جستجوی استاد راهنمای دانشگاهی

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +۷ مورد دیگر

Anamaria Crisan is an Assistant Professor at the University of Waterloo, affiliated with the Insight Lab. Her research focuses on interdisciplinary work at the intersection of Human-Computer Interaction (HCI), Data Visualization, and Applied AI/ML. She explores human-centered approaches to AI/ML systems, visualization design for decision-making, and healthcare data science applications. Dr. Crisan holds a PhD in Computer Science from the University of British Columbia (2019), an MSc in Bioinformatics (2010), and a BComp in Biomedical Computing from Queen’s University (2008). Her educational background bridges computer science, biology, and healthcare informatics. Her research interests include responsible AI/ML systems, interactive visualization for data-driven decisions, and leveraging visualization in healthcare to improve outcomes. She emphasizes transparency, trustworthiness, and human alignment in AI technologies. Her work spans diverse applications such as genomic epidemiology, dashboard design, and ethical AI evaluation. Notable contributions include studies on human-AI collaboration, visualization linters, and scalable dashboard census methodologies. She has published widely in top-tier venues like IEEE VIS and ACM CHI. Dr. Crisan’s lab (UW Insight Lab) focuses on human-centered approaches to automating data science and improving visualization practices in critical domains like healthcare and public health.

• Human-Computer Interaction
• Data Visualization
• Applied AI/ML
• +۳ مورد دیگر

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.

• Artificial Intelligence
• AI Safety
• AI Alignment
• +۹ مورد دیگر

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at the School of Computer Science, Georgia Institute of Technology, and an Associate Professor at the University of Toronto (on leave). He previously held a NUS Presidential Young Professorship at the National University of Singapore. His research focuses on automated reasoning, aiming to enable computing systems to handle uncertain real-world environments through scalable techniques integrating randomized algorithms, statistical inference, formal methods, distribution testing, and software engineering. Core research areas: Automated Reasoning, Formal Methods, Approximate Model Counting, Probabilistic Inference, Constraint Solving His research group has achieved significant recognition in both individual awards and publications. Key trends in his recent work include advancing model counting algorithms, developing frameworks for probabilistic explanations, and improving scalability in formal verification and constraint satisfaction. His tools have consistently ranked top in international competitions, demonstrating practical impact in automated reasoning. 2019 NRF Fellowship for AI 2022 ACP Early Career Researcher Award 2020 IEEE Intelligent Systems AI's 10 to Watch Top placements in Model Counting, SAT, and CAV competitions He mentors a diverse group of PhD and Master's students and collaborates with institutions worldwide. His group's publications span premier conferences in AI, formal methods, and design automation, reflecting interdisciplinary contributions to theoretical and applied computer science.

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۶ مورد دیگر

Caroline Lemieux is an Assistant Professor at the Department of Computer Science, University of British Columbia (UBC), with research focused on advancing software correctness, security, and performance through innovative testing and synthesis techniques. Her work bridges Programming Languages and Software Engineering , particularly in fuzz testing, specification mining, and program synthesis. PhD from University of California, Berkeley (2021), advised by Koushik Sen Postdoctoral researcher at Microsoft Research, NYC (2021-2022) Key contributions: FuzzFactory , CodaMOSA , Arvada , and Gauss Her research integrates machine learning with traditional testing methods, exemplified by projects like RLCheck (reinforcement learning for test generation) and AutoPandas (neural synthesis for dataframes). Recent publications analyze generator-based fuzzing challenges and propose hybrid strategies combining coverage feedback with AI-driven insights. Scientific Awards : ACM/SIGSOFT Best Paper Award (ESEC/FSE 2019) ACM/SIGSOFT Tool Demonstration Award (ISSTA 2019) ACM/SIGSOFT Distinguished Artifact Award (ISSTA 2019) NSERC Postgraduate Scholarship-Doctoral (PGS D) UBC Governor General's Silver Medal (2016) Teaching roles include: 2025W2: CPSC 539L - Topics in Programming Languages 2024W2: CPSC 410 - Advanced Software Engineering 2023W2: CPSC 410 (with Alex Summers) She supervises graduate and undergraduate researchers working on projects like ExploTest (automated unit test generation) and GRIMOIRE (grammar extraction from pseudo-rules). Her research team collaborates with institutions including Microsoft Research, Google, and academic partners in systems security and AI-driven testing.

• Programming Languages
• Software Engineering
• Fuzz Testing
• +۶ مورد دیگر

Florian Shkurti is an Assistant Professor in the Department of Computer Science at the University of Toronto Mississauga (UTM), affiliated with the UofT Robotics Institute, Vector Institute, and Acceleration Consortium. His research focuses on robotics, machine learning, and computer vision, emphasizing safe and effective autonomous systems in dynamic environments. He directs the Robot Vision and Learning (RVL) lab, exploring areas like environmental monitoring, autonomous navigation, and mobile manipulation. Research Interests: His work spans robotics, machine learning, and computer vision. Key areas include robot perception, planning under uncertainty, safe exploration, imitation learning, and applications in field robotics, autonomous vehicles, and chemistry lab automation. He develops methods enabling robots to perceive, reason, and act safely in collaboration with humans. Publications: Recent work includes advancements in safe multitask learning, interactive crowd navigation, and diffusion models for trajectory planning. His research bridges theoretical foundations with real-world applications in environmental science and autonomous systems. Affiliations: Faculty Member, UofT Robotics Institute; Faculty Affiliate, Vector Institute; Faculty Member, Acceleration Consortium. He also holds positions at UTM's Mathematical & Computational Sciences department. Teaching: Courses include Imitation Learning for Robotics, Neural Networks, and Mobile Robotics. He emphasizes hands-on experience with autonomous systems through projects involving RC cars and simulation tools. Labs & Teams: Leads the RVL lab, collaborating on projects like RoboCulture (automated biological experimentation) and SICNav (safe crowd navigation systems). The lab focuses on cross-disciplinary robotics solutions for real-world challenges.

• Robotics
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .

• Artificial Intelligence
• Deep Learning
• Combinatorial Optimization
• +۷ مورد دیگر

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.

• Data Systems for AI
• Retrieval-Augmented Generation (RAG)
• Database Systems
• +۴ مورد دیگر

Alexander Summers is an Associate Professor at the Department of Computer Science , University of British Columbia . He joined UBC in March 2020 after serving as a Senior Researcher (Oberassistent) at ETH Zurich from 2014-2020. His research bridges Programming Languages , Formal Methods , and Software Engineering , with a focus on automated verification tools for heap-based and concurrent programs. MSc Joint Mathematics and Computer Science, Imperial College London (2004) PhD Computer Science, Imperial College London (2009) Postdoc, ETH Zurich (2009-2014) Summers leads the Prusti Project , developing deductive verification tools for Rust, and contributes to the Viper Project for intermediate verification languages. His work addresses challenges in: Memory safety and concurrency verification Ownership models and aliasing control Automated reasoning with SMT solvers Resource-oriented programming specifications Debugging verification condition quantifiers Formal validation of verification infrastructure His research has been recognized with a Amazon Research Award and ACM SIGPLAN Distinguished Paper Awards . He teaches courses like Advanced Software Engineering and Program Verifiers and Program Verification , and supervises graduate students in formal verification and Rust-related research.

• Programming Languages
• Formal Methods
• Software Engineering
• +۴ مورد دیگر

Benjamin Alarie is a Professor and the Osler Chair in Business Law at the University of Toronto Faculty of Law. He is a leading scholar at the intersection of artificial intelligence, law, and taxation, and the co-founder and CEO of Blue J, an AI-powered tax research platform used by over 3,000 organizations across North America and the UK. Education: LL.M. – Yale Law School (2003) J.D. – University of Toronto (2002), with honours M.A. – University of Toronto (2002) B.A. – Wilfrid Laurier University (1999), with high distinction Research Interests: Alarie’s research spans tax law , legal theory , judicial behaviour , and the applications of machine learning in law . He introduced the concept of the “legal singularity”—the idea that legal systems may evolve toward greater clarity and predictability through AI and computational methods. His work explores how AI can enhance legal reasoning, regulatory design, and tax compliance. He is particularly focused on the ethical and practical implications of generative AI in legal and tax practice, including liability, transparency, and the future role of human practitioners in AI-augmented systems. Scientific Awards: PROSE Award (2024) for The Legal Singularity Donner Prize shortlist (2024) for The Legal Singularity vLex Fastcase 50 Honoree (2023) Top 50 Changemaker, The Globe and Mail (2022) Alan Mewett QC Prize for Excellence (2009) Advising & Grants: Alarie is a frequent advisor to courts, government agencies, and global professional networks. His work at Blue J involves strategic partnerships with CPA.com, CPA Canada, the National Association of Tax Professionals, Big 4 accounting firms, and Tax Notes. He also serves as a faculty affiliate at the Vector Institute for AI and the Schwartz Reisman Institute for Technology and Society. Labs & Teams: As CEO of Blue J, Alarie leads a multidisciplinary team of legal scholars, engineers, and data scientists. The platform is used by tax authorities and accounting firms to deliver precise, defensible, and efficient tax research and decision-making tools.

• Tax Law
• Artificial Intelligence and Law
• Legal Theory
• +۳ مورد دیگر

Catherine Valcke is a Professor at the University of Toronto Faculty of Law , where she served as Associate Dean and Acting Associate Dean. She holds degrees from the University of Sherbrooke (LL.B. Civil Law), University of Toronto (LL.B. Common Law), University of Chicago (LL.M.), and Columbia University (S.J.D.). Her academic career includes visiting appointments at the University of Montreal , University of Tunis II , and the Center for Transnational Legal Studies . Education: J.S.D., Columbia University (1997) LL.M., University of Chicago (1989) LL.B. (Common Law), University of Toronto (1988) LL.B. (Civil Law), University of Sherbrooke (1985) Research focuses on comparative legal theory , contractual obligations , and Anglo-French legal convergence , with a current SSHRC-funded project: French and English Private Law Paradigms . Her 2018 book Comparing Law: Comparative Law as Reconstruction of Collective Commitments proposes an interpretive framework for comparative studies. Recent publications analyze legal harmonization in bilingual contexts and AI’s impact on legal scholarship , including co-authorship of the forthcoming Théorie des Obligations (5th ed., 2022) and co-editorship of the Elgar Encyclopedia of Comparative Law (3rd ed., 2023). Awards include SSHRC grants and election to the International Academy of Comparative Law (2007). Her work has been cited by the UK House of Lords in Chartbrook Ltd. v. Persimmon Homes Ltd. (2009).

• Comparative Law
• Contract Law
• Civil Law
• +۹ مورد دیگر

Professor Guy-Vincent Jourdan is affiliated with the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Ph.D. from Université de Rennes/INRIA (France, 1995) focusing on distributed systems analysis. Prior to academia, he served as CTO and CEO of Decision Academic Graphics, an Ottawa-based firm. His research interests span software security, cybersecurity (including cybercrime prevention), distributed systems modeling, formal methods, mobile applications, and rich internet applications. Specific technical emphases include phishing detection systems, blockchain fraud analysis, and adversarial machine learning. Professor Jourdan has pioneered tools like D-ForenRIA for reconstructing user interactions in Rich Internet Applications and contributed to cybersecurity frameworks such as HEART for log anomaly detection. His work integrates machine learning techniques with domain-specific challenges in network security and software verification. His publications (2023-2025) reflect advancements in AI-driven vulnerability analysis, blockchain fraud detection, and automated phishing detection systems. Notable projects include SV-TrustEval-C for source code vulnerability analysis and Intellitweet for social media threat detection. While no scientific awards are explicitly listed, his prolific publication record and industry-academia transition highlight sustained contributions to computer science and cybersecurity domains.

• Software Security
• Cybersecurity
• Distributed Systems
• +۶ مورد دیگر

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

• Machine Learning
• Artificial Intelligence
• GFlowNets
• +۷ مورد دیگر

Kevin Leyton-Brown is a Professor of Computer Science at the University of British Columbia (UBC), holding a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). He is also an Associate Member of the Vancouver School of Economics and a Fellow of the Royal Society of Canada, ACM, and AAAI. His research focuses on AI, machine learning, computational economics, and game theory, with notable contributions to algorithmic market design, heuristic algorithms, and large language models. He co-authored influential textbooks on multiagent systems and game theory, and his work has been recognized with prestigious awards including the INFORMS Franz Edelman Award and the Killam Teaching Prize. Education: PhD (Computer Science), Stanford University; MSc (Computer Science), Stanford University; BSc (Computer Science), McMaster University. Research Interests: Artificial Intelligence, Machine Learning, Game Theory, Computational Economics, Algorithmic Game Theory, Market Design, and Large Language Models. He has developed impactful tools like SATzilla, AutoWEKA, and Mechanical TA, and contributed to high-stakes projects such as spectrum auction design and Ugandan agricultural market platforms. Awards & Recognition: Royal Society of Canada Fellow (2023), ACM SIG-KDD Research Track Test of Time Award (2023), INFORMS Franz Edelman Award (2018), ACM Fellow (2020), AAAI Fellow (2018), Killam Teaching Prize (UBC), and numerous paper awards from top conferences like AAAI, ICML, and ACM-EC. Leadership & Affiliations: Director of UBC’s CAIDA and AIM-SI research clusters, former Chair of ACM SIG-Ecom, and advisor to companies like AI21 Labs and Auctionomics. He has held visiting roles at institutions including MIT, Harvard, and the Simons Institute.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +۵ مورد دیگر

Istvan David is an Assistant Professor in the Department of Computing and Software at McMaster University , with research expertise spanning Digital Twins , Model-Driven Engineering , and Sustainability . His work bridges theoretical and applied domains, focusing on smart ecosystems , collaborative modeling , and AI-driven simulation . Key contributions include frameworks for digital twin evolution and interoperability in sustainable systems. Education : BSc, MSc, and PhD in Computer Engineering and Computer Science from Budapest University of Technology and Economics, and University of Antwerp. Research Areas : Digital Twins, Model-Driven Engineering, Reinforcement Learning, Smart Ecosystems, Sustainability, Collaborative Modeling, Cyber-Biophysical Systems, and Software Architecture. Recent Article Trends emphasize AI integration with digital twins, collaborative modeling in industrial contexts, and sustainable systems engineering . His work often combines machine learning with formal modeling to address challenges in technical sustainability and smart agriculture .

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +۶ مورد دیگر