جستجوی استاد راهنمای دانشگاهی

Aukosh Jagannathمشاهده پروفایل

دانشیار

• Probability theory
• Mathematical foundations of data science
• Mathematical physics
• +۳ مورد دیگر

Aukosh Jagannath is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, with cross-appointments in the Department of Applied Mathematics and the Cheriton School of Computer Science. He is also an affiliate of the Perimeter Institute and Waterloo.AI. His research focuses on probability theory, mathematical foundations of data science, and mathematical physics, particularly in areas like high-dimensional learning, statistical-computational tradeoffs, and spin glasses. Before joining Waterloo, he held postdoctoral positions at Harvard University and the University of Toronto, and completed his PhD in mathematics at NYU’s Courant Institute under Gerard Ben Arous, alongside an undergraduate degree in math and physics from NYU. His work has been supported by grants from NSERC, the Canada Research Chairs program, the Canadian Foundation for Innovation, and the Ontario Research Fund. He has contributed to groundbreaking research on topics such as stochastic gradient descent dynamics, spectral alignment in neural networks, and the theoretical underpinnings of deep learning. Jagannath has advised numerous graduate and postdoctoral researchers, including current PhD students Taj Jones-McCormick and Varnan Sarangian, and former advisees like Aseem Baranwal (now at XTY Labs) and Yiming Xu (now at the University of Kentucky). His awards include an Outstanding Paper Award at NeurIPS 2022 for work on high-dimensional SGD dynamics.

Jason Bramburgerمشاهده پروفایل

استادیار

• Dynamical Systems
• Data-Driven Methods
• Pattern Formation
• +۳ مورد دیگر

Jason Bramburger is an Assistant Professor at Concordia University and an adjunct professor at McGill University. He is a member of the CRM Applied Math Lab and contributes to the Physica D Editorial Board. His research focuses on dynamical systems, data-driven discovery, pattern formation, and polynomial optimization applied to differential equations. He holds a Ph.D. from the University of Ottawa (2017), with postdoctoral fellowships at Brown University and the University of Victoria, and prior experience as an acting instructor at the University of Washington. Research interests include multiscale dynamics, nonlinear Floquet theory, and Koopman operator methods. Recent work emphasizes data-driven analysis of dynamical systems, with applications to Poincaré maps, invariant measures, and control systems. Key contributions include methods for discovering Poincaré maps via sparse regression and bounding long-term system behaviors using convex optimization. Notable publications span topics like real-time motion detection, localized patterns in networks, and ergodic optimization. Bramburger actively contributes to open-source projects, including a textbook on Data-Driven Methods for Dynamic Systems , and maintains GitHub repositories for computational tools in dynamical systems and neural networks.

Luc Devroyeمشاهده پروفایل

استاد

• Computer Science
• Probability Theory
• Algorithms
• +۴ مورد دیگر

Luc Devroye is a Professor in the School of Computer Science at McGill University, Montreal. He joined McGill in 1977 after completing his PhD at the University of Texas in 1976. His research spans probabilistic analysis of algorithms, random structures, and typography. Notable contributions include work on random trees, data structures, and simulation methods. Affiliations : McGill University since 1977, School of Computer Science. Education : PhD in Computer Science from the University of Texas (1976), studies at the University of Leuven and Osaka University. Research Interests : Algorithms, probability theory, random graphs, data structures, and typography. Focus on average-case analysis, random tree structures, and simulation techniques. His work emphasizes theoretical foundations with practical applications, including contributions to random number generation and algorithmic efficiency. He advises students in theoretical computer science and maintains an active research agenda with over 500 publications. Teaching : Courses like COMP 252 (Algorithms), COMP 690 (Probabilistic Analysis of Algorithms). Students : Mentored numerous PhD and MSc students, many now in academia and industry.

Mohammad R. Salavatipourمشاهده پروفایل

استاد

• Approximation Algorithms
• Randomized Algorithms
• Hardness of Approximation
• +۳ مورد دیگر

Mohammad R. Salavatipour is a Professor and Associate Dean Graduate Studies in the Department of Computing Science at the University of Alberta. He has taught advanced courses such as Advanced Algorithms (Fall 2025), Approximation Algorithms (Winter 2022, Winter 2018), and Algorithms for Streaming and Big Data (Fall 2019). Research Interests include approximation algorithms, randomized algorithms, hardness of approximation, algorithmic graph theory, combinatorial optimization, and probabilistic/discharge methods. He has contributed to theoretical computer science through course design and student mentorship. Students supervised encompass current and former Ph.D. and M.Sc. candidates, including Kinter Ren , Mohsen Mohammadi , and Babak Behsaz , many of whom now hold positions at institutions like Google, Microsoft, and universities.

Wei Yuمشاهده پروفایل

استاد

• Network Information Theory
• Wireless Communications
• Optimization
• +۶ مورد دیگر

Professor Wei Yu is a Canada Research Chair (Tier 1) in Information Theory and Wireless Communications at the University of Toronto , Department of Electrical and Computer Engineering. He received his Ph.D. in Electrical Engineering from Stanford University (2002) and B.A.Sc. in Computer Engineering and Mathematics from University of Waterloo (1997). Key roles: IEEE Fellow, Fellow of Canadian Academy of Engineering, Member of Royal Society of Canada Editorial leadership: IEEE Transactions on Wireless Communications, IEEE Transactions on Information Theory Research Interests span network information theory, convex optimization, wireless communications, and broadband access networks. His work focuses on multiuser information theory, MIMO systems, and 5G/6G network optimization. Publications reflect expertise in reconfigurable intelligent surfaces (RIS), covariance-based activity detection, and fractional programming for signal processing. Scientific Recognition includes multiple IEEE Best Paper Awards (2008, 2017, 2021), NSERC Steacie Fellowship (2015), and the 2024 IEEE Joint Communications/Information Theory Society Paper Award. He is a Clarivate Highly Cited Researcher. Teaching Contributions include ECE1502 (Information Theory), ECE431 (Digital Signal Processing), and ECE1505 (Convex Optimization), with a focus on mathematical foundations and practical applications in communications.

• Stochastic Processes
• Stochastic Models of Evolving Populations
• Probability Theory
• +۶ مورد دیگر

Donald Dawson is a Distinguished Research Professor at the Department of Mathematics and Statistics , Carleton University. His work spans over five decades, focusing on stochastic processes, population genetics, and interacting particle systems. Research Interests : Stochastic Processes, Stochastic Models of Evolving Populations, Measure-Valued Diffusions, Large Deviations, and Multiscale Analysis. His research often bridges theoretical probability with applications in biology and physics. Scientific Awards : CRM-Fields Institute Prize (2004) Article Trends : His recent publications emphasize mean-field models, interacting branching processes, and ultrametric systems, reflecting a sustained focus on stochastic dynamics and their mathematical foundations. Supervision : Available for honours project supervision. His career includes mentoring roles in stochastic population systems and queueing theory.

Yllias Chaliمشاهده پروفایل

استاد

• Natural Language Processing
• Text Summarization
• Text Generation
• +۴ مورد دیگر

Yllias Chali is a Professor at the Department of Mathematics and Computer Science, University of Lethbridge, Canada. His research focuses on Natural Language Processing , Artificial Intelligence , and Human Language Technologies , with a focus on text summarization, question answering, and knowledge representation. He has supervised numerous graduate students and secured funding from NSERC for projects in these areas. Teaching : Courses include Fundamentals of Programming I , Artificial Intelligence , and Statistical Methods and Machine Learning . Research Trends : Recent publications emphasize abstractive text summarization using BART, graph attention networks , multi-hop question generation , and faithfulness in NLP models. His work integrates machine learning , syntactic analysis , and semantic modeling . Advising : Collaborates with graduate students on NLP projects, including Narjes Delpisheh and Elozino Egonmwan. Funded by NSERC. Labs & Teams : Leads NLP research initiatives at the University of Lethbridge, contributing to computational linguistics and machine learning advancements.

Vijay Bhattiproluمشاهده پروفایل

استادیار

• Approximation Algorithms
• Hardness of Approximation
• Polynomial Maximization over Convex Sets
• +۴ مورد دیگر

Vijay Bhattiprolu serves as an Assistant Professor in the Department of Combinatorics & Optimization at the University of Waterloo, where his research bridges theoretical computer science and advanced mathematical disciplines. His work focuses on fundamental questions in computational complexity and optimization theory, with significant implications for algorithm design and analysis. Dr. Bhattiprolu's academic journey includes a Ph.D. from Carnegie Mellon University (2019) under Venkat Guruswami, a postdoctoral fellowship at Princeton University and the Institute for Advanced Study (2019-2022) as part of the Simons Collaboration on Algorithms and Geometry, and undergraduate studies at the University of Illinois at Urbana-Champaign (2014) with research mentorship from Sariel Har-Peled and Mahesh Viswanathan. Ph.D. in Computer Science, Carnegie Mellon University (2014-2019) Postdoc, Princeton University / Institute for Advanced Study (2019-2022) B.Sc., University of Illinois at Urbana-Champaign (2011-2014) His research program centers on approximation algorithms, hardness of approximation, and their deep connections to functional analysis and convex geometry. Key themes include polynomial maximization over convex sets, spectral theory, asymptotic convex geometry, and sum of squares methods, with applications to fundamental optimization problems. This interdisciplinary approach leverages mathematical tools to establish tight bounds on computational efficiency and develop novel algorithmic frameworks. Dr. Bhattiprolu's publication record reveals consistent contributions to theoretical foundations of optimization, particularly in matrix norm approximation, quadratic form maximization, and geometric methods in algorithm design. His work frequently appears in premier venues including STOC, FOCS, and SICOMP, demonstrating sustained impact on understanding computational limits and advancing approximation techniques. He currently mentors three graduate students: Yang Xiao (Ph.D., co-advised with Chaitanya Swamy), Jacob Skitsko (Ph.D., co-advised with Kostya Pashkovich), and Martin Liu (Masters). His teaching portfolio includes advanced courses in approximation algorithms (CO759) and core optimization theory (CO250, CO370), reflecting his commitment to training the next generation of theoretical computer scientists.

• Probability Theory
• Stochastic Processes
• Random Walks
• +۴ مورد دیگر

Alexander Fribergh is an Associate Professor at the Department of Mathematics and Statistics under the Faculty of Arts and Sciences at the University of Montreal. His research focuses on Probability Theory , Stochastic Processes , and Statistical Physics , with applications in Random Walks and Mathematical Analysis . Research Areas : Probability Theory, Random Walks in Random Environments, High-dimensional Lattice Models, Percolation Theory, Scaling Limits in Stochastic Systems Key Projects : Lead Researcher: Random Walks in Random Environments and Traps (2022–2029) Lead Researcher: Étude des marches aléatoires biaisées sur des clusters de percolations (2015–2019) Co-researcher: Centre de recherches mathématiques (CRM) (2015–2023) Grants : FRQNT - Programme de regroupements stratégiques (2022–2029) NSERC - Discovery Grant (2020–2027) FRQNT - Programme de subvention à la découverte (2015–2022) Teaching : Courses include MAT-2719 – Marches et graphes aléatoires , MAT-6701 – Probabilités , and MAT-6717 – Probabilités . Student Supervision : Supervised 8 theses (2017–2024) across Master's and Doctoral levels, including Chanel-Agouès Emile (2024, Master's), Davignon Élise (2024, Doctoral), and Ouimet Frédéric (2019, Doctoral).

Jane Breenمشاهده پروفایل

استادیار

• Combinatorial matrix theory
• Spectral graph theory
• Markov chains
• +۳ مورد دیگر

Jane Breen is an Assistant Professor in the Department of Mathematics and Statistics at Ontario Tech University, within the Faculty of Science. Previously, she held a postdoctoral position at Iowa State University under Dr. Steve Butler, and completed her Ph.D. at the University of Manitoba under Dr. Steve Kirkland. Her research focuses on combinatorial matrix theory, linear algebra applications in network science, and spectral properties of Markov chains. She specializes in analyzing directed graphs' influence on finite Markov chains and the role of eigenvalues/eigenvectors in system behavior. Education: Ph.D. in Mathematics from University of Manitoba (supervised by Dr. Steve Kirkland) Postdoctoral training at Iowa State University (with Dr. Steve Butler) Research Expertise: Her work bridges combinatorial theory and applied mathematics, emphasizing spectral graph theory and random walks on graphs. She explores how spectral properties of transition matrices influence modeled systems, particularly in network science contexts. Awards: No specific awards listed in available materials. Advising/Grants: No current students or grant details provided. Advising history not explicitly documented here.

• 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.

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.

• Probability
• Combinatorics
• Mathematics
• +۳ مورد دیگر

J. E. Paguyo is a Britton Postdoctoral Fellow in the Department of Mathematics & Statistics at McMaster University, mentored by Shui Feng. He holds a PhD in Mathematics from the University of Southern California (supervised by Jason Fulman) and a BS in Mathematics from the University of California, Davis. His research focuses on probability and combinatorics, with a particular emphasis on limit theorems and random discrete structures. He describes himself as a 'West Coast Coin Flipper' and has an Erdős number of 3. His research interests span the intersection of probability and combinatorics, including topics like random parking functions, hierarchical models, and stochastic processes. His recent work applies Stein's method to analyze limit theorems across various random structures. Notable themes in his articles include asymptotic behavior, cycle structures in discrete systems, and stochastic convergence in hierarchical models. Paguyo's academic journey includes postdoctoral research at McMaster and prior education on the West Coast of the U.S. He is reachable at paguyoj@mcmaster.ca and typically resides in Hamilton Hall 311.

Myron Hlynkaمشاهده پروفایل

استاد

• Queueing Theory
• Applied Probability
• Stochastic Processes
• +۳ مورد دیگر

Dr. Myron Hlynka is a Professor and Chair of the Actuarial Science Program in the Department of Mathematics and Statistics at the University of Windsor, part of the Faculty of Science. He holds a Ph.D. in Mathematics from Penn State University. His research focuses on Queueing Theory, Applied Probability, Stochastic Processes, and Operational Research, with notable applications to healthcare systems and disease modeling. He has advised multiple graduate students, including T. Sajobi, D. Chang, and C. Ramasundarahettige. His work explores theoretical queueing models across diverse domains, such as telecommunications, healthcare, and traffic systems. Recent research highlights include modeling medical interventions using game theory and Markov chains, analyzing parking lot dynamics, and studying sleep patterns as stochastic processes. Dr. Hlynka has contributed to software tools for queueing analysis and has published extensively on topics ranging from Laplace transforms to Fibonacci sequence applications. His research emphasizes practical applications of stochastic models to real-world challenges, including optimization of service systems, risk assessment, and performance evaluation. He maintains an active role in academic leadership through his chairmanship and contributions to curricula in actuarial science.

Denis Larocqueمشاهده پروفایل

استاد

• Non-parametric statistical methods
• Data mining
• Multidimensional Data Analysis
• +۶ مورد دیگر

Denis Larocque is a Full Professor in the Department of Decision Sciences at HEC Montréal, specializing in statistical methods, data mining, and multidimensional data analysis. He serves as Director of the Doctorate in Administration (Ph.D.) program and is affiliated with the GERAD (Group for Research in Decision Analysis) and CIRRELT (Interuniversity Research Center on Enterprise Networks, Logistics and Transport). His academic background includes a M.Sc. and Ph.D. in Statistics from the University of Montreal. His research focuses on non-parametric statistical methods, survival analysis, reinforcement learning applications in transportation systems, and machine learning techniques such as random forests. Recent work includes studies on traffic signal control optimization, causal inference in medical data, and predictive modeling in healthcare. Larocque has supervised over 100 students and projects, including 2 doctoral theses and 4 master’s theses in the last five years. He teaches advanced data science courses and has contributed to interdisciplinary research across transportation, healthcare, and marketing domains. His publications span journals in statistics, transportation engineering, and bioinformatics. He actively collaborates with industry partners, applying statistical and machine learning methodologies to solve real-world problems in sectors such as finance, logistics, and healthcare. His work emphasizes practical applications of data-driven decision-making and methodological advancements in statistical computing.