جست‌وجوی استاد و پژوهشگر

Xin Bingمشاهده پروفایل

استادیار

• Data Science
• Theoretical Statistics
• Mathematical Statistics
• +۶ مورد دیگر

Xin Bing is an Assistant Professor at the University of Toronto, specializing in Data Science and Theoretical Statistics. His research focuses on high-dimensional statistics, latent factor models, and statistical methodology with theoretical guarantees. He holds a Ph.D. from Cornell University, an MS from the University of Washington, and a BS from Shandong University. His work addresses challenges in multivariate analysis, model-based clustering, and applications in genetics, neuroscience, and immunology. Recent contributions include advancements in kernel ridge regression, latent factor analysis, and robust federated learning. He actively publishes in top journals like The Annals of Statistics and Bernoulli . Key areas of exploration include developing efficient estimation algorithms for mixture models and understanding statistical-computational trade-offs. His GitHub repositories, such as LOVE and STRS , reflect his focus on overlapping clustering and self-tuning rank selection techniques.

Maryam Mehri Dehnaviمشاهده پروفایل

دانشیار

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Maryam Mehri Dehnavi is an Associate Professor of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing. Her research group, ParaMathics, focuses on scalable numerical methods, high-performance libraries, and compilers for cloud and parallel computing platforms. Her research interests span cloud computing, machine learning, numerical analysis, and compiler optimization. She has been recognized with awards including the Ontario Early Researcher Award (2021) and an NSF CRII grant. She has advised numerous students, many of whom have achieved notable academic and industry roles. Teaching: Applied Parallel Computing, Parallel Computing, Cloud Computing Leadership Roles: General Chair of PPOPP 2023, Keynote Speaker at SIAM PP 2024 Labs/Teams: ParaMathics Research Group Her work bridges theoretical advances in numerical methods with practical applications in high-performance computing, emphasizing efficiency and scalability across diverse architectures.

Qiang Sunمشاهده پروفایل

دانشیار

• Generative AI
• Trustworthy AI
• Next-generation Statistics
• +۸ مورد دیگر

Qiang Sun is an Associate Professor of Statistics and Computer Science at the University of Toronto (UofT), holding cross-appointments in the Department of Computer Science and the Department of Computer and Mathematical Sciences. He leads the StatsLE (Statistics, Learning, and Engineering) Lab, focusing on generative AI, trustworthy AI, and next-generation statistics. His research bridges statistical theory with applied challenges in industry, emphasizing ensemble learning, transfer learning, and reinforcement learning, with applications in tech, finance, and science. Education: PhD from the University of North Carolina at Chapel Hill (UNC-CH) BS in SCGY from the University of Science and Technology of China (USTC) Research Interests: Sun’s work spans foundational AI and statistical methods, including GenAI, robust algorithms, and interdisciplinary applications. His lab explores topics like resistant clustering, physical commonsense violation detection in gameplay, and microbiome analysis. Recent trends in his publications emphasize robustness (e.g., heavy-tailed errors), multimodal learning, and lifelong learning frameworks. Scientific Awards: Connaught Award James E. Grizzle Distinguished Alumni Noah’s Ark Distinguished Lecture Advising & Grants: While student names are not listed, his grants and collaborations reflect industrial and academic partnerships. He serves as an associate editor for the Electronic Journal of Statistics and as an area chair for conferences like AISTATS and COLT. Labs & Teams: The StatsLE Lab at UofT drives his research, integrating statistical theory with cutting-edge AI applications.

• Discrete Optimization
• Approximation Algorithms
• Mathematical Programming
• +۳ مورد دیگر

Zachary Friggstad is an Associate Professor in the Department of Computing Science at the University of Alberta. His research focuses on algorithm design for discrete optimization problems, including vehicle routing, facility placement, and scheduling. He specializes in approximation algorithms with proven performance guarantees, often leveraging convex relaxations like linear and semidefinite programming. Education: B.Sc. in Computing Science, University of Lethbridge, 2005 M.Sc. in Computing Science, University of Alberta, 2007 Ph.D. in Computing Science, University of Alberta, 2011 Research Interests: Discrete Optimization Approximation Algorithms Mathematical Programming Algorithm Design for NP-Hard Problems Graph Algorithms and Combinatorial Optimization His publications span over 50 works, with recent trends emphasizing fair clustering algorithms, TSP approximations, and narrative distillation in cross-media contexts. No scientific awards are explicitly listed in the provided text. He has advised no students in the listed records. His work bridges theoretical computer science and practical algorithmic solutions for complex optimization challenges.

Tony Humphriesمشاهده پروفایل

دانشیار

• Mathematical Hematopoiesis
• Numerical Analysis of Dynamical Systems
• Delay Differential Equations
• +۳ مورد دیگر

Dr. Tony Humphries is an Associate Professor in the Department of Mathematics and Statistics at McGill University, part of the Faculty of Science. His research focuses on mathematical hematopoiesis, numerical analysis of dynamical systems, and functional and delay differential equations (DDEs), particularly those involving state-dependent delays. He applies these methods to biological systems, with notable work on blood cell dynamics and cancer therapy modeling. Humphries' research integrates advanced numerical techniques with theoretical analysis, addressing challenges in DDE stability, bifurcations, and computational implementation. His work bridges pure mathematics (e.g., Lyapunov-Razumikhin theory) with applied problems in oncology, immunology, and drug intervention strategies. Key research areas include: Mathematical modeling of hematopoietic stem cell populations Analysis of state-dependent delay equations Numerical methods for stiff and nonlinear DDE systems Applications in chemotherapy-induced neutropenia and viral oncology His recent publications (2017–2024) explore bistability, chaos in DDE systems, and clinical applications like optimizing G-CSF administration. He collaborates across disciplines, linking mathematical theory with biomedical challenges.

Xu Wangمشاهده پروفایل

استاد

• Automated Statistical Learning
• Data Mining
• Stochastic Process
• +۴ مورد دیگر

Xu (Sunny) Wang is a Professor in the Department of Mathematics at Wilfrid Laurier University, affiliated with the Faculty of Science. Their research focuses on automated statistical learning, stochastic processes (particularly self-exciting point processes), mixture models, and applications of applied statistics in health and economics. They teach various mathematics courses and actively engage in interdisciplinary research collaborations. Research interests include data mining methodologies for healthcare and business analytics, with recent emphasis on modeling terror attacks using self-exciting processes, sleep apnea diagnosis via neural networks, and analyzing periodic communication patterns. Their work spans statistical theory (e.g., nonparametric methods) and practical applications like emergency call clustering and economic efficiency studies in Ghana. Publications reflect a strong computational-statistical foundation, combining mathematical rigor with real-world problem-solving across disciplines. Supervision opportunities are available for students interested in statistical modeling, data science, and interdisciplinary research projects.

• Machine Learning
• Statistical Learning Theory
• Kernel Methods
• +۴ مورد دیگر

Danica J. Sutherland is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), holding a Canada CIFAR AI Chair and affiliation with Amii (Alberta Machine Intelligence Institute). She leads research in machine learning theory, optimal transport, kernel methods, and fairness in AI. Previously, she was affiliated with the Toyota Technological Institute at Chicago (TTIC), the Gatsby Computational Neuroscience Unit (UCL), and completed her PhD at Carnegie Mellon University under Jeff Schneider. Education: PhD in Machine Learning (Carnegie Mellon University, 2016), B.A. in Computer Science (Swarthmore College, 2011). Her work spans theoretical foundations of deep learning, active learning, generative models, and applications of optimal transport. She co-developed the POT Python Optimal Transport library and advocates for trans rights through initiatives like Queer in AI. Research focuses on statistical learning theory, kernel-based methods for hypothesis testing, and scalable graph neural networks. Recent work includes studies on LLM finetuning dynamics, privacy-preserving generative models, and bias in language models. She teaches advanced courses on statistical learning theory and machine learning, emphasizing theoretical rigor and practical applications. Awards include an Outstanding Paper at ICLR 2025 and a Best Paper at ACM FAccT 2023. Her lab collaborates widely, with projects funded by CIFAR, NSERC, and industry partnerships. She advises a team of PhD and master’s students, prioritizing mentorship for queer and marginalized scholars. Labs/Teams: PI of the UBC Machine Learning Theory (ML-TN) group, co-lead of the Kantorovich Initiative (optimal transport research), and core member of the Canadian AI Institute (CAIDA). She actively contributes to open-source tools like POT and promotes inclusive AI through community initiatives.

Bruce Shepherdمشاهده پروفایل

استاد

• Optimization
• Algorithms
• Convex Geometry & Polyhedral Combinatorics
• +۵ مورد دیگر

Bruce Shepherd is a Professor in the Department of Computer Science at the University of British Columbia. Previously, he held roles at the London School of Economics (LSE), Bell Laboratories, and McGill University. His academic journey includes a BSc from the University of Victoria, followed by an MSc and PhD in Combinatorics and Optimization from the University of Waterloo under supervision of Dan Younger and Bill Pulleyblank. He has held prestigious fellowships, including NATO Postdoctoral and Von Humboldt, and served as acting director of the Institute of Applied Mathematics at UBC (2019-2020). Research focuses on optimization, algorithms, graph theory, and network design , with applications in real-time systems, scheduling, and pricing. Key contributions include work on multiflow topologies, Gomory-Hu trees, and resilient network design. He has authored over 80 papers and served on editorial boards for journals like Mathematical Programming and SIAM-MPS Series . Awards and Recognition : NATO Postdoctoral Fellowship (1990s) Von Humboldt Fellowship (1990s) Nuffield Foundation Award (1990s) James McGill Professor (McGill University, 2007-2017) Professional Activities : Co-editor of Mathematical Programming Series A (2007-2016). Visiting positions at Microsoft Research, University of Washington, and the Fields Institute. Organized workshops on discrete optimization, data science, and robust network design.

Mahsa Hosseiniمشاهده پروفایل

استادیار

• Operations Management
• Service Operations
• Online Platforms
• +۴ مورد دیگر

Mahsa Hosseini is an Assistant Professor in Supply Chain Management & Data Analytics at the Odette School of Business, University of Windsor. She holds a Ph.D. from the Rotman School of Management (University of Toronto), an MBA, and a B.Sc. in Industrial Engineering from Sharif University of Technology. Her research focuses on operations management challenges in service operations, online platforms, sharing economy systems, and fairness/diversity considerations in algorithmic decision-making. Education : Ph.D., Operations Management, Rotman School of Management, University of Toronto (2023) MBA, Sharif University of Technology B.Sc., Industrial Engineering, Sharif University of Technology Her work addresses dynamic optimization problems such as car-sharing network relocations and diverse recommendation algorithms. Key contributions include developing dynamic relocation policies improving car-sharing efficiency by 23% and analyzing satiation effects in recommendation systems. Awards : 2023 Fellowship in Management, University of Toronto 2023 Doctoral Completion Award, University of Toronto Dr. Hosseini has presented research at major conferences including INFORMS and CORS. Her advisory experience includes teaching roles at Rotman and industry positions in business intelligence and market segmentation planning. Current research explores long-term impacts of algorithmic fairness in digital platforms and dynamic resource allocation strategies.

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.

Hausi A. Müllerمشاهده پروفایل

استاد

• Quantum Computing
• Self-Adaptive Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Hausi A. Müller is a Professor at the Department of Computer Science , University of Victoria , and serves as Associate Dean of Research in the Faculty of Engineering. He holds leadership roles in IEEE, including Vice President of the IEEE Computer Society Technical & Conferences Activities Board (T&C) and 2024-25 Chair of the IEEE Quantum Technical Community (QTC) . Research focuses on Quantum Computing , Self-Adaptive Systems , and Cyber-Physical Systems Key projects include NSERC CREATE grants for Quantum Computing and Dependable IoT systems Developed foundational frameworks like DYNAMICO and SmarterContext for context-aware systems Scientific Awards include: 2011 IBM Canada CAS Research Project of the Year Award CASCON 2012 Best Paper Award ACM Senior Member (2016) Fellow of the Canadian Academy of Engineering (FCAE) and IEEE (LMIEEE) His quantum computing research explores hybrid quantum-classical algorithms, educational frameworks like QNotation , and bioinformatics applications such as RNA folding via QUBO models. Current work emphasizes practical quantum applications and proactive AIOps using digital twins.

• Numerical Analysis
• Scientific Computing
• Domain Decomposition Methods
• +۴ مورد دیگر

Conor McCoid is a Research Fellow in Mathematics at McMaster University, specializing in numerical analysis, scientific computing, and computational geometry. His research focuses on domain decomposition methods, spectral collocation techniques, and algorithm development for computational problems such as phase field models and geometric intersections. He holds a PhD from the University of Geneva, where he worked on domain decomposition under Martin Gander, and a Master's from Simon Fraser University in spectral collocation methods with Manfred Trummer. His postdoctoral work at McMaster involves accelerating phase field models for fractures and optimizing Schwarz methods. McCoid's research interests include developing robust algorithms for geometric intersections (e.g., triangles and tetrahedra), improving spectral collocation methods for boundary value problems, and prototyping machine learning algorithms for music analysis (e.g., Mozart classification). His GitHub repository includes code for tetrahedral intersections, adaptive Schwarz methods, and music data processing using tools like music21 and scikit-learn. His publications explore numerical methods for multigrid cycles, nonlinear solvers, and geometric algorithms, often emphasizing robustness and efficiency in computational contexts. Collaborations include work with Blaise Bourdin (McMaster), Felix Kwok (Université Laval), and Martin Gander (Geneva).

Douglas Downمشاهده پروفایل

استاد

• Performance Evaluation
• Stochastic Models
• Data Centres
• +۷ مورد دیگر

Dr. Douglas Down is a Professor in the Department of Computing and Software at McMaster University. His research focuses on performance evaluation, stochastic models, data centers, and scheduling algorithms. He is actively involved in the Digital & Smart Systems research cluster, addressing challenges in healthcare logistics, energy-efficient computing, and resource allocation. Dr. Down teaches CAS 736: Analysis of Stochastic Networks, exploring statistical modeling techniques and current research in stochastic networks. His work integrates machine learning and optimization to solve real-world problems, such as blood product inventory management and thermal-aware data center management. He has published extensively on topics including demand forecasting, inventory policies, and energy-aware workload assignment. While no specific awards are listed, his contributions to operations research and computing systems are evident through his prolific publication record. Dr. Down advises graduate students and collaborates on projects involving healthcare systems, cloud computing, and distributed systems. His research addresses both theoretical and applied challenges, with a focus on decision-making under uncertainty and system optimization.

Maryam Daryalalمشاهده پروفایل

استادیار

• Sequential decision making under uncertainty
• Stochastic programming
• Robust optimization
• +۵ مورد دیگر

Dr. Maryam Daryalal is an Assistant Professor in the Department of Decision Sciences at HEC Montréal, affiliated with the University of Montréal. She holds secondary memberships in CIRRELT (Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise) and GERAD (Groupe pour la Recherche en Analyse des Décisions). Her research focuses on sequential decision-making under uncertainty, stochastic/robust optimization, and their applications in telecommunications, energy systems, and supply chain management. Education: Ph.D. in Industrial Engineering, University of Toronto (2022) M.Sc. in Computer Science, Concordia University (2016) M.Sc. in Industrial Engineering, Amirkabir University of Technology (2013) B.Sc. in Industrial Engineering & Systems Analysis, Amirkabir University of Technology (2011) Research Interests: Her work develops novel methodologies for multistage stochastic programming and robust optimization, addressing challenges in telecommunications network migration, energy systems planning, and large-scale decision-making. She bridges theoretical advances with practical applications through innovative decomposition techniques like hybrid logic-based Benders decomposition. Awards: 2023 INFORMS Telecom Best Paper Award Judith Liebman Award (2021) NSERC Discovery Grant (2024-2029) Advising & Grants: Supervises PhD/MSc students in optimization and telecommunication projects. Leads IVADO grants (e.g., $40k R3AI project) and co-leads a $100k IVADO project on net-zero power grids. Active in Mitacs industry collaborations. Professional Engagement: Serves on INFORMS committees, organizes international conferences (e.g., CORS, INFORMS Computing Society), and reviews for top journals like Operations Research and Mathematical Programming.

Jacques Desrosiersمشاهده پروفایل

استاد

• Operations Research
• Transportation
• Optimization
• +۳ مورد دیگر

Jacques Desrosiers is a Full Professor at HEC Montréal in the Department of Management Science since 1989. He has been with the institution since 1978 and is a founding member of the Group for Research in Decision Analysis (GERAD). His research focuses on optimization, transportation systems, and algorithmic methods for solving complex linear and combinatorial problems. He has received prestigious recognitions, including the Grand Prize for Excellence in Research (2015) and Fellowship in the Royal Society of Canada (2001). His work spans contributions to column generation techniques, degenerate linear programming, and vehicle routing problems. Notable achievements include advancing the minimum mean cycle-canceling algorithm, vector space decomposition for large-scale problems, and developing efficient heuristics for logistics challenges. His research bridges theoretical advancements and practical applications, influencing transportation and operations management globally. Desrosiers has authored over 150 peer-reviewed articles, with recent focus on algorithmic optimization, primal degeneracy handling, and decomposition methods. His contributions to software tools like COIN-OR’s CLP further highlight his commitment to advancing computational methods in operations research. Awards: Royal Society of Canada Fellowship (2001), HEC Montréal Grand Prize (2015) Key Research Themes: Column Generation, Network Flow Algorithms, Linear Programming, Vehicle Routing, Degeneracy Resolution Labs/Teams: Co-founder and active member of GERAD, collaborating on optimization projects