جست‌وجوی استاد و پژوهشگر

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

Tim J. Nyeمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Operations Research
• Process Modelling
• +۴ مورد دیگر

Tim J. Nye is an Associate Professor in the Department of Mechanical Engineering at McMaster University's Faculty of Engineering. He holds a Ph.D. in Mechanical Engineering (1997) from the University of Waterloo, following an M.Sc. (1989) at Ohio State and B.A.Sc. (1987) at Waterloo. His research focuses on applying operations research techniques to manufacturing systems, with specific expertise in optimization algorithms for sheet metal processes, hydroforming reliability, and adaptive control in forging. Education: Ph.D. Mechanical Engineering, University of Waterloo (1997) M.Sc. Mechanical Engineering, Ohio State (1989) B.A.Sc. Mechanical Engineering, University of Waterloo (1987) Research interests span multiple dimensions of advanced manufacturing: developing decision models for production investment, creating novel lot-sizing algorithms incorporating work-in-process costs, exact solutions for 2D nesting problems, and agent-based systems for reliability prediction using warranty data. His work bridges theoretical operations research with practical metal forming applications. Recent publications demonstrate consistent contributions to manufacturing optimization, with particular focus on stamping processes, sheet metal design, and hydroforming reliability. These align with McMaster's research clusters in Advanced Materials & Manufacturing and Infrastructure. Scientific awards include the 2002 CSME Best Student Paper competition win for machine vision research with S. Dworkin. He maintains active collaborations with industry partners, as evidenced by his research on industry-university R&D ventures. Current projects explore intelligent open die forging as a solid freeform fabrication method, demonstrating his commitment to both traditional manufacturing improvement and emerging rapid prototyping technologies.

• Machine Learning
• Representation Learning
• Geometric Deep Learning
• +۴ مورد دیگر

Siamak Ravanbakhsh is an Associate Professor at McGill University's School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with an emphasis on geometry, symmetry, and probabilistic inference. He has held academic positions at the University of British Columbia and was a postdoctoral fellow at Carnegie Mellon University. Education: B.Sc. in Computer Science, Sharif University of Technology M.Sc. and Ph.D. in Computer Science, University of Alberta (supervised by Russ Greiner) Postdoctoral Fellowship at Carnegie Mellon University (with Barnabás Póczos and Jeff Schneider) His research interests span geometric deep learning, equivariant networks, reinforcement learning, and AI for scientific applications. Notable contributions include work on symmetry-aware models, diffusion processes, and equivariant representation learning. Publications highlight advancements in causal abstraction, diffusion-based anomaly detection, and equivariant architectures for crystals and hierarchical structures. His work often bridges theory and application, emphasizing symmetry principles. Advising & Grants: Supervised over 20 graduate students and postdocs, including recent PhD graduates Daniel Levy and Mehran Shakerinava Active in mentoring M.Sc. and internship students He contributes to academic leadership roles at Mila and McGill, fostering interdisciplinary collaborations in AI research.

J.N.K. Raoمشاهده پروفایل

استاد پژوهشی

• Statistics
• Survey Sampling
• Statistical Inference
• +۳ مورد دیگر

J.N.K. Rao is a Distinguished Research Professor in the Department of Mathematics & Statistics at Carleton University. A leading expert in survey sampling and statistical inference, he has made groundbreaking contributions to small area estimation (SAE) and data integration methodologies. Research Focus: Specializes in survey methodology, poverty mapping, Bayesian inference, and empirical likelihood techniques. Honors: Gold Medal of the Statistical Society of Canada (1993), Fellow of the Royal Society of Canada (1991), Waksberg Award (2005), SAE Outstanding Achievement Medal (2017). His recent work explores model-based SAE, combining probability and non-probability samples, and improving inference validity through robust calibration. Awards highlight his decades-long impact on statistical theory and practice. Email: jrao@math.carleton.ca

Tzeng Yih Lamمشاهده پروفایل

استادیار

• Forest Measurements
• Forest Sampling
• Quantitative Silviculture
• +۳ مورد دیگر

Dr. Tzeng Yih Lam serves as an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia's Faculty of Forestry. His expertise lies in forest measurements, sampling methodologies, and quantitative silviculture, contributing to sustainable forest management practices through innovative biometric approaches. His educational background includes: PhD in Forest Science (2010) from Oregon State University MSc in Statistics (2009) from Oregon State University MSc in Tropical and International Forestry (2006) from Georg-August-Universität Göttingen BSc in Forestry (2001) from the University of New Brunswick Dr. Lam's research focuses on developing close-range photogrammetry tools for hard-to-measure tree attributes, designing cost-effective probability-based forest inventory systems using auxiliary information, and applying Bayesian filtering for forest growth projection. His work bridges field data collection with advanced statistical modeling to enhance timber production estimation and ecosystem service assessment. Analysis of his 15 most recent publications reveals dominant themes in innovative sampling design (particularly Critical Height Sampling and cluster/nested plots), integration of machine learning with rapid assessment techniques, and sophisticated height-diameter modeling across diverse species. His work consistently emphasizes spatial distribution mapping, LiDAR integration, and error reduction in field measurements. As an active contributor to the UBC Forest Measurements and Biometrics research group, Dr. Lam develops teaching materials for courses including Advanced Regression Analysis and Forest Measurements, advancing methodological training in forest biometrics through R workshops and practical field applications.

Mingbin Fengمشاهده پروفایل

دانشیار

• Monte Carlo simulation
• Nested simulation
• Derivative pricing
• +۷ مورد دیگر

Mingbin (Ben) Feng is an Associate Professor of Actuarial Science at the University of Waterloo's Department of Statistics and Actuarial Science. His research focuses on Monte Carlo simulation design, nested simulation, and applications in risk management, financial engineering, and stochastic optimization. He holds an ASA designation from the Society of Actuaries and a Certified Analytics Professional (CAP) credential. Education: Ph.D. in Industrial Engineering and Management Sciences (Northwestern University, 2016), M.Math in Actuarial Science (University of Waterloo, 2011), and B.Math with Distinction (University of Waterloo, 2010). His academic journey includes internships at AXIS Capital Holdings and The Hong Kong University of Science and Technology. Research Interests: Machine learning, green simulation, derivative pricing, systemic risk, and simulation analytics. Awards: Royal E. Cabell Fellowship, Hickman Scholarship, and Arthur P. Hurter Award. Teaching: Courses include Corporate Finance, Financial Mathematics, and Portfolio Optimization. He advises a diverse group of graduate and undergraduate students, with notable contributions to developing the vamc R package for variable annuity modeling. Key Research Contributions: Innovations in nested simulation efficiency, green simulation methodologies, and applications of machine learning in actuarial science.

Apurva Narayanمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

• Physical Oceanography
• Atmospheric Science
• Coastal Dynamics
• +۳ مورد دیگر

Prof. Jinyu Sheng is a Professor in the Department of Oceanography at Dalhousie University, affiliated with the Faculty of Science. His primary research focuses on physical oceanography and atmospheric science, with expertise in numerical modeling of coastal and shelf sea dynamics, climate change impacts, and biogeochemical processes. Education: BEng, East China Technical University MSc, Memorial University PhD, Memorial University Research Interests: Coastal and shelf sea circulation Extreme weather-ocean interactions Climate change projections for marine systems Numerical modeling of coupled physical-biogeochemical processes Tidal and storm surge dynamics Key Contributions: Developed advanced coupled circulation-ice-biogeochemistry models for the Northwest Atlantic Explored impacts of tropical cyclones on air-sea CO2 exchanges Studied tidal effects on dissolved oxygen variability Investigated storm-induced circulation changes in estuaries Awards & Recognition: 2011 Second Award, Ministry of Water Resources (China) 2000 Canada Foundation for Innovation Researcher 2013 HRM & Nova Scotia Provincial Volunteer Awards Grants & Collaborations: NSERC-Industry Fellowships CFI infrastructure grants International collaborations on ocean modeling Labs & Teams: Leading Dalhousie's coastal circulation modeling group Participates in Northwest Atlantic modeling initiatives

Victoria Savaleiمشاهده پروفایل

استاد

• Quantitative Methods
• Structural Equation Modeling
• Missing Data Analysis
• +۲ مورد دیگر

Dr. Victoria Savalei is a Professor and Area Coordinator in the Department of Psychology at the University of British Columbia's Faculty of Arts. Her research focuses on quantitative psychology, particularly structural equation modeling (SEM), which integrates regression, path analysis, and factor analysis to test complex psychological theories. She develops methods for analyzing imperfect data including non-normally distributed data, missing values, categorical responses, and small samples. Her work critically evaluates SEM practices and addresses statistical challenges related to psychology's replicability crisis. Dr. Savalei holds a PhD from UCLA (2007). Her current work advances the two-stage (TS) method for handling incomplete data. Research interests include improving fit indices in SEM with categorical data, computational approaches for standard errors with incomplete data, and robust statistical methods for small samples.

Eirikur Palssonمشاهده پروفایل

استاد مدعو

• Mathematical Biology
• Cell Signaling
• Ecological Systems
• +۳ مورد دیگر

Eirikur Palsson is an Adjunct Professor in Mathematical Biology at Simon Fraser University (SFU), associated with the Faculty of Science. He holds a PhD from Princeton University. His research focuses on mathematical modeling of cellular systems and ecological interactions, particularly studying cell signaling, multicellular dynamics, and nutrient competition in marine environments. He is affiliated with the Department of Biological Sciences and has cross-disciplinary ties with the Department of Physics. Education: PhD in Biological Sciences, Princeton University Research Interests: Palsson develops computational models to explore cell movement mechanisms in systems like Dictyostelium discoideum and Zebrafish, emphasizing cell adhesion and chemotaxis. His work on marine ecosystems investigates nutrient stoichiometry and Redfield ratio dynamics. Recent projects include modeling tumor-macrophage interactions and reservoir impacts on avian populations. Publications Trends: His research spans over three decades, combining theoretical biology with experimental validation. Recent work integrates computational models to address ecological and biomedical challenges, such as microplastic pollution effects and cancer cell invasion mechanisms. Awards and Grants: No specific awards listed, though his extensive publication record indicates sustained research excellence. Current projects likely involve SFU's research facilities. Labs and Collaborations: Laboratory locations: Office B8267 and Lab B9215/17B. Collaborations span mathematical biology, physics, and environmental science departments.

• Actuarial Science
• Quantitative Finance
• Risk Management
• +۵ مورد دیگر

Ben Feng is an Associate Professor and Director of the Master of Actuarial Science Program at the University of Waterloo. He specializes in actuarial science, quantitative finance, and risk management, with a focus on machine learning applications in financial modeling and anomaly detection. His research bridges theoretical advancements in statistical methods with practical solutions for pension planning, climate-smart portfolios, and systemic risk analysis. Key research interests include time series analysis, nested simulation techniques, and the integration of graph theory into financial systems. Recent work emphasizes anomaly detection in dynamic environments and climate-aware investment strategies. His articles span domains like multivariate time series, contrastive learning, and green simulation optimization. Ben has contributed to systemic risk modeling and efficient simulation algorithms, with publications in high-impact venues. His work often addresses computational challenges in finance and actuarial science through interdisciplinary approaches.

• Econometrics
• Financial Econometrics
• Asset Pricing
• +۳ مورد دیگر

Florian Richard is an Assistant Professor at the Department of Finance, Insurance and Real Estate at Université Laval. His research focuses on econometrics, financial econometrics, and asset pricing. He has presented work at major conferences including the International Conference on Computational and Financial Econometrics and the Société canadienne de science économique. His email is florian.richard@fsa.ulaval.ca. Research interests emphasize methodological advancements in econometric testing and model validation, particularly in multivariate systems and asset pricing frameworks. Key contributions include simulation-based approaches for non-nested model comparisons and confidence set constructions in high-dimensional settings. Recent presentations include conferences in 2023–2025, highlighting contributions to empirical finance and computational econometrics. No scientific awards are listed, but his active conference participation reflects ongoing scholarly engagement.

• Computational Astrophysics
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Joshua S. Speagle is an Assistant Professor at the University of Toronto , specializing in computational astrophysics, Bayesian statistics, and machine learning applications to astronomical problems. His work bridges theoretical modeling with practical software development, notably as the creator of the dynesty package for dynamic nested sampling in Bayesian inference. Research Interests : Computational astrophysics and statistical methods Bayesian inference and dynamic nested sampling Stellar evolution and galaxy formation Machine learning for astronomical data analysis Recent Publications (2025–2024) focus on generative models for inverse problems, galaxy formation at high redshift, stellar oscillation analysis, and simulation-based inference techniques. His software tools like dynesty and brutus are widely used in the astronomical community for Bayesian posterior and evidence estimation. Contact: j.speagle@utoronto.ca | Personal Website | ORCID: 0000-0003-2573-9832

Ricardo Mantillaمشاهده پروفایل

دانشیار

• Hydrology
• Flood Forecasting
• River Networks
• +۵ مورد دیگر

Ricardo Mantilla serves as an Associate Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. He joined the faculty in July 2021 after previously working as an Assistant Professor at the University of Iowa in the Department of Civil and Environmental Engineering. His academic journey includes significant contributions to hydrological research and flood forecasting systems development. Ph.D. in Civil Engineering (Hydrology), University of Colorado at Boulder (2007) M.Sc. in Civil Engineering (Water Resources), Universidad Nacional de Colombia (2003) B.Sc. in Civil Engineering, Universidad Nacional de Colombia (2000) Mantilla's research focuses on Surface Hydrology, particularly the role of self-similarity in river network topology and hydraulic geometric variables in shaping flood characteristics. He is recognized for developing the Hillslope-Link Model (HLM) and contributing to the Iowa Flood Information System (IFIS). His work bridges theoretical hydrology with practical flood forecasting applications, addressing how climatic and anthropogenic changes affect flood and drought regimes globally. Mantilla's publications demonstrate a consistent trajectory from theoretical foundations to practical implementations, with recent work emphasizing physics-based modeling approaches over purely statistical methods for flood prediction under changing climate conditions. Mantilla actively seeks graduate students for his research group, emphasizing the need for strong communication skills, passion for water resources engineering, problem-solving abilities, and comfort working in diverse environments. His research has led to the development of operational flood forecasting systems that utilize High-Performance Computing resources to provide public flood information through web platforms.