جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +۱۱ مورد دیگر

Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

Gautam Kamathمشاهده پروفایل

استادیار

• Differential Privacy
• Machine Learning
• Statistics
• +۴ مورد دیگر

Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.

WonSook Leeمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.

• Trustworthy Artificial Intelligence
• Privacy-Preserving Systems
• Adversarial Robustness
• +۴ مورد دیگر

Mathias Lécuyer is an Assistant Professor in the Computer Science Department at the University of British Columbia (UBC) , part of the Faculty of Science . His research focuses on trustworthy AI systems, with emphasis on privacy (differential privacy), adversarial robustness, and causal machine learning. He leads the Systopia Lab and collaborates with groups such as UBC S&P, TrustML, and CAIDA. Education: PhD in Computer Science from Columbia University (2019), MSc from Columbia University (2013) and École Polytechnique (2011). Research Interests: Ensuring rigorous guarantees for AI systems via differential privacy, robustness against adversarial attacks, and causal inference. His work spans theoretical foundations and practical implementations in areas like privacy-preserving systems, federated learning, and transparent machine learning. Awards: SOSP Distinguished Artifact Honorable Mention (2024), Google Research Award (2022), Bourse Carnot Fellowship (2011), and UBC’s top teaching evaluations (2021). Advising & Service: Supervised over 20 students (PhD, MSc, undergrad) across privacy and machine learning topics. Serves on PCs for top conferences (OSDI, S&P, NeurIPS) and organizes workshops like TrustML @ UBC. Actively mentors high school students in ML research through outreach programs. Labs/Teams: Leads the Systopia Lab at UBC, focusing on AI safety and privacy. Collaborates with MSR, Google, and industry partners on practical system implementations.

Hassan Z. Ashtianiمشاهده پروفایل

دانشیار

• Machine Learning Theory
• Privacy-Preserving Algorithms
• Gaussian Mixture Models
• +۵ مورد دیگر

Hassan Z. Ashtiani is an Associate Professor in the Department of Computing and Software within the Faculty of Engineering at McMaster University. His academic profile shows consistent engagement in both teaching and research activities, with evidence of active participation in major machine learning conferences and journals through 2025. Dr. Ashtiani's research focuses on the theoretical foundations of machine learning, with particular expertise in privacy-preserving algorithms, Gaussian mixture models, and adversarial robustness. His work bridges statistical learning theory with practical algorithm design, often addressing fundamental questions about sample complexity and computational efficiency in learning systems. A significant portion of his recent work explores the intersection of differential privacy with statistical learning, developing methods for private density estimation and distribution learning. Analysis of his publication record reveals a strong trend toward increasingly sophisticated theoretical frameworks for private and robust learning. His work consistently appears in top-tier venues including NeurIPS, ICML, COLT, and ALT, with recent contributions extending into agnostic private density estimation and robust learning with tolerance. The research demonstrates progression from foundational work on nearest neighbor search and clustering algorithms toward more complex problems in private learning of high-dimensional distributions. Dr. Ashtiani teaches across multiple levels of computer science education, including undergraduate courses in Automata and Computability (COMPSCI 2AC3) and Principles of Programming (COMPSCI 2S03), as well as graduate-level courses such as Fundamentals of Machine Learning (COMPSCI 4ML3) and Theoretical Foundations of Unsupervised Learning (CAS 775). His teaching portfolio shows consistent involvement in machine learning education since at least 2019, with evidence of teaching multiple sections each academic year. His scholarly impact is reflected in mentions across 3 news outlets, reference in 1 policy source, engagement from 7 X users, and 90 readers on Mendeley, suggesting growing recognition of his contributions to theoretical machine learning.

Gauthier Gidelمشاهده پروفایل

دانشیار

• Deep Learning
• Adversarial Machine Learning
• Generative Modeling
• +۵ مورد دیگر

Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.

Ruth Urnerمشاهده پروفایل

دانشیار

• Machine Learning Theory
• Adversarial Robustness
• Algorithmic Fairness
• +۴ مورد دیگر

Ruth Urner is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. She holds a PhD in Computer Science from the University of Waterloo (2013) and completed postdoctoral research at Max Planck Institute for Intelligent Systems (Germany), Carnegie Mellon University, and Georgia Tech. She was a Simons-Berkeley Fellow at the Simons Institute in 2017. Research Focus: Dr. Urner develops mathematical foundations for machine learning paradigms including semi-supervised/active learning, transfer learning, and adversarial robustness. Her current work addresses societal impacts of ML through interpretability and fairness frameworks. She leads projects on strategic classification, robust PAC learning, and calibrated model evaluation. Awards & Leadership: Simons-Berkeley Fellowship (2017) Best Paper Award at NIPS 2015 Workshop on Transfer Learning Organizer: Women in Machine Learning Theory workshops (COLT/ALT) Program Committee: NeurIPS, ICML, COLT, ICLR, AISTATS Teaching & Advising: She teaches Machine Learning Theory, Computational Logic, and Introduction to ML at York University. Current student advisees include Master's candidate Alireza Torabian. She has lectured at international summer schools including Hausdorff School on Algorithmic Data Analysis (Germany) and SMILES Summer School (Russia). Affiliations: Faculty affiliate at Vector Institute (Toronto) and collaborator with Max Planck Institute systems. Her lab investigates theoretical guarantees for learning algorithms under distribution shifts and adversarial conditions.

David Mouldمشاهده پروفایل

استاد

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +۴ مورد دیگر

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

Zhijing Jinمشاهده پروفایل

استادیار

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Causal Inference
• +۷ مورد دیگر

Zhijing Jin is an Assistant Professor at the University of Toronto and a postdoc at the Max Planck Institute for Intelligent Systems, working with Bernhard Schölkopf. She is also a faculty member at the Vector Institute and an ELLIS advisor. Her research focuses on Causal Reasoning with LLMs , Moral Reasoning in LLMs , and AI Safety , with contributions to AI for Science and NLP for Social Good. She leads the Jinesis AI Lab , which explores causal LLMs, multi-agent systems, and ethical AI applications. Education: PhD in Computer Science from Max Planck Institute (Germany) and ETH Zurich (Switzerland) Bachelor’s degree from University of Hong Kong, with visiting semesters at MIT and National Taiwan University Research Interests: Her work bridges causal inference and NLP, addressing robustness, interpretability, and ethical alignment of LLMs. Key projects include Corr2Cause (causal reasoning), GovSim / MoralSim (multi-agent LLMs), and frameworks like NLP4SG for social impact. She advocates for causal mechanisms to tackle AI safety and societal challenges. Recent Articles: Recent work explores political bias in LLMs, ethical dilemmas in multi-agent systems, and causal foundations for trustworthy AI. These studies emphasize real-world applications, such as healthcare NLP and policy analysis. Awards & Recognition: 3 Rising Star Awards 2 Best Paper Awards at NeurIPS 2024 Workshops Fellowships from Open Philanthropy and Future of Life Institute Grants & Mentorship: Funded by NSERC, Schmidt Sciences, and the Cooperative AI Foundation. She mentors ~20 students globally, including PhD candidates in multi-agent LLMs, causal LLMs, and AI safety. The Jinesis Lab offers remote mentorship across institutions like UofT, ETH Zurich, and University of Michigan. Labs & Collaborations: Active collaborations include MPI-IS, Vector Institute, and ETH Zurich’s AI Center. Her lab emphasizes open science, with tools like MoralLens and RouterAttack released publicly.

Mahmoud Alfadelمشاهده پروفایل

استادیار

• Software Ecosystems
• Software Releases
• Build Systems
• +۵ مورد دیگر

Mahmoud Alfadel is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His research focuses on software ecosystems, build systems, security vulnerabilities, and mining software repositories. He holds a PhD in Software Engineering from Concordia University (2021), an M.S. from KFUPM (2017), and a B.S. from Damascus University (2013). Education: Bachelor of Science in Information Technology, Damascus University (2013) Master of Science in Software Engineering, King Fahd University of Petroleum and Minerals (KFUPM, 2017) Doctor of Philosophy (Ph.D.) in Software Engineering, Concordia University (2021) His research emphasizes empirical studies in DevOps practices, continuous integration, and software security. Recent work explores vulnerability lifecycle analysis in Golang, fuzz testing adoption in open-source projects, and dependency-induced waste in CI pipelines. His publications reflect a focus on improving software quality and security through automated tools and empirical analysis. Awards and Grants: No specific awards or grants are listed in the profile. His work is supported through empirical studies and academic collaborations. Advising and Labs: No student advisees or specific lab affiliations are noted in the provided materials. His research is likely conducted through collaborative projects with students and industry partners.

Arpan Gujaratiمشاهده پروفایل

استادیار

• Real-Time Systems
• Distributed Systems
• Fault Tolerance
• +۵ مورد دیگر

Arpan Gujarati is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. His research focuses on real-time systems, distributed systems, fault tolerance, and reliability analysis in cloud and cyber-physical systems domains. He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and has postdoctoral and research experience at MPI-SWS and UBC. Education: PhD in Real-Time Systems (MPI-SWS/TU Kaiserslautern, 2020), Postdoctoral Researcher at MPI-SWS (2020), Research Associate at UBC (2022–2023), B.Sc. from Birla Institute of Technology and Science (BITS Pilani, India). Research interests include distributed real-time systems, reliability analysis of cloud applications, fault-tolerant machine learning, and scheduling algorithms. His work bridges theory and practice, addressing challenges in ultra-reliable CPS and resilient distributed systems. Awards: Best Dissertation Award (SIGBED, 2020), Best Paper Awards (RTSS 2022, ECRTS 2018), Distinguished Artifact Award (OSDI 2020). Advising: Supervises PhD and undergraduate students in distributed systems and resilience engineering. Active in UBC’s Computer Science Graduate Program, teaching courses like CPSC 416 (Distributed Systems) and CPSC 538G (Topics in Computer Systems). Labs/Teams: Leads the Systopia Lab, collaborating with industry partners on projects like robotic arm datasets and self-driving lab tools (RABIT). Involved in research groups focused on machine learning resilience and real-time systems.

Jason Jaskolkaمشاهده پروفایل

دانشیار

• Cyber Security Evaluation
• Security Assurance
• Formal Methods
• +۴ مورد دیگر

Jason Jaskolka is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from McMaster University and is a licensed Professional Engineer in Ontario. His research focuses on cyber security evaluation and assurance, formal methods, and secure software engineering. He leads the Cyber Security Evaluation and Assurance (CyberSEA) Lab, emphasizing security-by-design principles for complex systems like industrial control systems and IoT-enabled healthcare. Education: Ph.D. (Software Engineering, McMaster University, 2015), M.A.Sc. (Software Engineering, McMaster University, 2010), B.Eng. (Software Engineering and Game Design, McMaster University, 2009). Research interests include: Cyber Security Evaluation & Assurance, Data-Driven Security Metrics, Formal Verification of Security Properties, Secure Software Architecture Design, and Industrial Cyber-Physical Systems Security. His work addresses challenges in threat modeling, compliance with security standards, and mitigating implicit system vulnerabilities. Recognition includes the 2021 New Faculty Excellence in Teaching Award for innovative pedagogy. He actively supervises graduate students in funded research positions. Key collaborations include Health Canada's Scientific Advisory Committee on Digital Health Technologies and the U.S. Department of Homeland Security’s Cybersecurity Postdoctoral Fellowship at Stanford University. Labs/Teams: CyberSEA Lab, focusing on developing rigorous security evaluation frameworks and tools for software-dependent systems.

Jinqiu Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Automated Program Repair
• Software Testing
• +۴ مورد دیگر

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on software reliability, automated program repair, software testing, and quality assurance of machine learning systems, particularly in autonomous vehicles. He holds a PhD and MASc from the University of Waterloo and a B.Eng. from Nanjing University. Yang has been a tenure-track faculty member at Concordia since 2018, following research roles at IBM Watson and IBM CAS. Research Interests: Automated Program Repair Software Testing Machine Learning Systems Text Analytics of Software Artifacts Mining Software Repositories Autonomous Systems Quality Assurance Key Contributions: His recent work includes detecting concept drifts in ML systems (ICSE-25) and investigating social bias in LLM-generated code (AAAI-25). He leads the O-RISA Lab and has authored over 50 peer-reviewed publications, including distinguished papers at MSR-2018. Yang currently holds grants such as the NOVA FRQNT-NSERC Program (2024-2027) and the NSERC Discovery Grant (2019-2025). Service & Awards: Editorial Board Member of the Empirical Software Engineering Journal (EMSE) and active PC member in top venues like ICSE and FSE. Recipient of the IBM CAS Fellowship and ACM SIGSOFT Distinguished Paper Award. Teaching & Students: He mentors students in Master's and PhD programs, with funding available. His lab focuses on cutting-edge topics like secure code generation and autonomous vehicle reliability.

• Machine Learning
• AI Safety and Robustness
• Deep Learning
• +۴ مورد دیگر

Zico Kolter is a Professor and Director of the Machine Learning Department at Carnegie Mellon University . He also serves on the OpenAI Board of Directors as chair of the safety and security committee, co-founded Gray Swan AI (an AI security company), and acts as a Chief Expert at Robert Bosch, LLC . Research Focus: AI safety and robustness, LLM security, data impact on models, implicit models, and adversarial defense. Teaching: Offers graduate courses in artificial intelligence and deep learning systems. His work investigates robustness in deep learning, constraints in optimization, and security in foundation models. Recent publications address adversarial compression, diffusion models, and prompt engineering. Notable Awards: DARPA Young Faculty Award Sloan Fellowship Best Paper at NeurIPS, ICML (honorable mention), AISTATS (test of time), IJCAI, KDD, and PESGM.

Adam Brownمشاهده پروفایل

استادیار

• Science Education
• Science Communication
• Ecology
• +۳ مورد دیگر

Adam Brown is an Assistant Professor in the Department of Biology at the University of Ottawa, affiliated with both the Faculty of Science and Faculty of Education. He holds a BSc and HBSc from the University of Western Ontario (UWO) and a PhD from Laval University. His research focuses on Science Education and Science Communication, particularly in improving pedagogical methods for undergraduate Biology students and fostering public engagement with scientific topics through media production. He also conducts ecological studies on pollinator dynamics in plants like cloudberry and cranberry. His scientific awards include a National Finalist for the Nature Inspiration Award and recognition of his 2016 research as one of the USA National Science Teachers’ Association's 'Research Worth Reading'. Brown actively bridges science and the arts through performances in music, dance, and theatrical projects, reflecting his belief in interdisciplinary creativity. Recent publications emphasize innovative teaching strategies, adversarial science communication, and entrepreneurship in STEM education. His work integrates art and science, evidenced by his roles in TV documentaries and children’s science media. Collaborations with colleagues like Alandeom W. Oliveira highlight his contributions to educational frameworks and student skill development.