جستجوی استاد راهنمای دانشگاهی

George Labahnمشاهده پروفایل

استاد

• Computer Algebra
• Symbolic-Numeric Computation
• Computational Finance
• +۲ مورد دیگر

George Labahn is Professor and Director of the Symbolic Computation Group at the University of Waterloo's David R. Cheriton School of Computer Science. His research spans computer algebra, computational finance, and pen-based mathematical interfaces. Key contributions include fundamental algorithms for matrix normal forms (Hermite/Smith forms), symbolic-numeric polynomial computation, and development of the MathBrush system for handwritten mathematics recognition. Research focuses on efficient algorithms for polynomial matrix arithmetic, rational approximation, and solving differential equations with elliptic coefficients. Computational finance work develops numerical methods for option pricing under jump diffusion models. Current projects include fast Hermite form computation and rank-sensitive matrix algorithms. Serves as associate editor for Journal of Symbolic Computation (JSC) and previously for ACM Transactions on Mathematical Software (TOMS). Authored core Maple programming guides and implemented key packages for symbolic integration, differential equations, and graphics in Maple.

Kate Larsonمشاهده پروفایل

استاد

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning
• +۳ مورد دیگر

Kate Larson is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. Her research explores artificial intelligence with emphasis on multiagent systems, reinforcement learning, and AI applications for sustainability and climate challenges. She employs game-theoretic approaches to address coordination and cooperation in complex systems. Education Ph.D. in Computer Science from Carnegie Mellon University (2004), M.Sc. from Washington University in St. Louis (1999), and B.Sc. from Memorial University of Newfoundland (1997). Research Focus Professor Larson's work bridges theoretical AI and practical societal impacts. Key areas include: Designing cooperative AI frameworks for multiagent environments Developing value-alignment mechanisms for ethical AI Applying reinforcement learning to climate change mitigation Creating robust AI systems through game-theoretic guarantees Publication Trends Her recent publications (2024-2025) demonstrate strong focus on AI safety, game-theoretic foundations, and multimodal learning systems. Dominant themes include: algorithmic alignment techniques, multi-agent risk management, and interpretable AI architectures, with increasing emphasis on real-world climate and sustainability applications. Awards & Recognition No major scientific awards mentioned in source materials. Research Infrastructure No specific labs or teams detailed in available information.

Leili Rafiee Sevyeriمشاهده پروفایل

استادیار

• Hybrid Symbolic-Numeric Computation
• Scientific Computing
• Computer Algebra
• +۳ مورد دیگر

Leili Rafiee Sevyeri is an Assistant Professor in the Teaching Stream at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. Her research focuses on hybrid symbolic-numeric computation, scientific computing, and computer algebra. She holds a PhD and MSc in Applied Mathematics from Western University (2020 and 2016, respectively). Her teaching spans theoretical computer science, numerical methods in finance/business, and introductory computer science courses. She is based in location MC 6431 on campus and can be reached at leili.rafiee.sevyeri@uwaterloo.ca . Research interests include developing computational methods at the intersection of symbolic and numeric techniques, with applications in linear algebra, polynomial systems, and matrix theory. Recent work emphasizes interpolation methods, Bohemian matrix analysis, and approximate GCD algorithms. Her publications (e.g., Google Scholar ) reflect a focus on advancing computational tools for scientific and mathematical problems. No scientific awards are explicitly listed in the provided information. Advising and grant details are not mentioned, but her academic webpage provides further details on ongoing and past projects. She is actively involved in computational mathematics education and research within the university's Faculty of Mathematics.

• Operations Research
• Healthcare Systems Optimization
• Inventory Management
• +۳ مورد دیگر

Hossein Abouee Mehrizi is a Professor at the University of Waterloo, specializing in operations research and healthcare systems optimization. His affiliations include roles in the Data Analytics group, Applied Operations Research, Health Care, and Supply Chain Management and Logistics. His research focuses on optimizing service systems, healthcare logistics, inventory management, and data-driven decision-making. Key research areas include equity-efficiency trade-offs in service systems, perishable inventory management (e.g., blood products), spatial resource allocation, and dynamic service policies in healthcare. He has contributed to pandemic-era staff scheduling strategies and experimental analyses of healthcare cancellation rates. His work frequently integrates stochastic modeling, robust optimization, and game theory frameworks. Recent publications explore topics such as geographic virtual pooling of hospital resources, departure time choice models in urban traffic, and leveraging crowdsensing for social welfare. His methodologies emphasize data-driven approaches and asymptotic analysis in complex systems.

• Online Algorithms
• Resource Allocation
• Energy Systems Optimization
• +۴ مورد دیگر

Bo Sun is a Postdoctoral Scholar and Sessional Lecturer at the University of Waterloo. His research focuses on online algorithms, resource allocation, energy systems optimization, and machine learning applications. He is affiliated with the Post Doctoral Scholars, Visitors, and Sessional Lecturers groups at the institution. His work bridges theoretical computer science with practical challenges in energy networks, telecommunications, and distributed systems. Key research interests include developing fair and efficient algorithms for dynamic resource allocation problems, integrating machine learning predictions into competitive online algorithms, and optimizing spatiotemporal workloads with carbon-aware strategies. His publications address topics like group fairness in multi-class systems, carbon footprint reduction in workload management, and energy-efficient resource allocation in 5G networks. Recent work has explored Pareto-optimal algorithms for energy markets, robust online mechanisms with switching costs, and uncertainty-quantified predictions in algorithm design. Despite prolific publication activity, no scientific awards or grants are explicitly mentioned in the provided information. Bo Sun advises no formally listed students but has contributed to collaborative projects involving electric vehicle networks, battery swapping stations, and microgrid energy management. His research often involves interdisciplinary approaches combining operations research, computer science, and applied mathematics to solve real-world optimization challenges.

Qi-Ming Heمشاهده پروفایل

استاد

• Operations Research
• Applied Probability
• Queueing Theory
• +۵ مورد دیگر

Dr. Qi-Ming He is a Professor in the Department of Management Sciences at the University of Waterloo, within the Faculty of Engineering. His research focuses on Operations Research, Applied Probability, and Matrix Analytic Methods, with applications to queueing systems, inventory management, and telecommunications. He holds BSc and PhD degrees from the University of Science and Technology of China (1984, 1989) and a second PhD from the University of Waterloo (1996). His research interests span algorithmic probability, stochastic processes, and matrix analytic techniques applied to real-world systems such as manufacturing and healthcare logistics. Current projects involve phase-type distributions, queueing system design, and inventory optimization under multiple-item demand constraints. He has organized international conferences like CanQueue 2013 and contributed to proceedings of Matrix-Analytic Methods (MAM) conferences. Dr. He has published extensively in top journals including *Naval Research Logistics*, *European Journal of Operational Research*, and *Stochastic Models*. His work bridges theory and practice, addressing challenges in service systems, supply chains, and healthcare operations. He is active in academic service, serving on editorial boards and as a member of professional societies like INFORMS and CORS. Teaching responsibilities include graduate courses in stochastic processes, operations research, and matrix-analytic methods. He has taught at institutions worldwide, including Tsinghua University and City University of Hong Kong.

Stephen Wattمشاهده پروفایل

استاد

• Symbolic Computation
• Computer Algebra Systems
• Programming Languages
• +۵ مورد دیگر

Stephen Watt is a Professor at the University of Waterloo, affiliated with the Department of Computer Science within the Faculty of Mathematics. He holds a PhD in Computer Science (1986), MMath in Applied Mathematics (1981), and BSc in Honors Mathematics and Physics (1979). His research focuses on enabling computers to handle mathematics intelligently, with key areas including programming languages for mathematical software, symbolic computation algorithms, computer algebra system interoperability, mathematical knowledge management, and STEM education technology. His work spans theoretical and applied domains, such as developing algorithms for block matrices and handwriting recognition systems like the Legendre-Sobolev approach. He has contributed to initiatives like the International Mathematical Knowledge Trust (IMKT) and collaborated on projects like InkML for mathematical collaboration. Recent publications explore generative AI in STEM assessments, software portability, and GPU-optimized arithmetic operations. Dr. Watt’s research also addresses historical perspectives on symbolic computation and its evolution, alongside modern challenges in mathematical collaboration tools and digital ink compression. His contributions span conferences such as ISSAC, CICM, and SYNASC, reflecting interdisciplinary engagement with both theoretical and applied computer science.

Luis Lehnerمشاهده پروفایل

استاد پژوهش

• Strong Gravity
• Gravitational Wave Astronomy
• General Relativity
• +۷ مورد دیگر

Luis Lehner holds the Carlo Fidani Rainer Weiss Chair in Theoretical Physics at the Perimeter Institute for Theoretical Physics and is an Associate Professor in the Department of Physics at the University of Guelph. He also holds adjunct roles at the University of Wisconsin-Milwaukee and Louisiana State University. His research focuses on strong gravity regimes, testing alternative theories of gravity using gravitational wave detectors, and exploring higher-dimensional scenarios and AdS/CFT correspondence. He is a prominent figure in gravitational wave astronomy, working on multimessenger observations and developing frameworks to probe General Relativity's validity. Lehner has held leadership positions including Deputy Faculty Chair at Perimeter Institute and advisory roles at KITP and ICTP-SAIFR. His work bridges theoretical physics with observational astrophysics, emphasizing black hole dynamics, relativistic hydrodynamics, and turbulence. Recent publications highlight advancements in gravitational wave signal analysis, black hole mimicker studies, and nonlinear gravity effects. Education : Not explicitly listed in text, but his roles imply PhD-level training in Physics. His awards include membership in the National Academy of Sciences of Argentina, the Buchalter Cosmology Prize, and fellowships from APS and the Institute of Physics. He actively participates in international collaborations like the Gravitational Wave International Committee and contributes to detector networks like LIGO-Virgo.

David Yevickمشاهده پروفایل

استاد

• Photonics
• Optical Systems Design
• Machine Learning Applications in Optics
• +۳ مورد دیگر

David Yevick is a Professor at the University of Waterloo, affiliated with the Photonics and Atomic, Molecular, & Optical Physics research group. He leads the Advanced Optical Systems Lab and specializes in integrating machine learning with photonics, nonlinear optics, and quantum systems. His work spans optical communication systems, fiber nonlinearity mitigation, and quantum state analysis. Research interests include applying neural networks and deep learning to optical device performance prediction, material science simulations, and signal processing. Recent trends in his publications focus on combining ML techniques with optical systems, such as using random forests for CNT TFET analysis and variational autoencoders for phase transition modeling. His lab explores cutting-edge topics like self-phase modulation compensation in WDM systems and entropy-regulated data balancing. The Advanced Optical Systems Lab also develops novel photonic crystal designs and fiber compensation algorithms.

Jeffrey Paiمشاهده پروفایل

استاد

• Risk management
• Derivatives statistical modeling
• Term structure of interest rates
• +۳ مورد دیگر

Jeffrey Pai is a Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. With over 30 years of academic experience spanning multiple countries, he specializes in actuarial science, risk modeling, and financial derivatives. His research examines livestock/crop insurance, weather derivatives, and Bayesian statistical methods for risk assessment. Research encompasses quantitative risk management frameworks, stochastic modeling of insurance products, and econometric analysis of financial instruments. Recent work focuses on parametric insurance for natural disasters, mortality catastrophe modeling, and agricultural risk transfer mechanisms using advanced statistical approaches. Publications demonstrate consistent focus on practical applications of actuarial science, particularly in agricultural insurance and weather-related financial instruments. Recent trends show increased emphasis on Bayesian methods, catastrophe modeling, and developing market applications. Awards: Students' Teacher Recognition Awards (2021, 2002) CAS Best Paper Award (2016) Literati Network Outstanding Paper Award (2011) Featured in MACLEAN's Guide to Canadian Universities (2004) Extensive teaching includes courses in life contingencies, risk theory, and actuarial modeling. Secured research funding from international agencies including SSHRC (Canada), Society of Actuaries (USA), and UK Development Partnerships.

Andriy Prymakمشاهده پروفایل

استاد

• Discrete Geometry
• Convex Geometry
• Approximation Theory
• +۳ مورد دیگر

Andriy Prymak is a Professor of Mathematics at the University of Manitoba's Faculty of Science. His research focuses on Discrete and Convex Geometry, Approximation Theory, Combinatorics, Analysis, and Numerical Analysis. He actively supervises graduate and undergraduate students on projects related to geometric covering problems, convex body properties, and polynomial approximation. Recent work includes groundbreaking contributions to Hadwiger's covering conjecture, illumination problems, and minimal volume constant-width bodies. He holds an NSERC Discovery Grant for Multivariate Approximation studies. Prymak collaborates internationally, including with researchers like A. Arman and F. Dai, and has developed computational tools for geometric problems (e.g., GitHub repositories for chromatic number bounds). His advising spans M.Sc., Ph.D., and postdoctoral training, with notable student projects on cap bodies, hyperbolic cross polynomials, and Christoffel function estimates.

Igor Shinkarمشاهده پروفایل

دانشیار

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +۳ مورد دیگر

Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.

Laura Sanitàمشاهده پروفایل

دانشیار

• Combinatorial Optimization
• Approximation Algorithms
• Network Design
• +۳ مورد دیگر

Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milan, Italy. Previously, she held positions at TU Eindhoven (2020–2022) and the University of Waterloo, Canada, where she was an Assistant Professor (2012–2017) and later Associate Professor (2017–2020). She earned a Bachelor’s and Master’s in Management Engineering from Università di Roma Tor Vergata (2003–2005), followed by a PhD in Operations Research from Università Sapienza di Roma (2009). Her postdoctoral work (2009–2011) was at EPFL’s Discrete Optimization Group. Her research focuses on Combinatorial Optimization , Approximation Algorithms , Network Design , and Algorithmic Game Theory . Key contributions include advancements in node connectivity augmentation, graph stabilization, and polytope diameter analysis. She has received prestigious awards such as the NWO-VIDI Award (Netherlands), NSERC Discovery Accelerator Supplements, and the Early Researcher Award (Ontario). Laura co-organizes the Bocconi Theory Day (May 2024) and serves on program committees for conferences like SODA, ESA, and IPCO. She is an Associate Editor for Mathematical Programming , Mathematics of Operations Research , and Operations Research Letters . Current advisees include PhD students Sean Kafer, Dylan Hyatt-Denesik, and Lucy Verbeck. Her work bridges theoretical foundations and practical applications, with notable publications in Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Recent projects explore stabilization of capacitated matching games and iterative randomized rounding techniques for combinatorial problems.

• Reinforcement Learning
• Online Learning Algorithms
• Exploration-Exploitation Dilemma
• +۷ مورد دیگر

Csaba Szepesvári is a Professor in the Department of Computing Science at the University of Alberta and a Senior Staff Research Scientist at DeepMind, leading the Foundations team. He holds the Canada CIFAR AI Chair and is a Fellow at Amii. His research focuses on reinforcement learning (RL), online learning, and theoretical foundations of sequential decision-making. Key areas include exploration-exploitation strategies, Markov Decision Processes, and stochastic control. He has co-authored over 225 publications, including seminal works like Bandit Algorithms (2020) and Algorithms for Reinforcement Learning (2010). His academic affiliations include the Reinforcement Learning & Artificial Intelligence Lab (RLAI) at the University of Alberta and editorial roles at journals like Mathematics of Operations Research and the Journal of Machine Learning Research. Awards include the ECML/PKDD Test of Time Award (2016) and recognition for contributions to ICML, UAI, and other conferences. His work bridges theoretical insights with practical applications in AI, robotics, and automated decision systems. Research interests span efficient RL algorithms, statistical guarantees for offline learning, and the intersection of optimization and uncertainty quantification. Collaborations with institutions like DeepMind emphasize foundational advances in AI, while his teaching spans graduate and undergraduate courses in machine learning and algorithms. Key achievements include pioneering contributions to exploration strategies in bandits and RL, developing PAC-Bayes bounds for neural networks, and advancing understanding of double descent phenomena in overparameterized models. His lab (RLAI) is a hub for cutting-edge research in RL theory and applications.

Yves Lucetمشاهده پروفایل

استاد

• Computational Mathematics
• Optimization
• Convex Analysis
• +۶ مورد دیگر

Yves Lucet is a Professor in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds a PhD from the University of Toulouse. His research spans computational mathematics, optimization, and convex analysis, with a focus on algorithm development for computer-aided convex analysis and road design optimization. He leads the development of the CCA (Computational Convex Analysis) toolbox under Scilab, emphasizing hybrid symbolic-numerical algorithms. His work includes visualizing set-valued operators using Virtual Reality (VR) and collaborating on large-scale road design projects funded by NSERC, aiming to minimize construction costs while meeting safety and environmental constraints. Lucet advises graduate students and contributes to interdisciplinary teams involving mathematics, statistics, and engineering. His research also explores applications in computational geometry, image processing, and network communication. Education: PhD in Mathematics from the University of Toulouse. Research Interests: Computational Convex Analysis Algorithm Design for Optimization VR Visualization of Mathematical Operators Multi-objective Road Design Optimization Data-Driven Process Monitoring Key Projects: Development of the CCA toolbox, road design optimization funded by NSERC, and reinforcement learning approaches for prescriptive process monitoring. His work bridges mathematics and computer science, with applications in engineering and transportation.