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Wuyang ChenView profile

Assistant Professor

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

Wuyang Chen is an Assistant Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2023). His research focuses on integrating AI with physics, developing principled deep learning methods, and advancing areas like scientific machine learning (SciML), computer vision, and foundation models. Notable contributions include PDE-Controller for LLM-based scientific reasoning and neural operators for fluid reconstruction. He has received prestigious awards, including the INNS Doctoral Dissertation Award (2024) and the NSERC Discovery Grant. Dr. Chen serves as an Area Chair for NeurIPS 2025 and ICML 2025. His lab, DeLTA, emphasizes AI applications in science and hosts active collaborations. He teaches CMPT 419/983 (Special Topics in AI) and mentors students in research areas like model efficiency and self-supervised learning. Education: Ph.D., Electrical and Computer Engineering, The University of Texas at Austin (2023) Recent Awards: INNS 2024 Doctoral Dissertation Award, iSchools Doctoral Dissertation Award 2024, Google GCP Credit Award Key Activities: Organized NeurIPS 2024 workshop on Foundation Models for Science; presented at AAAI 2025, SIAM CSE25, and Berkeley BLISS seminar

• Operations Research
• Supply Chain Management
• Blockchain Technology
• +3 more

Guoqing Zhang is a Professor at the University of Windsor's Faculty of Engineering, specializing in operations research, supply chain management, and blockchain technologies. His research focuses on optimizing logistics systems, sustainable closed-loop supply chains, and strategic decision-making under hybrid carbon policies. Notably, he led a team that won the 2015 CORS National Conference's First Practice Prize for developing a cargo optimization tool for Air Canada. His work integrates advanced algorithms like neural genetic methods and blockchain applications to solve complex industrial problems. Key research areas include RFID adoption in healthcare supply chains, blockchain-driven financial systems, and emission reduction strategies. He advises students such as Nooshin Nekoiemehr, Qiqi Zhang, and Anqi Chen, contributing to innovative solutions in distributed manufacturing and maritime resilience. His publications address challenges in competitive refurbisher markets, dynamic pricing under social influence, and inventory-location modeling in pallet pooling systems. Awards: First Practice Prize, CORS National Conference 2015 Grants/Projects: Development of cargo optimization tools, blockchain implementations in financial supply chains, and sustainability-focused closed-loop networks Labs/Teams: Engaged in interdisciplinary research teams addressing supply chain resilience and environmental policy integration

William ShotykView profile

Professor

• Environmental Geochemistry
• Soil Science
• Trace Elements Cycling
• +7 more

William Shotyk is a Professor and Bocock Chair in Agriculture and the Environment at the University of Alberta's Faculty of Agricultural, Life and Environmental Sciences, Department of Renewable Resources. He holds joint appointments in the Land Reclamation International Graduate School (LRIGS) program. His research focuses on environmental soil science, geochemistry, and the impacts of industrial activities on ecosystems. Education: B.Sc. (Agr.) in Soil Science and Chemistry from the University of Guelph (1981), Ph.D. in Geochemistry from the University of Western Ontario (1986), and postdoctoral training at UC Riverside and University of Bern (Switzerland). He became a Full Professor at Heidelberg University before joining the University of Alberta in 2011. Research Interests: Geochemical cycling of trace metals (Pb, Sb, As, Cd, Ag, Tl, Hg), soil and water conservation, biogeochemical processes in peatlands, and the application of isotopic techniques (Pb, Sr, radiogenic isotopes) to environmental problems. Recent work includes studying atmospheric dust deposition from oil sands mining, trace element bioavailability, and the Anthropocene Epoch's geological markers. Key projects include the SWAMP (metal-free ultra-clean lab), the Elmvale Groundwater Observatory, and collaborations on peat core analyses to trace environmental changes. His work bridges fundamental geochemistry with applied issues like wastewater treatment and climate change impacts. Awards: Philippe Duchaufour Medal (2012), Fellow of the Royal Society of Canada (2018), Geochemistry Fellow (2019). Teaching: Courses on soil and water conservation (REN R 360/750), trace elements in the environment (REN R 418/518), and advanced research skills (REN R 605). He advises graduate students in environmental geochemistry and land reclamation. Lab/Teams: SWAMP Lab, LRIGS Program, and international collaborations on peatlands and Anthropocene research.

Ba ChuView profile

Professor

• Econometrics
• Time-Series Econometrics
• Nonparametric and Semiparametric Econometrics
• +7 more

Professor Ba Chu is a faculty member in the Department of Economics at Carleton University, Ottawa, Canada. He holds a Ph.D. from the University of London and has a strong academic background in econometrics and financial economics. His research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), including a 2020 Discovery Grant for work on simulation-based methods in large dynamic latent variable models. B.A. (Hanoi) M.Sc., M.Phil., Ph.D. (London) His research focuses on optimal asset allocation , time-series econometrics , and nonparametric and semiparametric methods . He has made significant contributions to dependence modelling using copulas , asymptotic theory , and large deviations approaches to risk assessment . His work bridges theoretical econometrics with practical applications in finance and macroeconomics. The recent publications highlight a consistent focus on high-dimensional and dynamic models, with applications in financial markets, portfolio optimization, and macroeconomic data analysis. His methodological contributions include advanced inference techniques in moment restriction models, duration dependence in high-frequency trading, and robust estimation under dependence and structural breaks. The research trend shows a deep integration of probability theory, statistical inference, and financial economics. NSERC Discovery Grant (2020), $90,000 over five years Professor Chu teaches courses such as Econometrics I (Econ 5027) , Applied Time Series Analysis (Econ 5713w) , and Statistical Methods in Economics . While no formal advisees are listed, his collaborative research involves prominent econometricians such as D.T. Jacho-Chavez, O.B. Linton, and S. Satchell. He has no known lab or research team listed publicly, but his work is theoretically rigorous and mathematically sophisticated, often involving simulation-based and asymptotic methods.

Abdelhamid TayebiView profile

Professor

• Control Systems Theory
• Robotics
• Autonomous Vehicles
• +7 more

Abdelhamid Tayebi is a Distinguished Professor and Research Chair in the Department of Electrical and Computer Engineering at Lakehead University. He also holds a courtesy professorship at Western University. His research focuses on control systems, autonomous navigation, and robotics, with applications to UAVs and multi-agent systems. Key roles include Graduate Coordinator and founder/director of the Robotics & Automatic Control Laboratory. Education: B.Sc. in Electrical Engineering, Ecole Nationale Polytechnique (1992) M.Sc. in Robotics, Université Pierre & Marie Curie (1993) Ph.D. in Robotics and Automatic Control, Université de Picardie Jules Verne (1997) Research Interests: Control systems theory, iterative learning control, autonomous vehicles, UAV navigation, hybrid feedback control, and nonlinear state estimation. His work emphasizes provable stability guarantees and real-world applications. Recent Articles Trends: Focus on hybrid control for obstacle avoidance, distributed estimation in multi-agent systems, and nonlinear observers for inertial navigation. Publications span IEEE Transactions on Automatic Control , Automatica , and robotics conferences like CDC and ACC. Awards: 2024 Distinguished Instructor Award (Lakehead University's top teaching honor) 2023 IEEE Fellow and EIC Fellow NSERC Discovery Accelerator Supplements (2013) Top 2% Highly Cited Scientist (Stanford, 2019–) Students & Labs: Advised over 20 PhD/MSc students and postdocs. Current research group includes postdocs Mayur Sawant (autonomous robotics) and Ishak Cheniouni (navigation algorithms). Notable former students include Miaomiao Wang (geometric observers) and Mouaad Boughellaba (multi-agent systems). Grants & Editorial Work: Holder of NSERC grants and Lakehead Research Chairs (2012–2027). Associate Editor for Automatica , IEEE Transactions on Control Systems Technology , and others. Active in reviewing for top journals/conferences.

Reto AchermannView profile

Assistant Professor

• Resilient Systems
• Efficient Systems
• Operating Systems
• +5 more

Reto Achermann is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. His research focuses on resilient and efficient systems at the intersection of operating systems, applied formal methods, and hardware models. He holds a PhD from ETH Zurich and previously served as a Postdoctoral Research Fellow at UBC under Prof. Margo Seltzer. Education: PhD in Computer Science from ETH Zurich (advised by Prof. Timothy Roscoe), MSc in Computer Science from ETH Zurich. Research interests include memory and storage systems, formal verification, device drivers, and software synthesis. He contributed to the Barrelfish OS project, particularly in memory management and hardware abstractions. Key achievements: Won distinguished artifact awards at ASPLOS'25 and SOSP'24 for 'Velosiraptor' and 'Verus'. Received UBC's Faculty of Science Excellence in Service Award for educational contributions. Served on program committees for ASPLOS, PLDI, EuroSys, and USENIX ATC. Current roles: Actively supervises graduate students in thesis-based MSc/PhD programs. Engages in interdisciplinary research and collaborates on grants. Previously advised undergraduate research projects and directed studies.

Ivan BeschastnikhView profile

Associate Professor

• Distributed Systems
• Software Engineering
• Cloud Computing
• +5 more

Ivan Beschastnikh is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. He leads research in distributed systems, software engineering, and formal methods, with a focus on privacy-preserving technologies, blockchain systems, and machine learning. His work bridges theoretical foundations and practical system implementations, emphasizing empirical validation and real-world deployment. Research Interests: His primary areas include distributed systems (e.g., consensus algorithms, transaction processing), formal methods for system correctness (e.g., PGo compiler for verified implementations), and privacy in federated learning and blockchain. He also explores interdisciplinary topics like AI ethics, human-computer interaction for developers, and system security. Key Projects: Includes PGo (formal specification to Go compiler), Biscotti (decentralized federated learning), Erlay (Bitcoin transaction relay optimization), and Teleoscope (text corpus analysis tool). His work has practical impact in blockchain scalability, secure multi-party ML, and distributed system validation. Awards: Recognized with the ASPLOS Distinguished Artifact Award, UBC Faculty Teaching Award, and ESEM Best Paper Award. His contributions span academic excellence, pedagogy, and collaborative research. Lab Affiliations: Active in the Systopia Lab and Software Practices Lab (SPL) , and collaborates with UBC’s Data Science Institute and Blockchain@UBC. He emphasizes interdisciplinary research, mentoring students in both theoretical and applied systems work.

François TardifView profile

Professor

• Herbicide resistance in weeds
• Weed physiology
• Plant genetics
• +3 more

Prof. François Tardif is a Professor in the Department of Plant Agriculture at the University of Guelph within the Ontario Agricultural College . His research focuses on herbicide resistance in weeds , including characterization of resistance mechanisms, development of diagnostic tools, and implementation of sustainable management programs. University: University of Guelph School: Ontario Agricultural College Department: Department of Plant Agriculture Email: ftardif@uoguelph.ca Prof. Tardif investigates herbicide resistance in species like Ambrosia trifida (giant ragweed), Conyza canadensis (horseweed), and Solanum ptychanthum (eastern black nightshade), examining molecular and physiological mechanisms, fitness trade-offs, and integrated control strategies. His work addresses both target-site and non-target-site resistance phenomena. The 15 most recent articles demonstrate expertise in glyphosate resistance , ALS inhibitor resistance , herbicide dose-response studies , and weed control in soybean/winter wheat systems . Key subtopics include ambrosia trifida , conyza canadensis , amaranthus powellii , and molecular mechanisms like psbA mutations and acetohydroxyacid synthase substitutions . He has collaborated extensively on field trials in Ontario, evaluating herbicide efficacy, crop tolerance, and resistance distribution patterns. His research contributes to sustainable agricultural practices through scientific understanding and practical management recommendations.

Jon MikkelsenView profile

Teaching Professor

• Naval Architecture
• Ocean Engineering
• Sustainable Fishing Technologies
• +5 more

Jon Mikkelsen is a Teaching Professor and Director of Naval Architecture and Marine Engineering at the University of British Columbia (UBC), Faculty of Applied Science, Department of Mechanical Engineering. He specializes in maritime engineering, sustainable fishing technologies, and mechanical design pedagogy. Educational Background: B.A.Sc. in Mechanical Engineering, UBC M.A.Sc. in Mechanical Engineering, UBC Mikkelsen's research focuses on naval architecture, including hullform optimization, hydrodynamics of waterfowl, and vertical axis ocean turbines. He has developed laser-based wave measurement systems and collaborated with British Columbia First Nations (Gitksan, Tsimshian, Stó:lō, Wet’suwet’en, Gitanyow) to modernize traditional fishing gear for selective salmon harvesting. His work integrates engineering principles with ecological conservation. Scientific Awards: SNAME Faculty Advisor of the Year Award, 2023 Peter Larkin Award, 2016 Wighton Fellowship, 2006 OMAE Conference Appreciation Award, ASME, 2005 As director of the Naval Architecture and Marine Engineering program, Mikkelsen mentors undergraduate design teams in projects ranging from autonomous sailboats (UBC Sailbot) to supermileage vehicles (TIME Magazine top invention, 2006). His publications span maritime engineering, renewable energy, and engineering education. He has secured grants from Fisheries and Oceans Canada for sustainable fishing initiatives.

Geordie RichardsView profile

Assistant Professor

• Nonlinear Partial Differential Equations
• Probability Theory
• Dynamical Systems
• +6 more

Geordie Richards is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph. He holds a PhD in Mathematics from the University of Toronto (2012) and has held academic positions at the University of Minnesota, University of Rochester, Utah State University, and the University of Toronto Mississauga. PhD in Mathematics, University of Toronto (2012) NSERC Graduate Scholarship (2006) Richards' research focuses on the analysis of deterministic and stochastic nonlinear partial differential equations (PDEs) arising in physics and engineering, particularly those modeling fluid dynamics and dispersive wave phenomena. His work integrates techniques from probability theory and dynamical systems to address questions of well-posedness, ergodicity, and uncertainty quantification in PDEs. Key themes include random data Cauchy theory, ergodicity of stochastic PDEs, singular stochastic dispersive equations, and applications to engineering uncertainty analysis. Recent publications highlight his contributions to stochastic hydrodynamic stability, surrogate modeling for nuclear energy systems, and ergodic theory for dispersive equations. His work bridges rigorous PDE analysis with practical applications in turbulence and nuclear reactor design. Mechanical & Aerospace Engineering Teacher of the Year, Utah State University (2019) NSERC Graduate Scholarship (2006) Richards has secured significant research funding including the NRC Faculty Development Grant (2019-2022) as Co-PI and NSF Conference Grant (2017). He teaches with active learning strategies, covering courses from first-year Calculus to advanced research topics.

Pengfei LiView profile

Assistant Professor

• Online optimization
• Trustworthy machine learning
• Sustainable AI
• +7 more

Pengfei Li is an Assistant Professor in the School of Information at the Rochester Institute of Technology (RIT), where he leads research at the intersection of machine learning, sustainability, and social equity. Previously, he completed his Ph.D. in Computer Science at the University of California, Riverside under Prof. Shaolei Ren, with additional collaborations at Caltech with Adam Wierman and an internship at Nokia Bell Labs. His educational background includes an M.S.E. in Robotics from Johns Hopkins University and a B.E. in Electrical Engineering from Zhejiang University. Dr. Li's research focuses on three interconnected pillars: developing trustworthy online algorithms with strict robustness guarantees, creating sustainable AI systems that minimize environmental impact, and addressing environmental and social inequities through algorithmic solutions. Analysis of his recent publications reveals a strong emphasis on the environmental consequences of AI systems, particularly water consumption ('Making AI Less 'Thirsty'') and geographical distribution of environmental burdens ('Towards Environmentally Equitable AI'). His work bridges theoretical computer science with practical sustainability challenges, often incorporating learning-augmented approaches to traditional online optimization problems. Scientific Recognition: Dissertation Completion Fellowship Award (DCFA) from the Graduate Program in Computer Science (February 2025) 'Making AI Less 'Thirsty'' in Communications of the ACM has received 15 citations and over 17,000 downloads Organizer of the workshop on learning-augmented algorithms at SIGMETRICS 2025 Dr. Li actively seeks to build a research group focused on societal fairness, reliable generative AI, and decision-focused learning. His work has established important connections between theoretical computer science and critical societal challenges, particularly around AI's environmental footprint and equitable resource distribution. Current research directions include developing algorithmic solutions for environmental and social fairness, with applications in water infrastructure, energy systems, and equitable AI deployment.

Tarah LynchView profile

Adjunct Assistant Professor

• Virology
• Infectious Disease Genomics
• PCR Assay Development
• +3 more

Tarah Lynch is an Adjunct Assistant Professor at the University of Calgary. Her research focuses on virology, infectious disease genomics, and molecular diagnostics for emerging pathogens. Affiliation: University of Calgary Academic Rank: Adjunct Assistant Professor Dr. Lynch's research interests include: Viral evolution and infection dynamics Development of real-time PCR assays for pathogen detection Genomic epidemiology of bacterial pathogens like Escherichia coli ST131 Antibiotic resistance mechanisms and spatial distribution Application of sequencing technologies in outbreak analysis Integration of computational approaches with clinical microbiology Her publication record demonstrates expertise in tracking viral variants (SARS-CoV-2, HPeV-5), analyzing bacterial pathogen genomics (STEC, MRSA), and developing diagnostic frameworks. Key themes include: Combining genomic data with outbreak investigation Environmental reservoirs of antibiotic-resistant bacteria Improving molecular diagnostics through algorithm development Multi-omics approaches to infection dynamics Public health applications of rapid sequencing Surveillance of emerging infectious diseases

Jia HuView profile

Assistant Clinical Professor

• Social Determinants of Health
• Housing and Health
• Climate Change and Health
• +5 more

Jia Hu is a public health physician and medical lead of the prevention and health promotion team within Population and Public Health at the British Columbia Centre for Disease Control (BCCDC). He also holds a position as a Clinical Assistant Professor at the University of British Columbia's School of Population & Public Health, where he contributes to academic training and research in public health disciplines. Dr. Hu's educational background includes medical training at the University of Alberta, followed by residency training in family medicine and public health and preventive medicine at the University of Toronto. During his residency, he earned a Master of Science in Health Policy, Planning, and Finance from the London School of Economics and the London School of Hygiene and Tropical Medicine. His research interests span multiple critical areas in modern public health, with particular focus on social determinants of health, housing and health, climate change and health, food security, mental wellness, and cancer prevention and screening. Dr. Hu has also developed significant expertise in behavior change and marketing strategies specifically tailored for public health interventions such as vaccination programs and cancer screening initiatives. His work bridges clinical practice with population-level health promotion strategies. Analysis of Dr. Hu's recent publications reveals a strong interdisciplinary research profile that spans public health, epidemiology, and increasingly, computational approaches to health data. While his earlier work focused on traditional public health domains, his recent publications show a growing engagement with artificial intelligence, natural language processing, and speech technology applications in healthcare contexts. This evolution demonstrates his ability to adapt to emerging technological paradigms while maintaining focus on core public health challenges. Dr. Hu's professional journey includes diverse experiences that have shaped his interdisciplinary approach: working at McKinsey advising large organizations on health-related issues, serving as a Medical Officer of Health in Alberta where he contributed to the COVID-19 response, and founding and leading a public health non-profit organization focused on increasing uptake of preventive health behaviors. As medical lead of the prevention and health promotion team at BCCDC, Dr. Hu oversees initiatives that address a wide range of public health concerns across British Columbia. His work integrates evidence-based approaches with practical implementation strategies to improve population health outcomes through prevention-focused interventions and health promotion activities.

Youcef DerbalView profile

Associate Professor

• Complex Adaptive Systems
• Cancer Modeling
• Machine Learning in Oncology
• +4 more

Dr. Youcef Derbal is an Associate Professor in the Department of Information Technology Management at Toronto Metropolitan University's Ted Rogers School of Information Technology Management. With a Ph.D. in Electrical and Computer Engineering from Queen's University, he brings extensive industry experience from robotics, telecommunications, and software sectors to his academic work. His research focuses on Complex Adaptive Systems, particularly in modeling cancer dynamics through agent-based simulations, machine learning applications for oncology, and computational frameworks for biological processes. Key research areas include tumor dynamics, cell signaling pathways, and tumor-infiltrating lymphocyte recruitment. Dr. Derbal teaches undergraduate courses in business information systems, programming, system analysis, database design, and client-server applications, along with graduate-level advanced project management. His publication record shows consistent output in computational biology, cancer modeling, and grid computing since 2006. Professional affiliations include: Association for Computing Machinery (ACM) Institute of Electrical and Electronics Engineers (IEEE) International Society for Computational Biology (ISCB) Administrative contributions include service on University Senate, chairing multiple departmental committees, leading the Business Technology Management curriculum development (2009-2014), and serving as School Director during 2013/2014.

Keyhan SheshyekaniView profile

Professor

• Smart grids
• Electromagnetic compatibility
• Electric vehicle systems
• +6 more

Keyhan Sheshyekani is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He holds membership in the NSERC/Hydro-Québec/RTE/EDF/OPAL-RT Industrial Research Chair, specializing in multi time-frame simulation of transients for large-scale power systems. His work bridges industry and academia through partnerships with major energy stakeholders. Research interests span: Smart grids : Cybersecurity, EV-grid integration, and demand response Electromagnetic systems : Grounding design, field modeling, and compatibility Energy control : Optimization algorithms for microgrids and converter systems Recent publications (2021-2025) show strong focus on: Machine learning applications in energy dispatch Cybersecurity frameworks for grid IT/OT convergence Real-time simulation of power electronics Advanced control strategies for EV charging infrastructure He actively mentors graduate students, with 10+ advised in the past five years working on projects like: EV aggregator controls for grid ancillary services FPGA-based real-time simulation Cybersecurity for synchrophasor networks