimaging, and quantifying memory fidelity through behavioral and neural data. Recent publications emphasize neuroimaging methodology (e.g., MRI data consistency checks), memory pattern completion mechanisms, and feature-specific neural reactivation during episodic recall. Research trends highlight interdisciplinary approaches merging cognitive neuroscience with advanced statistical techniques. Lab team includes post-doctoral fellows (Stephen Rhodes), graduate students (Carolyn Guay, Corey Loo, Michael Bone, Nick Hoang, Ryan Barker), and research assistants. The lab actively develops open-source tools like rMVPA and neuroim2 for fMRI analysis.

Jonathan CantView profile

Associate Professor

• Cognitive Neuroscience
• Visual Perception
• Object-Ensemble Perception
• +6 more

Dr. Jonathan Cant is an Associate Professor at the Department of Psychology, University of Toronto Scarborough (UTSC). His research focuses on cognitive neuroscience, particularly visual perception and cognition using fMRI and behavioral techniques. Born in Hamilton, Ontario, educated at Western University (Psychology) and Harvard University (postdoctoral) Expertise: Object perception, ensemble processing, texture/shape perception, scene recognition The Cant Lab investigates high-level human visual cortex organization, with a focus on the ventral stream's role in perception. Key research includes dissociating shape and surface property processing, material properties in cognition, and object-scene interactions in neural systems. Recent publications highlight ensemble encoding of facial expressions (2025), cross-modal ensemble coding (2024), and texture-shape relationships in ventral cortex (2015). His work bridges modular/distributed cortical organization theories and explores neural substrates for 3D shape vs. material compliance. Teaching includes PSY5222 (Visual Cognitive Representation), PSYC75 (Cognitive Psychology Laboratory), and PSYC51 (Cognitive Neuroscience of Vision). The lab welcomes motivated students and researchers through direct email applications.

• Signal Processing
• Communications
• Bioimaging
• +5 more

Amir Asif is a Professor at the Lassonde School of Engineering, York University, and concurrently serves as Vice President, Research and Innovation. His academic leadership roles include Dean of the Gina Cody School of Engineering and Computer Science at Concordia University (2014-2020). He specializes in signal processing, communications, and their applications in healthcare, power grids, and distributed systems. Asif holds a PhD from Carnegie Mellon University and a Harvard certification in executive leadership. Education: PhD, Electrical and Computer Engineering, Carnegie Mellon University (1996) MS, Electrical and Computer Engineering, Carnegie Mellon University (1993) BSc, University of Engineering and Technology Lahore (1990) Harvard Certificate in Leadership for Senior Executives (2018) Research Interests: Asif’s work spans signal processing for medical imaging (e.g., ultrasound elastography), smart grid optimization, and cybersecurity in power systems. His recent publications address hydrogen energy systems, EMG-based gesture recognition, and resilient control frameworks against cyberattacks. Grants & Leadership: He leads NSERC-funded projects on federated learning and resilient algorithms. He chairs the Ontario Council of University Research and serves on TRIUMF Innovations and the Richmond Hill Board of Trade. His grants include SSHRC funding for equity initiatives and NSERC support for distributed signal processing. Teaching & Mentorship: Asif has supervised over a dozen graduate students and taught courses like Digital Communications and Statistical Signal Processing Theory. Notable advisees include Arash Mohammadi (PhD, 2014) and Nick Sajadi (PhD, 2017).

Cody HyndmanView profile

Professor

• Mathematical Finance
• Computational Finance
• Probability and Stochastic Analysis
• +4 more

Cody Hyndman is a Full Professor and Acting Department Chair at the Department of Mathematics and Statistics, Concordia University, with a focus on Mathematical Finance, Machine Learning, and Stochastic Analysis. He has held significant administrative roles including Department Chair (2017–2023) and Acting Graduate Programs Director (2025–2025). Education: PhD, University of Waterloo (2005) MSc, University of Alberta BCom, University of Alberta His research spans Mathematical Finance , Stochastic Differential Equations , and Machine Learning , with notable contributions to arbitrage-free modeling, neural networks, and computational methods. Recent publications emphasize geometric deep learning and regularization techniques in finance. Scientific Awards: 2023: Concordia Academic Leadership Award Hyndman supervises graduate students in Mathematics and Statistics and co-founded the NSERC CREATE Program on Machine Learning in Quantitative Finance and Business Analytics (FIN-ML) , fostering industrial internships and interdisciplinary training.

Ankush AgarwalView profile

Associate Professor

• Mathematical finance
• Data science in finance
• Financial statistics
• +3 more

Ankush Agarwal is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His research focuses on mathematical finance, financial statistics, and Monte Carlo methods, with applications to risk management and derivatives pricing. He supervises PhD students in quantitative finance and has taught courses on Monte Carlo methods and advanced financial modeling at Western University. Education: PhD in Mathematics from Tata Institute of Fundamental Research (2015) Research interests span regime-switching models, longevity risk hedging, stochastic differential equations, and rare event simulation. His work combines theoretical probability with computational techniques for financial applications. Recent publications include studies on McKean-Vlasov SDEs, implied Sharpe ratio estimation, and optimal portfolio strategies under stochastic volatility. These works demonstrate his expertise in stochastic processes and financial engineering. Supervision: Current PhD advisees include Ying Liao, Buchun Wang, and Shuya Zhang at the University of Glasgow. Former advisees include Yongjie Wang and Yihan Zou.

David LatulippeView profile

Professor

• Membrane Science
• Bioprocessing
• DNA Purification
• +4 more

David Latulippe is a Professor in the Department of Chemical Engineering at McMaster University. He joined McMaster in 2012 after postdoctoral work at Cornell University and a PhD at Penn State University, focusing on membrane filtration for DNA purification. His industrial experience includes roles at ZENON Environmental (now GE Water) in hollow-fiber membrane design for water treatment. Research interests include Membrane science and technology Bioprocessing of therapeutic viruses Microscale systems for biological applications Environmental engineering solutions for water treatment Current projects involve collaborations with industry partners like Ceapro and Aevitas, and the development of a biomanufacturing automation lab with Sartorius. Recent publications highlight advancements in Nanofiltration and microfiltration for viral vectors Conductive membranes for electrochemical applications Microfluidic systems for DNA analysis Environmental monitoring of biocides and microplastics Scientific recognition includes the Young Membrane Scientist Award (2014). Teaching activities focus on Fluid Mechanics (CHEMENG 2O04) and Industrial Separation Processes (CHEMENG 4M03).

Amir Keyvan KhandaniView profile

Professor

• Information Theory
• Wireless Communications
• Optical Communications
• +5 more

Dr. Amir Keyvan Khandani is a Professor and Senior Ciena-NSERC Industrial Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds prestigious research chairs including Tier 1 Canada Research Chair in Wireless Communications and former Senior NSERC Chairs with Blackberry and Nortel. His research focuses on information theory, wireless and optical communications, and signal processing, emphasizing foundational principles and practical applications. Dr. Khandani earned his BEng and MEng from Tehran University (1985) and PhD from McGill University (1992). He joined Waterloo in 1993, supervising over 45 PhD students, 35 master’s candidates, and numerous postdoctoral researchers. His alumni work globally in academia and industry. Research interests include Network Information Theory , Media-Based Modulation , Full-Duplex Systems , and Quantum-Safe Encryption . Recent work explores secure key generation, interference management, and next-generation wireless innovations. Notable awards include NSERC/Ciena Industrial Research Chair and multiple Canada Research Chairs. His publications span foundational and applied topics in communications, with recent focus on cybersecurity and 5G/6G technologies. Dr. Khandani actively contributes to conferences, consults for industry/government, and teaches ECE 307 - Probability Theory and Statistics . His lab develops cutting-edge solutions in wireless networks, optical systems, and secure communication protocols.

Nils WildeView profile

Assistant Professor

• Robotics
• Autonomous Systems
• Human-Robot Interaction
• +16 more

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

Steven LivingstoneView profile

Associate Professor

• Data Science
• Affective Computing
• Human Computer Interaction
• +4 more

Steven Livingstone is an Associate Professor in the Department of Computer Science at Ontario Tech University, Faculty of Science. His research focuses on affective data science, applying machine learning and statistical modeling to understand emotion and its disorders. He leads the Affective Data Science Lab (ADSL), specializing in emotion recognition technologies using physiological data like EEG and motion capture. Livingstone holds a PhD from The University of Queensland (2008) and has published over 70 peer-reviewed papers, with over 2,400 citations. His RAVDESS dataset is widely used in speech emotion recognition research. His research interests span data science, affective computing, and music's role in emotion. Recent work emphasizes data provisioning for deep learning applications. Livingstone teaches courses including Scientific Data Analysis and Information Visualization. He actively mentors undergraduate and graduate students in his lab, focusing on research assistantships in emotion technology development. Key contributions include studies on musical tempo’s physiological effects, Parkinson’s disease facial mimicry deficits, and ensemble performance dynamics. His work has been featured in The Atlantic, NBC Today, and on the cover of Informatik Spektrum. The ADSL lab collaborates on projects combining data analytics with human-computer interaction to advance emotion-aware systems.

James LacefieldView profile

Professor

• Biomedical Ultrasound Imaging
• Vascular Imaging
• Cancer Research Applications
• +5 more

James Lacefield is a Professor in both the Department of Electrical and Computer Engineering and the Department of Medical Biophysics at Western University. He serves as Director of the School of Biomedical Engineering and maintains his research laboratory in the Amit Chakma Engineering Building. His academic appointments span multiple disciplines, reflecting the interdisciplinary nature of his work in biomedical ultrasound imaging. Dr. Lacefield earned his Ph.D. and B.S.E. in Biomedical Engineering from Duke University. His educational background established the foundation for his current research program that bridges engineering principles with medical applications. His research focuses on the physical acoustics and signal processing aspects of ultrasound imaging, with particular emphasis on quantitative vascular imaging applications. Dr. Lacefield's laboratory develops novel methods for color Doppler, power Doppler, and contrast-enhanced ultrasound imaging, with primary applications in cancer research. Current projects include optimization of high-resolution ultrasound systems for tumor vascular characterization and development of methods to quantify spatial blood flow distribution in tumors. Analysis of his recent publications reveals a strong focus on quantitative ultrasound techniques for cancer applications, with particular attention to tumor perfusion assessment using contrast-enhanced ultrasound. His work demonstrates increasing sophistication in speckle analysis methods to improve the reliability of perfusion measurements in preclinical tumor models. Associate Editor, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control Member, Council of Chairs of Bioengineering and Biomedical Engineering Member, College of Reviewers, Canadian Institutes of Health Research Dr. Lacefield maintains active collaborations with multiple research groups including the Imaging Research Laboratories at Robarts Research Institute. His professional activities include editorial work for major ultrasound journals and participation in national review panels for biomedical research funding.

Jiannan WangView profile

Associate Professor

• Database Systems
• Data Management
• Data Cleaning
• +5 more

Jiannan Wang is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). He holds a Ph.D. from Tsinghua University (2013) and a B.Sc. from Harbin Institute of Technology (2008). His research focuses on database systems, data management, and data science, with particular emphasis on data cleaning, crowdsourcing, and big data technologies. He leads the SFU Data Science Research Group, aiming to accelerate data science workflows through innovative tools like DataPrep and ConnectorX. Education: Ph.D. in Computer Science and Technology, Tsinghua University, China (2013) B.Sc. in Computer Science and Technology, Harbin Institute of Technology, China (2008) Research Interests: Dr. Wang's work spans database systems, data cleaning, crowdsourcing, and big data education. He develops open-source tools for data scientists to streamline data preparation and analysis. His lab's mission is to make data science more efficient through technologies like DataPrep and ConnectorX . Awards: IEEE TCDE Rising Star Award (2018) CS-Can|Info-Can Outstanding Early Career Researcher Award (2020) VLDB Best Experiments, Analysis & Benchmark Paper Award (2021) PVLDB Distinguished Review Board Member Award (2020) Advising & Leadership: Director of SFU's Professional Master's Program in Big Data and Visual Computing. Supervised over 20 graduate and undergraduate students, many of whom have gone on to roles at top companies like Google, Amazon, and Huawei. Lab & Teams: Part of the SFU Data Science Research Group and the SFU Big Data Academic Advisory Committee. His lab collaborates with industry partners and contributes to open-source projects in data management and machine learning.