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Alan RitterView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu

• Graph Algorithms
• Optimization
• High-Dimensional Geometry
• +3 more

Yang P. Liu is an Assistant Professor at Carnegie Mellon University 's Department of Computer Science . He received his PhD from Stanford University under the supervision of Aaron Sidford and previously studied at MIT . Fields of Interest : Graph Algorithms, Optimization, High-Dimensional Geometry, Additive Combinatorics, Theoretical Computer Science. His research focuses on algorithmic design and analysis for graph problems, optimization, and combinatorics, with applications in machine learning and complexity theory. Recent work includes advancements in parallel repetition games , combinatorial lines , and dynamic graph algorithms . In 2024, his research spanned FOCS , STOC , and RANDOM conferences, addressing problems in k-CSPs , min-cost flow , and hypergraph sparsification . Earlier contributions (2023) included deterministic flow algorithms and spectral hypergraph techniques. Scientific Awards : NDSEG Fellowship (2018-2021), Google PhD Fellowship (2022-2023), FOCS Best Paper (2022), STOC Best Student Paper (2022), FOCS Best Student Paper (2021). He teaches CS 15-759 , a graduate course on convex optimization theory and applications, covering gradient descent, interior point methods, and algorithmic sparsification techniques.

Sheila McIlraithView profile

Professor

• Artificial Intelligence
• AI Safety
• AI Alignment
• +9 more

Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.

• Operations Management
• Supply Chain Management
• Sharing Economy
• +6 more

Ming Hu is a Professor and University of Toronto Distinguished Professor of Business Operations and Analytics at Rotman School of Management, University of Toronto. He serves as Area Coordinator for the Operations Management & Statistics Area and holds editorial leadership roles including Editor-in-Chief of Naval Research Logistics and Associate Editor for multiple top journals. MS in Applied Mathematics, Brown University (2003) PhD in Operations Research, Columbia University (2009) His research focuses on sharing economy , social operations , and platform economics , examining how operational decisions can maximize societal benefit. Key areas include crowdfunding , two-sided markets , crowdsourcing , and group buying , with applications to DEI , sustainability , and AI-empowered operations . Recent work analyzes spatial operations in delivery systems, algorithmic fairness , and climate change adaptation in agricultural supply chains. His publications span top journals like Management Science and Operations Research , covering topics from blockchain traceability to quantum-inspired optimization . Scientific recognitions include: Wickham Skinner Early-Career Research Award (2016) Best Operations Management Paper in Management Science (2017) 2018 Poets & Quants Best 40 Under 40 MBA Professors As an Amazon Scholar (2022–) and Chair of Chain Analytics Institute (2023–), he bridges academic research with industry applications in AI-driven logistics and sustainable operations.

Alexandre Bouchard-CôtéView profile

Professor

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +4 more

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

Khuzaima DaudjeeView profile

Professor

• Distributed Systems
• Database Management
• Cloud Computing
• +3 more

Khuzaima Daudjee is a Professor and David R. Cheriton Faculty Fellow in the Cheriton School of Computer Science at the University of Waterloo. His research focuses on systems-oriented problems at the intersection of systems and data management, particularly building large-scale systems, storage infrastructure in the cloud, and modern hardware applications. He leads projects in distributed database systems, elastic scaling, and resource optimization. His recent work includes Caerus (geo-replicated transactions), Tiresias (predictive storage), and MorphoSys (automatic physical design metamorphosis). Daudjee has chaired major conferences including ICDE 2026 and serves on editorial boards for VLDB, SIGMOD, and IEEE TKDE journals. His awards include ACM Distinguished Scientist and multiple best paper awards. Educational initiatives include developing distributed systems teaching materials and supervising graduate students across database and distributed systems domains. His industry collaborations involve cloud infrastructure optimization and scalable data processing frameworks.

Murat ErdogduView profile

Assistant Professor

• Machine Learning
• High-dimensional Statistics
• Optimization
• +3 more

Murat Erdogdu is an Assistant Professor at the University of Toronto, jointly appointed in the Department of Computer Science and Department of Statistical Sciences . He is also a faculty member at the Vector Institute and holds the CIFAR Chair in Artificial Intelligence . PhD in Statistics, Stanford University (advised by Mohsen Bayati and Andrea Montanari) MSc in Computer Science, Stanford University BSc in Electrical Engineering and Mathematics, Bogazici University His research focuses on machine learning theory , high-dimensional statistics , and optimization . He has contributed to understanding gradient-based algorithms, sampling methods, and feature learning in structured data. Recent publications address problems in: Heavy-tailed sampling Mean-field Langevin dynamics Robust feature learning Minimax regression Stochastic optimization under infinite noise Scientific Awards: CIFAR Chair in Artificial Intelligence ICLR 2023 Spotlight NeurIPS 2019 & 2021 Spotlights He teaches graduate courses like STA 414/2104 (Statistical Methods for Machine Learning II) and STA 4273 (Modern Learning Theory) , emphasizing probabilistic modeling and theoretical analysis.

• Inverse Problems
• Partial Differential Equations
• Mathematical Physics
• +3 more

Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.

Peter SelingerView profile

Professor

• Quantum Computing
• Category Theory
• Mathematical Logic
• +4 more

Peter Selinger is a Professor in the Department of Mathematics and Statistics at Dalhousie University , with a cross-appointment in Computer Science. He specializes in mathematical methods in computer science, particularly quantum computing and combinatorial game theory . His work on quantum programming languages like Quipper and foundational research in category theory has garnered international recognition. Education : Ph.D. in Mathematics (University of Pennsylvania, 1997), undergraduate studies in Mathematics (Technische Universität Darmstadt). Research Interests span quantum computing, category theory, and combinatorial game theory. He has pioneered formalisms for quantum programming languages, developed categorical models for quantum mechanics, and analyzed game-theoretic structures in games like Hex. His recent work includes linear dependent type theory , quantum circuit synthesis , and combinatorial game classification . Publications demonstrate expertise in quantum programming languages, categorical semantics, and game theory. Key trends include Hamiltonian simulation , Clifford+T circuits , and monotone game realization . Scientific Honors include the Killam Professorship (2017–2022), Faculty of Science Award for Excellence in Teaching (2023), and fellowships from the Alfred P. Sloan Foundation and German National Scholarship Foundation . Students he has supervised include PhD graduates Xiaoning Bian , Francisco Rios , and Neil J. Ross , along with MSc students like Fahimeh Bayeh and Seth Greylyn . He has advised 16 postdoctoral researchers.

Steven RogakView profile

Professor

• Aerosol Science
• Nanoparticle Transport
• Combustion Processes
• +5 more

Steven Rogak is a Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science. He holds a P.Eng. license and degrees including a B.A.Sc. in Mechanical Engineering from UBC, and M.Sc. and Ph.D. from Caltech. P.Eng., University of British Columbia B.A.Sc., University of British Columbia M.Sc., Ph.D., California Institute of Technology His research focuses on aerosol science, particularly solid nanoparticles from combustion processes, their climate and health impacts, and mitigation strategies. Key areas include: Soot morphology and transport properties Engine emission reduction via fuel injectors Indoor air filtration systems Membrane-based energy exchangers Atmospheric particulate analysis The 15 most recent articles span experimental and theoretical studies on soot characterization, membrane technologies, and aerosol dynamics, with applications in climate modeling, healthcare ventilation, and sustainable materials. Collaborations include Westport Innovations and interdisciplinary teams. Rogak leads the Aerosol Laboratory at UBC, where he applies fluid mechanics and heat transfer fundamentals to address environmental and health challenges. He emphasizes experimental rigor and welcomes graduate students with expertise in these areas.

• Numerical analysis
• Partial differential equations
• Level set methods
• +7 more

Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University, specializing in applied mathematics, numerical analysis, and computational methods. His research focuses on numerical methods for partial differential equations, fluid mechanics, interface problems, and computer graphics. He holds a PhD from UCSB (2004) and has held academic positions at MIT and McGill since 2005. Currently, he serves on committees such as the Steering Committee of the Institut des Sciences Mathematiques and the CRM Applied Mathematics Lab. His educational background includes a PhD under Professors Xu-Dong Liu and Sanjoy Banerjee. Key research areas include level set methods, fluid-structure interaction, and invariant numerical methods. Notable works include the Correction Function Method for interface problems and the Characteristic Mapping Method for advection problems. Nave’s publications span topics like Poisson equations with discontinuous coefficients, fluid dynamics simulations, and high-order numerical schemes. He has advised numerous graduate and undergraduate students, contributing to their research in applied mathematics and computational science. His work bridges theoretical rigor and practical applications in engineering and physics. He teaches advanced courses such as Numerical Analysis I/II and Computational Methods in Applied Mathematics. His research group collaborates on projects involving fluid dynamics, elasticity, and geometric algorithms, with a focus on developing robust numerical tools for complex systems.

David LandriaultView profile

Professor

• Actuarial Science
• Quantitative Risk Management
• Applied Probability
• +4 more

David Landriault is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada, and a Canada Research Chair in Risk Theory. His research focuses on Actuarial Science, Quantitative Risk Management, Applied Probability, and Stochastic Processes, particularly in ruin theory, drawdown analysis, and stochastic control for insurance and finance applications. Education: PhD in Mathematics (2005), MSc in Mathematics (2003), BSc in Actuarial Science (2002) from Laval University. Affiliations: University of Waterloo (postdoctoral fellowship, 2006); Canada Research Chair in Risk Theory. Research Interests Risk and Ruin Theory Stochastic Control in Insurance and Finance Drawdown and Occupation Time Analysis Regime-Switching Models Reinsurance Design and Optimization Time-Dependent Risk Models Scientific Awards Fellow of the Canadian Institute of Actuaries (F.C.I.A.), 2009 Fellow of the Society of Actuaries (F.S.A.), 2006

• High-speed data converters
• Wireless transceivers
• Digital phase-locked loops
• +7 more

Shiyu Su is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on high-speed data converters, wireless transceivers, digital phase-locked loops (PLL), and AI-assisted analog/mixed-signal design automation. He holds a Ph.D. from the University of Southern California (2019) and teaches courses such as ECE 340 (Electronic Circuits 2) and ECE 432 (Radio Frequency Integrated Devices and Circuits). Education: B.S. from Beijing University of Post and Telecommunication (China) and Queen Mary, University of London (UK), 2011; M.S. and Ph.D. from USC, 2013 and 2019, all in electrical engineering. Research Interests: High-speed ADCs/DACs RF/mm-wave transceivers Time-approximation filters (TAF) Analog/mixed-signal design automation Memristor-based computing Biomedical interfaces Key Awards: IEEE SSCS Predoctoral Achievement Award (2017–2018) Best Student Paper Award at IEEE RFIC (2022) Ming Hsieh Institute Scholar (2019–2020) Lab Focus: The Shiyu Su Lab develops integrated circuits for communications, sensing, and computing, with a focus on AI-driven methodologies and digital-analog co-design. Collaborations include work with Prof. Wei Wu (USC) on memristor-based systems.

James BremerView profile

Professor

• Numerical Analysis
• Acoustic and Electromagnetic Scattering Problems
• Integral Equation Methods
• +5 more

James Bremer is a Professor in the Department of Mathematics and holds a cross-appointment in the Department of Computer and Mathematical Sciences at the University of Toronto's Scarborough campus. His research focuses on developing efficient numerical algorithms for solving elliptic boundary value problems, integral equations, and special function transforms.

Chaitanya SwamyView profile

Professor

• Combinatorial Optimization
• Approximation Algorithms
• Algorithmic Game Theory
• +4 more

Chaitanya Swamy is a Professor and University Research Chair in the Department of Combinatorics & Optimization at the University of Waterloo, Canada. His primary affiliation is within the Faculty of Mathematics, and he holds positions in both the Department of Combinatorics & Optimization and the School of Computer Science. He obtained his Ph.D. in Computer Science from Cornell University under the supervision of David Shmoys, followed by postdoctoral research at Caltech's Center for the Mathematics of Information. Swamy’s research focuses on algorithms, particularly in combinatorial optimization, approximation algorithms, algorithmic game theory, stochastic optimization, network design, scheduling, and online algorithms. His work spans theoretical contributions and practical applications, including algorithm design for facility location, network routing, and mechanism design. He has contributed to foundational results in approximation algorithms, such as the development of primal-dual methods and LP-rounding techniques. Swamy has held significant editorial roles, including as an associate editor for Discrete Optimization and SIAM Journal on Computing . He has organized major conferences like CanaDAM 2021 and sessions at ISMP 2018. His teaching record includes courses on combinatorial optimization, scheduling, and algorithmic game theory. He has advised numerous Ph.D. and Master’s students, many of whom have gone on to prestigious academic and industry positions. Swamy’s research has been recognized through awards for his students, including the University of Waterloo Alumni Gold Medal. He actively contributes to the academic community through committee work for conferences like STOC, APPROX, and SODA, and his publications reflect a deep engagement with both theoretical and applied aspects of algorithms and optimization.