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Min HuView profile

Assistant Professor

• Health Economics
• Applied Microeconomics
• Labour Economics
• +7 more

Min Hu is an Assistant Professor in the Department of Economics, Philosophy, and Political Science at the University of British Columbia, Okanagan Campus. He holds an adjunct position at Dalhousie University's School of Health Administration. His primary research focuses on Health Economics, Applied Microeconomics, and Labour Economics, with an emphasis on vulnerable populations in Canada, Indigenous health, income inequalities, and cancer economics. He teaches courses in Microeconomics, Health Economics, and Statistics. Education: PhD in Economics (2019), Dalhousie University; Postdoctoral Fellowship in Health Economics (2019–2020), Dalhousie University. Previous degrees include an M.A. (2014–2016) and B.A. in Economics from Dalhousie University. Research interests include the socio-economic determinants of health disparities, particularly among Indigenous peoples and immigrants, and the economic impacts of cancer-related policies. He employs applied econometric methods to analyze labor market outcomes and social policy effectiveness. His recent articles explore sustainable biomass utilization, energy policies, and environmental engineering innovations. Key themes include biomass supply chain optimization, CO₂ reduction strategies, and waste-to-energy technologies. Scientific Awards: Recipient of SSHRC New Frontiers Grant (2023–2026), CIHR Team Grant (2024–2026), and multiple UBC startup grants. His work is supported by SSHRC, CRDCN, and Indigenous Services Canada. He advises students in Economics and Global Studies (IGS) programs. Grants and funding span health economics, Indigenous well-being, and sustainable energy systems. Collaborations include interdisciplinary teams in health administration and environmental policy. Labs/Teams: Engaged with UBC's Okanagan Sustainability Initiatives and Dalhousie's Health Economics Research Group. Affiliated with professional organizations like the American Economic Association and Canadian Health Economics Study Group.

Peter F. DriessenView profile

Professor

• Communication
• Signal Processing
• Control Systems
• +6 more

Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing

Miao ZhengjieView profile

Assistant Professor

• Database Systems
• Data Management
• Data Curation and Integration
• +3 more

Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.

Aaron SmithView profile

Associate Professor

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +3 more

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

• Geohazards
• Landslides
• Debris flows
• +5 more

Scott McDougall is an Associate Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC). He specializes in geohazards, particularly landslides and related risks, with a focus on improving risk assessment and mitigation strategies. His work integrates field data, statistical analysis, numerical modeling, and laboratory experiments to understand landslide dynamics. Education: PhD in Geological Engineering, UBC (2006) BASc in Civil Engineering, University of Toronto (1998) Research Interests: Landslide mobility and runout modeling Tailings dam breaches and their impacts Risk evaluation frameworks for geohazards Shoreline erosion and landslide-generated waves Applications of machine learning in hazard prediction Key Contributions: Development of the UBC Geohazards Research Team to advance landslide risk reduction Leadership in the CanBreach project to improve tailings dam breach analysis Pioneering probabilistic runout prediction models for rock avalanches and debris flows Awards: Engineering Geology Best Paper Award 2024 CDA Published Paper Award Advising & Grants: Supervised over 15 graduate students and postdoctoral fellows Active industry collaborations with mining and engineering firms Funded by NSERC, Mitacs, and industry partnerships Labs & Teams: UBC Geohazards Research Team CanBreach Collaborative Research and Development Project

Ali GhodsiView profile

Professor

• Machine Learning
• Deep Learning
• Natural Language Processing
• +3 more

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

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.

Rachel PottingerView profile

Professor

• Data Management
• Database Systems
• Data Integration
• +7 more

Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.

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.

Yongyi MaoView profile

Professor

• Communications
• Machine Learning

Yongyi Mao is a Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds a Ph.D. in Electrical Engineering from the University of Toronto and has a multidisciplinary background in medical biophysics and engineering. His research focuses on communications and machine learning, with notable contributions to federated learning, information theory, and adversarial robustness. Professor Mao has held academic roles since 2003, advancing from Assistant to Full Professor by 2012. Education: B.Eng., Southeast University, 1992 M.D., Nanjing Medical University, 1995 M.S., University of Toronto (Medical Biophysics), 1998 Ph.D., University of Toronto (Electrical Engineering), 2003 Research interests span machine learning frameworks, federated learning, adversarial attacks, and domain adaptation. His work often bridges theoretical foundations (e.g., generalization bounds) with practical applications in text classification and watermarking. Publications reflect a strong emphasis on machine learning theory and NLP applications, with recent trends toward improving model robustness and generalization. No scientific awards are explicitly listed, though his prolific output suggests significant recognition in the field. Advising and grants: While specific student names or grant details are not provided, his position as a Full Professor indicates active research supervision and likely grant involvement. His lab focuses on advancing AI and communication technologies through interdisciplinary approaches.

• Experimental Gravity
• Astrophysical Signals
• Nuclear Physics
• +8 more

Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +5 more

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

Zoe LiView profile

Associate Professor

• Uncertainty Analysis
• Risk Management
• Stochastic Modelling
• +7 more

Dr. Zoe Li is an Associate Professor in the Department of Civil Engineering at McMaster University, with an additional role as an Associate Member in the Department of Computing and Software. She specializes in developing modeling and decision-support tools to address challenges in water resources management, environmental systems analysis, and climate change impacts. Her research focuses on hydrological modeling, probabilistic forecasting, risk analysis, and environmental systems optimization. She holds a B.Eng. from China and M.A.Sc./Ph.D. degrees from the University of Regina. Her work spans interdisciplinary collaborations, including studies on dam safety under climate change, wastewater treatment plant optimization, and AI-driven environmental monitoring. She has been actively involved in projects such as CityDNA, a digital tool to model urban environments for decision-making during crises like the pandemic. Dr. Li teaches courses in environmental systems engineering and principles of environmental engineering, emphasizing uncertainty quantification and sustainable practices. Her research publications (15+ recent articles) address topics like machine learning applications in environmental systems, climate change impacts on water resources, and infrastructure resilience. She advises graduate students in environmental engineering and systems optimization. Notable grants include funding from Roche Canada for pandemic-related urban modeling. Her work is frequently published in high-impact journals like Journal of Environmental Management and Water Resources Research .

• Algorithmic Databases
• Data Mining
• Computational Geometry
• +5 more

Stavros Sintos is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). He joined UIC after a postdoctoral fellowship at the University of Chicago, where he was part of the ChiData group under Sanjay Krishnan. He earned his Ph.D. in Computer Science from Duke University in 2020, advised by Pankaj K. Agarwal. His research focuses on designing efficient algorithms for databases, data mining, and computational geometry . Key themes include: Theoretical guarantees for practical problems Compact indexing structures for query efficiency Geometric optimization combined with database systems Fairness in algorithmic design (e.g., Fair Set Cover, FairHash) Temporal data analysis and dynamic query processing Notable scientific contributions include a Best Paper Award at ICDT 2024 for work on range entropy queries. His publications span top venues like SIGMOD, PODS, VLDB, and SODA, covering areas such as clustering algorithms, synthetic query witnesses, and temporal join optimizations.

Kourosh DavoudiView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.