جست‌وجوی استاد و پژوهشگر

Danica Sutherlandمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Statistical & Theoretical Machine Learning
• +۴ مورد دیگر

Danica Sutherland is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. Her research focuses on artificial intelligence and machine learning, with a particular emphasis on statistical and theoretical foundations, fairness in AI, and differentially private data generation. Academic Rank: Assistant Professor University: University of British Columbia School: Faculty of Science Department: Department of Computer Science Her work explores topics like neural network theory, kernel methods for fair representation learning, and differential privacy in image generation. She is affiliated with UBC's AMLTN (Applied Machine Learning) and CAIDA research centers. Notable awards include the AI4MATH Best Paper Award, Outstanding Paper at ICLR 2025, and the ACM FAccT Best Paper Award. She has supervised graduate theses on topics such as infinite-width neural networks, differentially private image generation, and invariant representation learning. At UBC, she teaches courses like Machine Learning II, Advanced Machine Learning, and Topics in Artificial Intelligence - Statistical Learning Theory. Her research leverages public data and theoretical insights to improve machine learning models while addressing privacy and fairness constraints.

Ashvin Goelمشاهده پروفایل

استاد

• Systems Software
• Cloud Computing
• Storage Systems
• +۵ مورد دیگر

Ashvin Goel is a Professor in the Department of Electrical and Computer Engineering and Department of Computer Science at the University of Toronto , where he leads research at the intersection of Operating Systems , Reliability Engineering , and Computer Security . His work focuses on ensuring software systems can withstand bugs and vulnerabilities, with publications in top venues like SOSP , OSDI , and FAST . He received his PhD in Computer Science and Engineering from Oregon Graduate Institute in 2003, with prior degrees from UCLA (MS) and IIT Kanpur (BS). Current research projects include runtime verification systems like Recon and intrusion recovery frameworks such as Taser , often in collaboration with Professor Angela Demke Brown. His team explores kernel instrumentation, file system consistency, and security recovery methods. NSERC Discovery Accelerator Award 2012 FAST Best Paper Award 2012 Google Faculty Research Award 2011 Netapp Faculty Award 2008 Ontario Early Researcher Award Professor Goel's recent publications address deterministic concurrency control, distributed graph mining, and storage system reliability. He teaches graduate courses in Dependable Software Systems and Distributed Systems , with prior course development in operating systems security and time-sensitive applications.

Yaoliang Yuمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization Algorithms
• Computer Vision
• +۵ مورد دیگر

Yaoliang Yu is an Associate Professor in the Department of Computer Science at the University of Waterloo, Canada. He holds a Ph.D. from the University of Alberta (2013), and M.Sc. and B.Sc. degrees from Fudan University, China (2008 and 2005, respectively). His research focuses on Machine Learning, particularly generative models, representation learning, optimization algorithms, and applications in computer vision and natural language processing. He actively explores topics such as diffusion models, federated learning, adversarial robustness, and algorithmic fairness. Affiliations: School of Computer Science, University of Waterloo Key Research Areas: Generative Modelling, Optimization Theory, Federated Learning, Adversarial Machine Learning Education: Ph.D. in Computer Science, University of Alberta (2013) M.Sc. in Computer Science, Fudan University (2008) B.Sc. in Computer Science, Fudan University (2005) Research Interests: Yu’s work bridges theoretical foundations and practical applications in machine learning. His recent efforts emphasize robustness in generative models (e.g., diffusion models), optimization methods for non-convex problems, and privacy-preserving federated learning. He also investigates adversarial attacks and defenses, such as data poisoning and model unlearning mechanisms. Publications: His recent work explores diffusion models with group equivariance, noise-aware federated learning, and convergence guarantees for optimization algorithms like Adam. These studies reflect a trend toward unifying theory and practice in scalable ML systems. Awards: No awards explicitly listed in the provided text. Advising & Grants: While specific grants are not detailed, his research portfolio suggests involvement in projects funded by Canadian NSERC or industry partnerships. He advises students on topics such as generative AI and optimization, though no student names are listed here.

Xi Heمشاهده پروفایل

استادیار

• Privacy and Security
• Big Data
• Machine Learning
• +۲ مورد دیگر

Xi He is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. Her research centers on privacy and security for big data, with a focus on developing trustworthy tools for machine learning and data exploration. She holds a Ph.D. from Duke University (2018) and an M.Sc. from Duke University (2015). Her work spans privacy-preserving data analytics, secure ML frameworks, and algorithmic fairness. Recent publications emphasize explainable AI for brain aging , neural tangent kernels , and graph-structured learning . She actively investigates transferability of covariance networks in neuroimaging contexts. Dr. He's articles reveal a strong emphasis on theoretical ML foundations applied to neuroscience, with innovations in model interpretability and high-dimensional data analysis. She leads research on graph-constrained algorithms and adaptive statistical methods.

• Biosystems Engineering
• Chemical Engineering
• Data Modeling
• +۳ مورد دیگر

Dr. Jason Morrison is an Associate Professor and Associate Head (Undergraduate) in the Department of Biosystems Engineering at the University of Manitoba. He holds degrees in Mechanical Engineering, Computer Science, and Computational Science from McMaster and Carleton Universities. His research focuses on creating simplified data models, optimizing model fits, and analyzing process outcomes, particularly in chemical spectra modeling, biological material analysis, and 3D printer performance optimization. He has received grants in both Information Technology and Materials and Chemical Engineering, emphasizing reproducible open-source tools for additive manufacturing and spectroscopy data analysis. Dr. Morrison’s work addresses challenges in proprietary data formats that hinder reproducibility and interoperability in these fields. His teaching awards reflect his dedication to student success in engineering education. Recent research spans agricultural applications (e.g., Fusarium detection in wheat), biomaterials development (e.g., hemp fiber processing), and medical device sterilization (3D-printed plastics). His publications (2015–2021) highlight interdisciplinary approaches bridging engineering, biology, and computer science. Education: B.Eng. in Mechanical Engineering, McMaster University (1993) B.Sc. Honours in Computer Science, McMaster University (1995) M.Comp.Sc. in Computational Science, Carleton University (1997) Ph.D. in Computational Science, Carleton University (2002) Research Interests: Optimization of mathematical models and data simplification Spectroscopic analysis of chemical/biological materials Additive manufacturing (3D printing) tooling and sterilization Ergonomics of agricultural machinery design Non-destructive testing via hyperspectral imaging Grants & Awards: Grants in Information Technology and Materials Engineering (details unspecified) Teaching awards (specific names unspecified) Labs/Teams: Not explicitly mentioned in profile; focus on interdisciplinary collaborations in biosystems engineering.

• 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.

Almut Burchardمشاهده پروفایل

استاد

• Functional Analysis
• Partial Differential Equations
• Probability
• +۴ مورد دیگر

Almut Burchard is a Professor in the Department of Mathematics at the University of Toronto, with research focusing on geometric problems in functional analysis, partial differential equations, probability, and network calculus. She has held positions at the University of Toronto since 2005, following appointments at the University of Virginia and Princeton University. Ph.D. in Mathematics, Georgia Institute of Technology (1994) Diplom in Mathematics, Universität Heidelberg (1989) Her research integrates symmetrization techniques , sharp inequalities , and nonlocal functionals , with applications to network delay analysis . Recent publications explore heat kernel monotonicity, Wasserstein-type interactions, and geometric optimization. She supervises graduate students and collaborates extensively in mathematics and computer science. Key article trends include geometric analysis , symmetrization , network calculus , and nonlocal PDE . Her work bridges pure mathematics with applied problems in communication systems. Scientific distinctions include the Alfred P. Sloan Research Fellowship (1999), Fields Institute Fellowship (2021), and Sigma Xi Ph.D. Thesis Award (1995). She has received teaching and research accolades, including the ACM Sigmetrics Best Student Paper Award. As a faculty member, she teaches MAT157 Analysis I and advises graduate students. Her interdisciplinary collaborations span mathematics, computer science, and engineering, particularly in network performance and geometric optimization problems.

Abdol-Reza Mansouriمشاهده پروفایل

استاد

• Sub-Riemannian Geometry
• Stochastic Analysis
• Malliavin Calculus
• +۶ مورد دیگر

Abdol-Reza Mansouri is a Professor in the Department of Mathematics & Statistics at Queen's University, serving as Coordinator of Graduate Studies. He holds a Ph.D. from Harvard University and degrees from McGill University. His research focuses on sub-Riemannian geometry, stochastic analysis, and geometric control theory, with applications in mathematical imaging and inverse problems. He actively supervises graduate students and postdoctoral fellows in pure and applied mathematics. Research interests include hypoelliptic operators, Malliavin calculus, topological obstructions in control systems, and mathematical problems in imaging. His academic experience includes a postdoctoral position at MIT and teaching roles spanning advanced courses like stochastic differential equations, control theory, and differential geometry. He has supervised numerous students and collaborates on interdisciplinary projects. Teaching highlights include courses on stochastic calculus, modern control theory, and differential manifolds. He co-organizes research seminars on topics like microlocal analysis and sub-Riemannian geometry. Mansouri is affiliated with the American Mathematical Society, SIAM, and MAA.

Michael Greenspanمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Object Recognition
• +۴ مورد دیگر

Michael Greenspan is a Professor in the Department of Electrical and Computer Engineering at Queen's University, affiliated with the Ingenuity Labs Research Institute. His expertise spans computer vision, image processing, and robotics. He holds a PhD in Systems and Computer Engineering from Carleton University and has extensive industry experience, including leading the Computational Video Group at the National Research Council of Canada. His research focuses on object recognition, motion planning, and applied computational geometry, with applications in autonomous robotics and 3D scene analysis. He has published over 30 technical papers and 3 patents, and collaborates with industry on applied projects. Education BSc in Physics and Applied Mathematics (1986), University of Toronto BASc and MASc in Electrical Engineering (1989, 1991), University of Ottawa PhD in Systems and Computer Engineering (1991), Carleton University Research Interests Object recognition and pose determination using geometric probing and minimalist template matching Efficient motion planning algorithms for robotic systems Computational geometry solutions like TINN for nearest neighbor problems Applications in autonomous robotic capture, LiDAR data analysis, and collaborative robotics Dr. Greenspan’s work bridges theoretical computer vision with practical robotic systems, emphasizing real-time performance and industrial relevance. His lab, the Robotics and Computer Vision Laboratory, develops algorithms for 3D localization, sensor fusion, and autonomous navigation. He is active in professional organizations like IEEE and serves on the Research Management Committee of Precarn Associates. Key Contributions Developed TINN for nearest-neighbor problem optimization Pioneered efficient collision detection methods for robotic motion planning Advanced 3D point cloud registration techniques using virtual interest points Co-created the CycleCrash dataset for bicycle collision analysis

Naser Ezzati-Jivanمشاهده پروفایل

دانشیار

• Software Engineering
• Performance Evaluation
• Software Tracing
• +۴ مورد دیگر

Naser Ezzati-Jivan is an Associate Professor in the Department of Computer Science at Brock University, Canada. He holds a PhD in Computer Engineering from the University of Montreal. His research focuses on software performance engineering, software tracing, distributed systems, and energy efficiency in IoT and cloud environments. He leads a research team exploring adaptive tracing techniques, malware detection, and anomaly classification in IoT networks. He has secured grants from NSERC, Mitacs, and FedDev for projects on software energy efficiency and smart IoT devices. Dr. Ezzati-Jivan teaches courses such as Software Performance Engineering (COSC 5P07) and Software Engineering 1/2 (COSC 4P01/4P02), emphasizing Agile methodologies, AI integration, and tool usage (Git, Azure DevOps). His research team includes members like Morteza Noferesti and Madeline Janecek, and he actively mentors graduate students and postdocs. His recent work includes publications on execution trace reconstruction using diffusion models, CNN-BiLSTM anomaly detection, and kernel-level performance analysis. He collaborates with institutions like the University of Montreal’s DORSAL lab and has presented at conferences like MIDDLEWARE and TRUSTCOM. His research bridges system-level tracing with software engineering practices to enhance observability and robustness in modern distributed systems.

Mikhail Belkinمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Feature Learning
• +۵ مورد دیگر

Mikhail Belkin is a Professor at the Halicioglu Data Science Institute and the Computer Science and Engineering Department at the University of California San Diego (UCSD), as well as an Amazon Scholar. His research focuses on foundational aspects of machine learning, particularly deep learning and large language models, emphasizing understanding phenomena like feature learning, over-parameterization, and generalization. He serves as editor-in-chief of the SIAM Journal on Mathematics of Data Science (SIMODS) and was elected an ACM Fellow in 2023. Research Interests: Generalization theory in deep learning Optimization dynamics and convergence Feature learning mechanisms in neural networks Kernel methods and their connections to neural networks Double descent phenomenon and model complexity Key Contributions: Belkin's work has advanced understanding of interpolation in over-parameterized models, revealing how zero-training-error models can generalize well despite classical statistical concerns. His analyses of optimization landscapes and loss surfaces have clarified why gradient-based methods succeed in deep learning. He introduced the Average Gradient Outer Product (AGOP) framework to explain feature learning in neural networks, bridging classical kernel methods and modern architectures. Publications: His recent work explores feature learning in neural networks, convolutional architectures, and the theoretical underpinnings of LLMs. Over 15 recent papers are listed, spanning topics like kernel regression, matrix completion, and the mathematical foundations of deep learning. Awards & Grants: ACM Fellow (2023) NSF/Simons Collaboration on Theoretical Foundations of Deep Learning PI in the TILOS AI Institute (NSF-funded) Advising & Teams: Belkin collaborates with interdisciplinary teams at UCSD and institutions like the Broad Institute’s Eric and Wendy Schmidt Center, focusing on AI’s societal implications and scientific applications. His work emphasizes bridging theory and practice, with algorithms like Recursive Feature Machines (RFM) and EigenPro showcasing scalable, interpretable machine learning solutions.

Yiqiang Zhaoمشاهده پروفایل

استاد

• Applied Probability
• Queueing Theory
• Stochastic Modelling
• +۴ مورد دیگر

Dr. Yiqiang Q. Zhao is a Professor and former Interim Director of the School of Mathematics and Statistics at Carleton University. He holds a Ph.D. from the University of Saskatchewan and has held academic positions at the University of Winnipeg and Carleton since 2000. His research focuses on applied probability, stochastic processes, and their applications in telecommunications, computer networks, finance, and health sciences. He has supervised over 150 graduate students and postdoctoral fellows. Education: B.Sc. from Nanjing University of Information Science and Technology (China), M.Sc. and Ph.D. from the University of Saskatchewan (Canada). Research interests include stochastic modeling, queueing theory, credit risk modeling, and derivative markets. He has authored over 130 publications and serves on editorial boards of journals like Stochastic Models and Queueing Systems . Awards: Recipient of the Carleton Faculty of Science Teaching Award (2002-2003), twice recognized as the most welcomed teacher by graduate students. Advising and Grants: Supervised 150+ students; grants from NSERC, Alcatel, Nortel, and MITACS. Current roles include Associate Dean (Graduate Affairs) in the Faculty of Science. Labs/Teams: Active in research teams focusing on queueing systems, stochastic networks, and mean-field modeling.

Cristina Ruiz Martínمشاهده پروفایل

استادیار

• Organizational Resilience
• Discrete-Event Modeling and Simulation
• Agent-Based Modeling
• +۳ مورد دیگر

Cristina Ruiz Martín is an Assistant Professor, Teaching Stream at Carleton University's Department of Systems and Computer Engineering (Faculty of Engineering and Design). She holds a Ph.D. from both Carleton University (Canada) and Universidad de Valladolid (Spain) through a cotutelle program. Her academic background includes an MSc in Industrial Engineering (2014) and a Project Management degree (2013) from Universidad de Valladolid. Her research focuses on Organizational Resilience , Discrete-Event Modeling and Simulation , Agent-Based Modeling , and Network Theory . Recent work explores resilience in firms, pandemic modeling, and simulation frameworks like DEVS and Cell-DEVS. She has taught courses in Strategic Management and Managerial Economics at Universidad de Valladolid, and currently contributes to engineering education at Carleton. Her publications emphasize applications of simulation techniques to real-world challenges, including pandemic spread analysis, emergency communication systems, and organizational dynamics. She actively develops collaborative modeling tools and integrates simulation into interdisciplinary studies. Key technical contributions include methodologies for combinatorial auctions, real-time DEVS kernels, and integrating BDI agents with simulation environments. Her work bridges theoretical modeling with practical applications in manufacturing, healthcare, and crisis management.

Marc Moreno Mazaمشاهده پروفایل

استاد

• Computer Algebra
• Polynomial System Solving
• Parallel Algorithms
• +۳ مورد دیگر

Marc Moreno Maza is a Professor in the Computer Science and Applied Mathematics Departments at the University of Western Ontario, and a Principal Scientist at the Ontario Research Centre for Computer Algebra (ORCCA). His research focuses on applying computer science to mathematics, particularly polynomial system solving and algorithm design. Key areas include parallel computing, high-performance algebraic algorithms, and GPU acceleration. He leads projects funded by NSERC and industry partnerships, such as 'Hardware Acceleration Technologies for Polynomial Systems' and collaborative work with IBM and CAS Research. His research spans four directions: theoretical foundations of polynomial equations, efficient algorithm development, software implementation (e.g., RegularChains library in Maple), and applications to real-world challenges. He has delivered over 100 talks worldwide on topics like cylindrical algebraic decomposition, GPU-based polynomial arithmetic, and parametric system solving. His work emphasizes optimizing algorithms for modern architectures and leveraging parallelism. Notable contributions include the BPAS and CUMODP libraries for polynomial arithmetic, and the RegularChains library for semi-algebraic set computations. He collaborates internationally, with grants supporting both theoretical and applied research. His team addresses challenges in computational algebra, from theoretical breakthroughs to practical software tools.

Paul Massetمشاهده پروفایل

استادیار

• Behavioral Neuroscience
• Systems Neuroscience
• Machine Learning
• +۳ مورد دیگر

Paul Masset is an Assistant Professor in the Department of Psychology at McGill University, focusing on the intersection of neuroscience, artificial intelligence, and experimental psychology. His research investigates how neuronal heterogeneity enables efficient distributed computations in brain circuits, particularly through dopamine-based reinforcement learning models validated via large-scale electrophysiological recordings in rodents. He leads an interdisciplinary team developing biologically plausible neural networks and interpretable deep learning tools for analyzing neural signals. Key research areas include systems neuroscience, machine learning applications in neuroscience, and computational models of neural circuits. His work bridges theoretical models with experimental validation, emphasizing translational insights between natural and artificial intelligence. Recent publications highlight advancements in multi-timescale reinforcement learning mechanisms, interpretable neural signal analysis, and natural gradient optimization in spiking networks. His lab's funding来源于 Canadian and Quebec governmental grants, alongside private foundations. Collaborations with institutions like Mila and Harvard extend his computational neuroscience expertise. Team projects include summer research initiatives supported by NSERC and McGill programs, fostering early-career involvement in neuroscience-AI integration. Lab activities emphasize open science practices and cross-disciplinary collaboration.