جست‌وجوی استاد و پژوهشگر

Michael Janzenمشاهده پروفایل

دانشیار

• Virtual Camera Selection
• Serious Game Modules
• Camera Trap Image Processing
• +۳ مورد دیگر

Dr. Michael Janzen is an Associate Professor in the Department of Computing Science at The King's University. He holds a PhD from the University of Saskatchewan, an M.Sc. from the University of Guelph, and a B.Sc. from Brock University. His research focuses on virtual camera selection using semi-ring constraint satisfaction problems (SCSPs), serious game modules, and camera trap image processing for ecological studies. His work intersects computer science, environmental monitoring, and AI ethics. Education includes: PhD in Computing Science, University of Saskatchewan M.Sc. in Computer and Information Science, University of Guelph B.Sc. in Computers and Physics, Brock University Research interests emphasize practical applications of AI and computational methods in virtual environments, wildlife monitoring, and educational gaming. Recent projects include developing automated systems for wildlife fence design analysis and image screening tools for ecological data. His work bridges theoretical computer science with real-world challenges in conservation and technology ethics.

Adrian Liuمشاهده پروفایل

استاد

• Cosmology
• Astrophysics
• Radio Astronomy
• +۵ مورد دیگر

Adrian Liu is a Professor in the Department of Physics and a member of the McGill Space Institute at McGill University. He leads the McGill Cosmic Dawn and Hydrogen Cosmology Group, focusing on theoretical and observational cosmology to study the formation of the first stars and galaxies. His research leverages data from radio telescopes like the Hydrogen Epoch of Reionization Array (HERA) to explore cosmic reionization and fundamental cosmology. Research interests include 21cm cosmology, intensity mapping, and the interplay between astrophysics and cosmology during the Epoch of Reionization. Key projects involve analyzing HERA observations to map large-scale structures and constrain cosmological parameters. Liu collaborates extensively, mentoring students and postdocs in cutting-edge observational and theoretical work. Notable contributions include developing methodologies to mitigate instrumental systematics and foregrounds in 21cm data, as well as advancing machine learning applications for signal recovery. His group has published extensively on reionization models, power spectrum analysis, and interdisciplinary techniques combining astrophysics with statistical methods. Students under his advisement have achieved significant milestones, including postdoc placements at institutions like the University of Chicago and Perimeter Institute. Collaborations span global initiatives, reflecting his role in advancing Canada’s contributions to radio astronomy and cosmology.

Kamyar Khodamoradiمشاهده پروفایل

استادیار

• theoretical computer science
• approximation algorithms
• combinatorial optimization

Kamyar Khodamoradi is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research focuses on theoretical computer science, particularly approximation algorithms and combinatorial optimization, where he designs algorithms with mathematically provable guarantees for intractable problems. Dr. Khodamoradi earned his Ph.D. from Simon Fraser University and completed postdoctoral appointments at the University of British Columbia and several European institutions. His research explores fundamental challenges in optimization across disciplines like computational geometry, graph theory, and resource allocation. Recent work demonstrates advanced techniques for clustering, scheduling, and network optimization problems. Publications emphasize algorithm design with theoretical rigor, frequently appearing in top-tier computer science venues.

JingTao Yaoمشاهده پروفایل

استاد

• Data Science
• Data Analytics
• Artificial Intelligence
• +۹ مورد دیگر

Dr. JingTao Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He joined the University of Regina in January 2002 and has been actively contributing to the academic community since then. His extensive academic career includes previous teaching positions at Massey University (New Zealand), National University of Singapore, The Open University (Singapore), and Xi'an Jiaotong University (China). Dr. Yao's research interests span multiple areas within computer science, with a strong focus on granular computing, rough sets, soft computing, data mining, three-way decisions, neural networks, computational finance, electronic commerce, and web intelligence . His work represents significant contributions to the theoretical foundations and practical applications of these areas. His recent publications demonstrate a consistent research trajectory focusing on three-way decision theory, game-theoretic rough sets, and their applications across diverse domains including text classification, intrusion detection, fraud detection, and biomedical applications. The publications show increasing integration of traditional rough set theory with modern deep learning techniques. Ranked as world top 2% scientist (top 0.92%) by Stanford University Three Highly Cited Papers according to Web of Science One Hot Paper according to Web of Science Dr. Yao coordinates the Rough Set Technology Lab and the Web Intelligence Consortium Canada Research Centre. He has served on numerous administrative committees at the departmental, faculty, and university levels, including as Chair of the Graduate Committee (Data Science) and member of various search and review committees. His professional activities include extensive editorial work as Area Editor of the International Journal of Approximate Reasoning and editorial board membership for several other journals. He has also been actively involved in organizing numerous international conferences in his field, serving as chair, program committee member, and steering committee member for major conferences in rough sets, granular computing, and web intelligence.

Mohand Said Alliliمشاهده پروفایل

استاد

• Machine Vision
• Machine Learning
• Multimedia Data Processing
• +۵ مورد دیگر

Mohand Said Allili is a full professor in the Department of Computer Science and Engineering at the University of Quebec in Outaouais (UQO). He directs the Imaging, Vision, and Artificial Intelligence Research Laboratory (LARIVIA), focusing on machine vision, machine learning, multimedia data processing, and cybersecurity applications in medical and aerial imagery analysis. His research spans over three decades with notable contributions in image segmentation, texture analysis, deep learning models for medical diagnostics, and AI-driven infrastructure inspection systems. Key projects include drone-based bridge defect detection systems and dermatological imaging networks. His work integrates probabilistic modeling (e.g., Gaussian mixtures) with modern neural networks for robust computer vision solutions. Publications highlight trends in: Deep learning applications for infrastructure monitoring (bridge defect detection) Medical imaging advancements (skin lesion segmentation) Augmented reality systems for remote inspection Innovative texture modeling techniques Allili's lab develops cutting-edge tools like MEDiXNet for dermatological analysis and SES-ReNet for human detection in adverse conditions. His work bridges theoretical computer science with practical engineering solutions. Research focuses on: Semantic image/video annotation Multimodal data fusion Lightweight neural networks for edge computing Context-aware feature extraction

Cristián Bravo Romanمشاهده پروفایل

استاد

• Credit Risk Modeling
• Machine Learning
• Banking Analytics
• +۲ مورد دیگر

Cristián Bravo Roman is Professor and Canada Research Chair in Banking and Insurance Analytics at Western University's Department of Statistical and Actuarial Sciences. He holds a Ph.D. from the University of Chile (2013) and leads research on financial analytics, machine learning applications in banking, and risk modeling. His work develops AI-driven approaches for credit scoring, risk assessment, and financial decision-making. Recent publications explore reinforcement learning for credit limit optimization, fuzzy entropy methods for portfolio selection, and transformer models for corporate default prediction. Bravo supervises multiple graduate students in financial analytics and machine learning applications. He has received the Canada Research Chair award (Tier 2) and multiple NSERC grants. Bravo's industry collaborations focus on implementing research insights in banking and insurance sectors through the NSERC Alliance Grant.

Okan Arslanمشاهده پروفایل

دانشیار

• Operations Research
• Combinatorial Optimization
• Network Design
• +۳ مورد دیگر

Okan Arslan is an Associate Professor in the Department of Decision Sciences at HEC Montréal. He holds memberships in the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Group for Research in Decision Analysis (GERAD). His expertise lies in Operations Research, Combinatorial Optimization, and Large-Scale Network Design. Current research focuses on smart last-mile delivery systems. Education: Ph.D. in Industrial Engineering from Bilkent University. Research Interests: Operations Research, Combinatorial Optimization, Network Design, Logistics, Transportation Systems, and Last-Mile Delivery Optimization. Recent work explores facility location under uncertainty, selective routing frameworks, and crowd-based logistics solutions. Awards include the 2024 Transportation Science Meritorious Service Award (2025), Amazon Last Mile Routing Challenge Third Place (2021), and the HEC Montréal New Researcher Prize (2021). He has also received an Honorable Mention from INFORMS (2019) for work on evasive flow capturing. Teaching includes courses such as Fundamentals of Optimization and Distribution Management. He has supervised 4 master’s theses (e.g., Benders decomposition for facility location, routing optimization) and 2 project supervisions (e.g., workload forecasting at Hydro-Québec). His lab affiliations and collaborations emphasize applied research in transportation and logistics, with a focus on practical industrial applications and network resilience.

Laurent Charlinمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +۳ مورد دیگر

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

Paulin Coulibalyمشاهده پروفایل

استاد

• Hydrologic Modelling
• Flood Forecasting
• Water Resources Management
• +۷ مورد دیگر

Dr. Paulin Coulibaly is a Professor in the Department of Civil Engineering at McMaster University, serving as Director of the NSERC Canadian FloodNet, a multi-disciplinary research network funded by NSERC with industry and academic partnerships. His research focuses on advanced flood forecasting, nonstationary hydrologic analysis, hydrometric network design, and integrating machine learning with satellite data for ungauged basins. He holds a B.A.Sc., M.A.Sc. from Nice, and a Ph.D. from Université Laval, and is a Professional Engineer (P.Eng.). Research interests include probabilistic ensemble forecasting, climate change impacts on water resources, and community flood resilience. He teaches courses such as Hydrologic Modeling (CIV ENG 6M03) and Advanced Statistical Methods in Hydrology (CIV ENG 757). FloodNet collaborates with UNU-INWEH and governmental agencies to enhance flood management globally, emphasizing reduced damages and socio-economic impacts. Key achievements include leadership of FloodNet and contributions to infrastructure resilience. His work bridges academic research with operational needs of flood forecasters and policymakers, addressing transboundary water challenges and Indigenous knowledge integration. Ongoing projects involve spatial twin frameworks, climate risk assessment, and hydrometric network optimization.

Serdar Yukselمشاهده پروفایل

استاد

• Stochastic Control Theory
• Information Theory
• Probability Theory
• +۵ مورد دیگر

Serdar Yuksel is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada, affiliated with the Faculty of Arts and Science. He holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), and B.Sc. from Bilkent University. His research focuses on stochastic control theory, information theory, probability theory, and their applications in decentralized systems, networked control, and reinforcement learning. Yuksel's work bridges control, information, and probability, addressing challenges in stochastic stability, optimal control under partial/decentralized information, and ergodic properties of dynamical systems. Education: Ph.D. (2006) - Electrical and Computer Engineering - UIUC M.Sc. (2003) - Electrical and Computer Engineering - UIUC B.Sc. (2001) - Electrical and Electronics Engineering - Bilkent University Research Interests: Stochastic control theory, including optimization and stability of Markovian systems Information theory, particularly zero-delay coding and systems with feedback Probability theory, focusing on Markov processes and ergodic theory Decentralized and networked control systems under information constraints Reinforcement learning and approximation methods for stochastic systems Awards: 2013 CAIMS/PIMS Early Career Award in applied mathematics Teaching and Academic Leadership: Yuksel has taught courses such as Optimization and Control of Stochastic Systems, Mathematics of Engineering Systems, and served as Chair of Mathematics & Engineering at Queen's University. He advises numerous graduate and undergraduate students, mentoring projects in control theory, optimization, and applied mathematics. Labs/Teams: Leads the Stochastic Control (and Related Fields) research group, collaborating on topics like decentralized control, information structures, and mean-field games. Active in organizing seminars and international collaborations.

Ehssan Koupaieمشاهده پروفایل

استادیار

• Waste management
• Wastewater treatment
• Biochemical engineering
• +۶ مورد دیگر

Ehssan Koupaie is an Assistant Professor in the Department of Chemical Engineering at Queen's University, Canada. His research focuses on Environmental BioEngineering, particularly on sustainable waste-to-energy technologies, anaerobic digestion optimization, and wastewater treatment processes. He holds a Ph.D. in Civil and Environmental Engineering from the University of British Columbia (2017) and completed a postdoctoral fellowship at the University of Toronto. Key affiliations include the Beaty Water Research Centre and cross-appointments in the Department of Chemical Engineering. His research integrates anaerobic digestion, thermal hydrolysis, and nanotechnology to enhance bioenergy recovery from organic waste. Current projects address pretreatment technologies, co-digestion of municipal/industrial waste, sludge treatment, and sidestream wastewater treatment. Education: Ph.D. (2017, UBC), M.Sc. (2011, Amirkabir University), B.Sc. (2008, Isfahan University of Technology). Professional memberships include the American Water Works Association and the Canadian Society of Civil Engineering. Research emphasizes interdisciplinary collaboration, with over 100 publications in journals such as Water Research, Bioresource Technology, and Environmental Science & Technology. Key contributions include novel radio frequency thermal hydrolysis systems and probabilistic health risk assessments of biosolids use.

Chenghao Liuمشاهده پروفایل

پژوهشگر ارشد

• Molecular Design
• Chemistry
• Computer Science
• +۳ مورد دیگر

Chenghao Liu is a postdoctoral Research Fellow at the California Institute of Technology (Caltech), where he is supervised by Prof. Frances Arnold starting in 2025. He previously completed his Ph.D. at McGill University (2019-2024) under Prof. Dmytro Perepichka and collaborated with Prof. Yoshua Bengio at Mila. He co-founded the protein design startup Dreamfold (2021-2025) and was a Vanier Canada Graduate Scholar (2021-2024). His research bridges chemistry and computer science, focusing on AI-driven molecular and materials design. His recent publications highlight work on generative AI for 3D molecule synthesis (SynCoGen), GFlowNets for synthesizable molecular generation (RGFN), and covalent organic frameworks with tailored electronic properties. He has contributed to machine learning applications in chemistry, including protein backbone generation and CO₂ photoreduction catalysts. His work spans molecular design , AI algorithms , and materials discovery . Scientific Awards Vanier Canada Graduate Scholar (2021-2024)

Martin Lysyمشاهده پروفایل

دانشیار

• Statistics
• Quantitative Finance
• Biostatistics
• +۳ مورد دیگر

Martin Lysy is an Associate Professor and Director of the Statistical Consulting and Survey Research Unit at the University of Waterloo. His primary affiliation is with the Department of Statistics, and he is actively involved in the fields of Statistics and Quantitative Finance. His research focuses on developing advanced statistical methodologies with applications in biostatistics, environmental science, neuroscience, and machine learning. Lysy’s work often involves creating robust computational tools and software packages to address complex problems in these domains. Key research interests include Bayesian inference, spatial statistics, stochastic processes, and the statistical analysis of biological systems such as microrheology and neural spike trains. He has contributed to the development of R packages like SpatialGEV for spatial extremes and nichROVER for ecological niche modeling. His recent work emphasizes methodological innovations in capture-recapture methods for population estimation, environmental data analysis, and computational efficiency in Bayesian frameworks. Notable contributions include studies on the lockdown effects on air quality in Ontario, Canada, and interdisciplinary projects integrating machine learning with systems pharmacology. Lysy’s research bridges theoretical statistics with real-world applications, ensuring practical relevance across diverse scientific disciplines.

Xuemin Shenمشاهده پروفایل

استاد

• 6G Networks
• AI-Driven Communications
• Digital Twin Technology
• +۵ مورد دیگر

Xuemin (Sherman) Shen is a University Professor at the University of Waterloo, specializing in advanced communication systems and AI-driven network optimization. His research focuses on 6G networks, digital twin technology, vehicular systems, and generative AI applications in wireless communications. He leads initiatives in edge computing, federated learning, and secure physical layer communications. Key research areas include 6G architecture design, intelligent transportation systems, and AI integration into network management. His work bridges theoretical advancements with practical implementations in areas like OTFS modulation, MIMO equalization, and hybrid-field antenna systems. Recent projects emphasize digital twin frameworks for network orchestration, immersive communications via edge computing, and trustworthy semantic communication systems. His contributions span both foundational studies and applied solutions in wireless security, distributed learning, and terahertz communication systems.

Ladan Tahvildariمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• Self-Adaptive Systems
• +۵ مورد دیگر

Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.