جست‌وجوی استاد و پژوهشگر

Richard S. Suttonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Intelligence
• +۵ مورد دیگر

Richard S. Sutton is a Professor in the Department of Computing Science at the University of Alberta , a Research Scientist at Keen Technologies, and the Principal Investigator at the Reinforcement Learning and Artificial Intelligence Lab . He also serves as the Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii) and as Chief Scientific Advisor there. Additionally, he founded the Openmind Research Institute and is Chief Scientific Officer at ExperienceFlow. His research focuses on identifying general computational principles underlying intelligence and goal-directed behavior. He emphasizes the interaction between intelligent agents and their environment , exploring how goals, choices, and information sources emerge from this interaction. His work spans reinforcement learning (RL) , agent-environment dynamics, and ethical considerations in AI, such as robot rights and moral philosophy. His articles include technical works on RL, ethical frameworks, and philosophical essays like The Bitter Lesson (2019) and The Definition of Intelligence (2016). Canada CIFAR AI Chair Co-author of foundational RL textbook Developed Tile Coding Software for RL

David Lieمشاهده پروفایل

استاد

• Computer Systems Security
• Trusted Execution Environments
• Mobile Platform Security
• +۷ مورد دیگر

David Lie is a Professor at the University of Toronto, jointly appointed in the Edward S. Rogers Department of Electrical and Computer Engineering, Department of Computer Science, and Faculty of Law. He directs the Schwartz Reisman Institute for Technology and Society, co-founded the IT3 Lab, and serves as Associate Director at the Data Sciences Institute. His research focuses on securing computer systems through operating systems, architecture, and formal verification approaches. B.A.Sc (University of Toronto, 1998) M.S. (Stanford, 2001) Ph.D. (Stanford, 2004) His research emphasizes building secure systems for mobile platforms and cloud computing, with significant contributions to trusted execution environments (XOM architecture precursor to Intel SGX/ARM TrustZone) and Android permission mapping (PScout tool). Recent work spans cryptographic side-channels, web tracking detection, and AI safety. Key honors include SOSP 2003 Best Paper, Ontario MRI Early Researcher Award (2008), Connaught Global Challenge Award (2017), and Canada Research Chairs (Tier 2 2013-2018, Tier 1 current). He has secured over $30M in research funding and served as General Chair for CCS 2018. Lie leads the IT3 Lab (Technology and Policy Integration), collaborates with industry leaders (Google, VMware, Telus), and mentors graduate students working on practical security implementations. He co-teaches ECE1724: Privacy Problems with Lisa Austin from the Faculty of Law, reflecting his technology-policy interests.

Jean-François Godboutمشاهده پروفایل

استاد

• Data Science
• AI Safety
• Generative AI
• +۶ مورد دیگر

Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications

• Software Engineering
• Human-Computer Interaction
• Information Visualization
• +۳ مورد دیگر

Margaret-Anne Storey is a Professor of Computer Science at the University of Victoria and holds the Canada Research Chair Tier I in Human and Social Aspects of Software Engineering. She is affiliated with the Faculty of Engineering and Computer Science and leads the Computer Human Interaction and Software Engineering Lab. Her research focuses on software engineering, human-computer interaction, information visualization, and collaborative work practices. Storey earned her PhD from Simon Fraser University (SFU). Her work bridges socio-technical systems, developer experience (DevEx), and the ethical integration of AI in software engineering. She has pioneered studies on remote work productivity during the pandemic, developer satisfaction, and the human-centered design of tools. Key research interests include understanding developer productivity through frameworks like SPACE (2021), analyzing code review strategies, and exploring the impact of generative AI on software engineering research. Her work often employs mixed-methods approaches and emphasizes empirical validation. Storey has been recognized with the Canada Research Chair Tier I (2020–present). Her contributions span keynote addresses at major conferences (e.g., ICSE), framework development (e.g., DASP for security practices), and interdisciplinary collaborations with organizations like Microsoft. Her research also addresses societal challenges, such as drug-checking technology and participatory culture in education. She advocates for human-centric AI in software engineering and critical questioning of AI’s societal impacts.

Sudhakar Gantiمشاهده پروفایل

دانشیار

• Cloud Computing Resource Management
• Software Defined Networking (SDN)
• Traffic Management and Quality-of-Service
• +۳ مورد دیگر

Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.

Yizhou Zhangمشاهده پروفایل

استادیار

• Programming Languages
• Compiler Design
• Formal Methods
• +۳ مورد دیگر

Yizhou Zhang is an Assistant Professor in the Department of Computer Science at the University of Waterloo. He holds a PhD and MS from Cornell University (2019 and 2016) and a BS from Shanghai Jiao Tong University (2012). His research focuses on programming languages, including design, implementation, and theory, with emphasis on formal methods, compiler optimization, and probabilistic programming. Education: PhD, Cornell University, 2019 MS, Cornell University, 2016 BS, Shanghai Jiao Tong University, 2012 Research interests span programming language theory, compiler construction, and formal verification. His work explores topics like certified compilers, effect handlers, and probabilistic program analysis. Recent publications emphasize formal models for memoization, nested family polymorphism, and bidirectional control flow. His publications reflect contributions to probabilistic programming semantics, compiler optimization techniques, and type systems. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. Zhang’s research often intersects with formal methods and practical compiler implementation challenges.

Pierre Duchesneمشاهده پروفایل

استاد

• Time Series Analysis
• Sampling Theory
• Multivariate Analysis
• +۲ مورد دیگر

Pierre Duchesne is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal . He serves as Professor-responsibility for the M.Sc. and Ph.D. in Statistics programs (2000-2004). His research focuses on applied statistics with emphasis on: Time Series Analysis (univariate and multivariate models, serial correlation testing, portmanteau statistics) Sampling Theory (robust estimation methods, calibration estimators) Multivariate Analysis (ARCH effects, vector autoregressive models, causality testing) Applications in Econometrics and Financial Econometrics His work combines theoretical development with practical implementation through: Wavelet-based diagnostic methods Simulation studies for model validation Software development (S-PLUS/SAS) for statistical analysis Collaboration with organizations like Statistics Canada and Canadian Journal of Statistics He has served as Associate Editor for journals including Computational Statistics & Data Analysis (CSDA) and Canadian Journal of Statistics (CJS/RCS) .

• Software Engineering
• Formal Methods
• Safety and Security Assurance
• +۷ مورد دیگر

Marsha Chechik is a Professor in the Department of Computer Science at the Faculty of Arts and Science, University of Toronto. She previously served as Department Chair from 2019-2022 and as Acting Dean in the Faculty of Information from July-December 2022. Her academic career spans numerous research contributions and leadership roles within the software engineering community. Professor Chechik's primary research interests focus on software engineering with emphasis on formal methods to enhance software quality. Her work encompasses scalable automated verification techniques including model-checking and theorem-proving, formal specification languages, verification of protocols, non-classical logics, and reasoning under inconsistency. She has made significant contributions to model management, software product lines, safety and security assurance, and automotive safety systems. Her research bridges theoretical foundations with practical applications, particularly in managing uncertainty in software models and developing techniques for automotive safety verification. Her recent publications demonstrate a strong focus on model management and transformations, software product lines and variability analysis, safety and security assurance cases, and semantic analysis of software evolution. The integration of formal methods with practical software engineering challenges, especially in safety-critical domains like automotive systems, represents a consistent theme throughout her work. Professor Chechik has been recognized with multiple prestigious awards including a Best Paper Award at RE'12, a SIGSOFT Distinguished Paper Award at ICSE'12, a Best Student Paper Award at CASCON'07, and a Distinguished Paper Award at ICSE'07, highlighting the impact and quality of her research contributions. She actively supervises graduate students and has successfully guided numerous Ph.D. candidates to completion. Her group has produced graduates who predominantly pursue research careers in both academic institutions and industrial research labs. She currently leads several funded projects including the Automotive Safety project (in collaboration with General Motors) and the Software Evolution project, focusing on practical applications of her research interests. Professor Chechik leads the Software Engineering Lab at the University of Toronto, where innovative projects like Matchmakers (a serious game for software engineering) are developed. Her collaborative network extends across institutions, with notable partnerships including Julia Rubin at the University of British Columbia, demonstrating her commitment to interdisciplinary research and academic collaboration.

Alan Wassyngمشاهده پروفایل

استاد

• Software Engineering
• Formal Methods
• Safety-Critical Systems
• +۶ مورد دیگر

Alan Wassyng is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. With over four decades of academic contributions, he specializes in formal methods, safety-critical systems, and software assurance cases. Key research intersections: Automotive software safety Medical device software certification Cyber-physical systems engineering Model-driven development with domain experts His scholarly work emphasizes rigorous methodologies for software verification, particularly through tabular expressions and Workflow+ models. Recent projects include a $2M GM Canada partnership to advance automotive safety systems. Teaching leadership spans interdisciplinary capstone projects in biomedical engineering, covering software design, safety-critical development, and mechatronics applications.

Vijay Ganeshمشاهده پروفایل

استاد

• SAT/SMT Solvers
• Artificial Intelligence
• Formal Methods
• +۵ مورد دیگر

Dr. Vijay Ganesh is a Professor of Computer Science at Georgia Institute of Technology, where he also serves as Associate Director of the IDEaS Institute and is affiliated with Tech AI. Previously, he held roles as Associate Professor (2018–2023) and Assistant Professor (2012–2018) at the University of Waterloo, and Research Scientist at MIT (2007–2012). He earned his PhD from Stanford University in 2007. His research focuses on SAT/SMT solvers and their applications in AI, software engineering, security, mathematics, and physics. Notable contributions include developing solvers like MapleSAT, Z3str4, and AlphaZ3, and exploring machine learning-augmented reasoning. He has led projects in logic for AI, proof complexity, and security of blockchain technologies. His awards include ACM Impact Paper (2019), ACM Test of Time (2016), and DATE’s Ten-Year Most Influential Paper (2008). He has advised startups like Quantstamp, a blockchain security firm, and co-directed the Waterloo AI Institute (2021–2023). His teaching includes courses on discrete mathematics, software engineering, and AI. Education: PhD in Computer Science, Stanford University (2007); Master’s in Electrical Engineering, Stanford (2000) Research Interests: SAT/SMT solvers, formal methods, automated testing, AI security, combinatorial mathematics Affiliations: Georgia Tech’s School of Computer Science, IDEaS Institute

Andrej Bogdanovمشاهده پروفایل

استاد

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۶ مورد دیگر

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

Benjamin C. M. Fungمشاهده پروفایل

استاد

• Data Mining
• Artificial Intelligence
• Cybersecurity
• +۶ مورد دیگر

Benjamin C. M. Fung is a Full Professor at McGill University's School of Information Studies and holds the Canada Research Chair in Data Mining for Cybersecurity. He is also an Associate Member at the School of Computer Science and affiliated with MILA and the McGill Digital Humanities Initiatives. His research focuses on data mining, AI, cybersecurity, privacy protection, and their applications in crime investigation, health informatics, and smart cities. Education: Ph.D. in Computing Science, Simon Fraser University (2007) M.Sc. and B.Sc. in Computing Science, Simon Fraser University Professional Engineer (P.Eng.) in Software Engineering, Ontario Research Interests: Data mining techniques for cybersecurity, AI ethics, privacy-preserving technologies, malware analysis, and foreign interference detection. His work has been supported by NSERC, SSHRC, DRDC, and FRQNT. Key Contributions: Developed the Kam1n0 tool for malware analysis, featured in Montreal's technological innovations. Co-founded the McGill Data Mining and Security (DMaS) Lab. Authored over 180 refereed publications with an h-index of 62. Awards & Recognition: 2022 IEEE CSR Best Research Paper Award ACM & IEEE Senior Member (2013) Top 2% Global Scientists (2021–present) Media & Public Engagement: Frequently comments on cybersecurity, disinformation, and foreign interference for CBC, CBC Radio, OMNI TV, and international outlets. His op-eds address topics like AI ethics and national security.

Hedayat Zarkoobمشاهده پروفایل

پژوهشگر

• AI in Education
• Educational Technology
• Machine Learning Applications
• +۳ مورد دیگر

Hedayat Zarkoob is a Researcher in the Department of Computer Science at the University of British Columbia (UBC), where he completed his PhD under Prof. Kevin Leyton-Brown. His work focuses on AI applications in education, particularly in peer grading systems, active learning, and large-scale classroom engagement. He is actively involved in teaching within UBC's Master of Data Science (MDS) program and the Computer Science department, leading courses on privacy, algorithms, communication, and societal impacts of technology. Education: PhD in Computer Science, UBC (supervised by Kevin Leyton-Brown) MSc in Computer Science, Simon Fraser University (supervised by Andrei Bulatov) Research Interests: Zarkoob's research bridges AI and education, with emphasis on scalable assessment tools (e.g., Mechanical TA 2), classroom engagement platforms (Agora), and AI-driven conference review systems. His work combines algorithm design, educational technology, and human-AI collaboration. Recent Projects: Agora, an open-source tool for student participation in large courses; Mechanical TA 2, a peer-grading system with algorithmic support; and AAAI 2021 workflow innovations. Teaching Roles: Upcoming courses include DSCI 541, 553, 542, 512, and CPSC 430. He has co-taught CPSC 430 (Computers and Society) since 2023.

Yousra Aaferمشاهده پروفایل

استادیار

• Mobile and Smart Device Security
• System and Software Security
• Android Security
• +۴ مورد دیگر

Yousra Aafer is an Assistant Professor in the Department of Computer Science at the University of Waterloo. Her research focuses on mobile and smart device security, system security, and software security, particularly in the context of Android and cyber-physical systems. She holds a Ph.D. and M.Eng. from Syracuse University. Education: Ph.D., Syracuse University, United States (2016) M.Eng., Syracuse University, United States (2012) Her research interests include analyzing vulnerabilities in Android systems, binary analysis, IoT security, and fuzzing techniques. She explores methods to enhance security through formalized protocols, probabilistic protection recommendations, and leveraging large language models for vulnerability detection. Her work addresses critical areas such as cross-language buffer overflow detection, residual API audits in custom ROMs, and cyber-physical inconsistency in robotic vehicles. Her recent publications span topics like Android security, binary disassembly (e.g., D-ARM), and IoT security protocols (e.g., ProFactory). She has also contributed to frameworks like Poirot for probabilistic protection recommendations and StochFuzz for efficient binary fuzzing. While no awards are explicitly mentioned, her extensive publication record indicates active recognition in the security research community. She advises on multiple projects but no student names are listed here. Her work often involves collaboration on tools and frameworks for practical security applications.

Mark Lawfordمشاهده پروفایل

استاد

• Software engineering
• Safety critical systems
• Digital & Smart Systems
• +۳ مورد دیگر

Mark Lawford is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. His expertise spans software engineering, safety critical systems, and model-based development, with a focus on digital & smart systems and transportation research clusters. Contact: Email: lawford@mcmaster.ca Website: www.cas.mcmaster.ca/~lawford His research explores automotive systems , formal verification , and model-driven engineering , particularly addressing challenges in automotive electrification , centralized E/E architectures , and safety assurance cases . Recent publications emphasize test case generation , change impact analysis , and model transformation tools for Simulink, SysML, and AUTOSAR. Key trends in his work include autonomous vehicle safety , real-time control systems , and integration of formal methods with industrial practices across automotive, biomedical, and nuclear domains.