جستجوی استاد راهنمای دانشگاهی

Kuldeep S. Meelمشاهده پروفایل

دانشیار

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +۶ 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.

Linyi Liمشاهده پروفایل

استادیار

• Deep learning
• Trustworthy machine learning
• Computer security
• +۴ مورد دیگر

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

Caroline Lemieuxمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Fuzz Testing
• +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.

Miao Zhengjieمشاهده پروفایل

استادیار

• Database Systems
• Data Management
• Data Curation and Integration
• +۳ مورد دیگر

Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.

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.

Xiaodong Linمشاهده پروفایل

استاد

• Information Security
• Privacy-Enhancing Technologies
• Digital Forensics
• +۴ مورد دیگر

Dr. Xiaodong Lin is a Professor at the University of Guelph's School of Computer Science and an IEEE Fellow (2017) for contributions to secure vehicular communications. He leads the Blockchain Technology and Cybersecurity (BTC) lab, focusing on privacy-enhancing technologies, digital forensics, wireless network security, blockchain applications, and DeFi security. PhD, Beijing University of Posts and Telecommunications, China PhD, University of Waterloo, Canada His research bridges theoretical and applied domains in cybersecurity, particularly vehicular networks, smart grids, and decentralized systems. Recent work examines blockchain security, privacy-preserving protocols for IoT, and secure data aggregation in wireless networks. Key publication trends include secure fog computing for vehicular crowdsensing, privacy-preserving authentication in 5G, and cryptographic solutions for smart grids. Awards highlight multiple Best Paper recognitions at IEEE conferences. IEEE Fellow (2017) Best Paper Awards (IEEE INFOCOM 2018, GLOBECOM 2017, SECURECOMM 2016) Dr. Lin supervises graduate students in blockchain security, AI security, and digital forensics. His lab provides financial support to qualified students.

Shiva Nejatiمشاهده پروفایل

استاد

• Software Engineering
• Cyber-Physical Systems
• Autonomous Vehicles
• +۷ مورد دیگر

Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.

Juergen Dingelمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Engineering
• Formal Methods
• +۷ مورد دیگر

Juergen Dingel is a Professor in the School of Computing at Queen's University, Canada. He joined the faculty in 2000 and holds a PhD in Computer Science from Carnegie Mellon University (1999). His research focuses on software modeling, model-driven engineering, formal methods, and formal verification, with applications in real-time systems and embedded systems. He leads the Modeling and Analysis in Software Engineering (MASE) research group. Education: PhD in Computer Science, Carnegie Mellon University (1999) M.Sc. in Pure and Applied Logic, Berlin University of Technology (1994) M.Sc. in Computer Science, Berlin University of Technology (1992) Research Interests: Model-driven engineering and transformation Formal specification and verification Automated testing and debugging Real-time and embedded systems Service composition and distributed systems His work emphasizes practical tools like Papyrus-RT and MDebugger , integrating formal methods into software development. Grants & Collaborations: Funded by NSERC, OCE, and industry partners (IBM, GM, Ericsson) Focus on automotive systems, IoT, and safety-critical applications Service: Editorial board member for SoSyM , STTT , and JOT Former chair of the MODELS Steering Committee (2016–2018) PC co-chair for MODELS 2014 and FMOODS/FORTE 2011 Labs & Teams: Leads the MASE group, which develops open-source tools for model-driven engineering. Collaborates with industry on automotive and IoT projects.

Olivia Di Matteoمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Software Engineering
• Quantum Compilation
• +۵ مورد دیگر

Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.

• Global Navigation Satellite Systems (GNSS)
• Avionics Systems
• Wireless Communication
• +۷ مورد دیگر

René Jr Landry is a full Professor in the Department of Electrical Engineering at École de technologie supérieure (ETS), Université du Québec, specializing in Global Navigation Satellite Systems (GNSS), avionics, and wireless communication technologies. His academic journey includes a B.Ing. from Polytechnique Montréal, M.Sc. from University of Surrey (UK), and Ph.D. from SupAréo in Toulouse. He maintains active research leadership through two key laboratories: LASSENA (Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics) and LACIME (Communications and Microelectronic Integration Laboratory). His research spans critical aerospace navigation domains including GNSS signal processing, inertial navigation systems, software-defined radio for avionics, radio frequency interference mitigation, and indoor positioning technologies. Landry's work addresses real-world challenges in satellite navigation robustness, precision positioning in urban/denied environments, and next-generation avionic system security. His current projects focus on blockchain-enhanced IoT security, AI-driven GNSS disruption analysis, and adaptive RF front-ends for multi-band avionics applications. Analysis of his recent publications reveals strong emphasis on resilient positioning systems through multi-constellation integration (particularly Iridium-NEXT), blockchain applications for navigation security, and explainable AI techniques for GNSS signal quality assessment. His work increasingly bridges traditional navigation engineering with cutting-edge security and machine learning paradigms. 2014 Prix d'excellence du c.a. pour les services à la collectivité Landry has supervised over 100 graduate students across doctoral, master's, and research projects since 2005, with current supervision extending through Summer 2025. His research funding supports multiple industry partnerships focused on avionics certification, software-defined radio implementations, and next-generation navigation systems. The LASSENA laboratory under his leadership develops certified avionic products from open-source SDR platforms and advances multi-sensor fusion techniques for challenging navigation environments. His research infrastructure includes specialized facilities for GNSS signal simulation, avionics hardware testing, and multi-sensor integration. Current work emphasizes flight-tested validation of RF front-end technologies, blockchain-secured navigation data, and real-time interference mitigation systems for aviation applications.

Zhen Ming Jiangمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Computer Systems
• +۶ مورد دیگر

Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at York University's Department of Electrical Engineering and Computer Science. His research bridges software engineering, artificial intelligence, and computer systems with significant industrial impact. Dr. Jiang earned his Ph.D. from Queen's University's School of Computing and MMath/BMath degrees from the University of Waterloo's David R. Cheriton School of Computer Science. During his doctoral studies, he collaborated with BlackBerry's Performance Engineering team, developing tools now used daily to monitor commercial software systems. His research focuses on engineering rigor for AI-powered applications , software engineering evolution in the Generative AI era , and performance optimization of large-scale systems . Key areas include software performance engineering, mining software repositories, debugging distributed systems, source code analysis, and software visualization. His work combines empirical studies with practical tool development. Recent publications reveal strong trends in applying AI to software engineering challenges, particularly in machine learning systems reliability, blockchain efficiency, and AIOps solutions. His research consistently emphasizes empirical validation using real-world systems and industrial case studies. Scientific recognition includes: York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems NSERC Discovery Accelerator Supplements (DAS), 2020 Best Paper Award at ICST 2016 IEEE Software Best SEIP Paper at ICSE 2015 Ph.D. Research Achievement Award at Queen's University Multiple best paper awards at WCRE, MSR, and ICSE Dr. Jiang actively supervises graduate students and has secured competitive research funding including NSERC grants. His service includes program committee roles for top conferences (ICSE, ASE, ICSME) and editorial work for leading journals (TSE, TOSEM, EMSE). He leads research initiatives focused on foundation model-powered systems, collaborating with industry partners on performance monitoring and debugging solutions for large-scale distributed environments.

Istvan David McMasterمشاهده پروفایل

استادیار

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +۶ 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 .

Xujie Siمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۵ مورد دیگر

Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.

• Software Engineering
• Model-Driven Engineering
• Automotive Embedded Systems
• +۳ مورد دیگر

Vera Pantelic is an Adjunct Assistant Professor in the Department of Computing and Software at McMaster University. Her research focuses on software engineering practices for model-based development in automotive systems, particularly centralized Electrical/Electronic (E/E) architectures, Simulink modeling, and supervisory control of probabilistic discrete event systems. Education: Not explicitly mentioned in the text. Her scholarly activity includes extensive contributions to conferences and journals in automotive software engineering, model transformation, and real-time systems. Her work addresses challenges in modularity, documentation, and compliance within automotive embedded systems. Her recent publications emphasize advancements in centralized E/E architectures, model-driven testing, and assurance cases for automotive safety. She collaborates on topics integrating software engineering principles with automotive domain requirements. Scientific Awards: No specific awards mentioned in the text. She serves as an advisor in software engineering, though specific student names are not listed. Her projects involve simulation-based testing, model refactoring, and compliance frameworks, supported by industry partnerships and academic grants. Her work contributes to labs and teams focused on automotive software reliability and model-driven engineering. No explicit lab or team affiliations are detailed in the provided text.

Mahdi Tew-Fikمشاهده پروفایل

استاد

• Hydraulic Engineering
• Hydrological Engineering
• Modeling and Simulation
• +۱۱ مورد دیگر

Professor Mahdi Tew-Fik is affiliated with Polytechnique Montréal as a Full Professor in the Department of Civil, Geological and Mining Engineering . His research focuses on hydraulic engineering , sediment transport , dam safety , and flood modeling , with expertise in numerical simulations and VOF methods . His educational background includes a B. Ing. from Polytechnique d'Alger , M.Sc. from the University of Liège , DESS from UQÀM , and Ph.D. from Polytechnique Montréal . He has supervised numerous Ph.D. and Master's students in projects related to river hydraulics and dam failure analysis . Professor Mahdi has received the Discovery Grant (2021) for his research. His recent publications emphasize free-surface flow modeling , multi-fluid simulations , and flood risk assessment . He is a member of the Experimental and Digital Water Flow Engineering Group (GENIE EAU) .