جست‌وجوی استاد و پژوهشگر

• Electric Power Systems
• Renewable Energy Integration
• Power System Stability and Control
• +۳ مورد دیگر

Xiaozhe Wang is an Associate Professor in the Department of Electrical and Computer Engineering at McGill University, holding the Canada Research Chair (Tier II) in Resilient and Stable Zero-Emission Electric Power Grids and the Rubin & So Foundation Faculty Scholar. He joined McGill in 2016 after a postdoctoral fellowship at MIT under Prof. Konstantin Turitsyn. He earned his Ph.D. from Cornell University (2015), with a minor in Applied Mathematics, and holds degrees from Zhejiang University (B.S., 2010) and Cornell (M.Eng., 2011). His research focuses on resilient power grids, data-driven methodologies, and cybersecurity in energy systems. Key areas include electric vehicle integration, stability assessment, and control strategies for renewable energy systems. He develops advanced techniques for uncertainty quantification, wide-area monitoring, and adversarial attack detection. Notable achievements include pioneering work on polynomial chaos expansion for probabilistic assessment and sparse identification for nonlinear dynamics. His articles explore topics like microgrid control, false data injection attacks, and decentralized energy trading. Awards: Canada Research Chair (Tier II), Rubin & So Foundation Scholar Grants/Projects: Focus on resilience, cybersecurity, and renewable integration funded via NSERC, Mitacs, and industry partnerships. He advises students through fellowships like Mitacs Elevate and Banting Postdoctoral Fellowships. His lab emphasizes interdisciplinary approaches to modern grid challenges, including lab experiments and field trials.

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

• 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.

• Energy Efficiency in Software
• Multicore Memory Systems
• Hardware/Software Co-Design
• +۳ مورد دیگر

Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.

Deepa Kundurمشاهده پروفایل

استاد

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +۴ مورد دیگر

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

Mireille E. Brouckeمشاهده پروفایل

استاد

• Mathematical System Theory
• Systems Neuroscience
• Reach Control Problems
• +۳ مورد دیگر

Mireille E. Broucke is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, where she is a member of the Systems Control Group within the Faculty of Applied Science and Engineering. She teaches various undergraduate and graduate courses including Adaptive Control and Reinforcement Learning, Robot Modeling and Control, and Introduction to Nonlinear Systems, demonstrating her commitment to education in control systems engineering. Professor Broucke's research focuses on mathematical system theory with particular emphasis on Systems Neuroscience, Reach Control Problems, and Patterned Linear Systems. Her work bridges theoretical control theory with applications in neuroscience and robotics. She has developed theoretical frameworks for understanding neural adaptation through control theory principles and has applied reach control theory to robotics problems including motion control of quadrocopters. Her research demonstrates how control theory can provide insights into biological systems while also advancing engineering applications. Her recent publications show a clear trend toward applying control theory to neuroscience, particularly in understanding adaptive internal models in the brain. The publications span from theoretical reach control problems on simplices and polytopes to practical applications in robotics and neural systems. Her work increasingly focuses on the intersection of control theory and neuroscience, examining how the brain implements adaptive control mechanisms for motor functions. This represents a significant shift from her earlier work which was more focused on pure control theory problems. Professor Broucke has advised several PhD students including Fatima Ghadieh, Erick Mejia Uzeda, and Mohamed Hafez. Her research has been supported by various grants that enable her work in control theory and its applications to neuroscience and robotics. She maintains an active research program with numerous publications in top control theory journals including IEEE Transactions on Automatic Control, Automatica, and Systems and Control Letters.

Lacra Pavelمشاهده پروفایل

استاد

• Game Theory
• Control Theory
• Optimization
• +۷ مورد دیگر

Lacra Pavel is a Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, Faculty of Applied Science and Engineering. She joined the department in August 2002 after industry experience at Nortel Networks and Solinet Systems, and remains active in the System Control Group and Photonics Group. Her educational background includes: Diploma of Engineering (with distinction) in Automatic Control, Technical University Gh. Asachi of Iasi, Romania (1989) PhD in Electrical and Computer Engineering, Queen's University at Kingston (1996) Research focuses on integrating game theory, control theory, and optimization within networked systems. She pioneered applications in noncooperative/evolutionary game theory for network control, nonlinear/robust control frameworks, and energy-efficient optical/transportation networks. Current work develops mathematical foundations for learning in games via control-theoretic approaches to enable autonomous multi-agent network optimization. Recent publications (2019-2013) reveal dominant trends: distributed Nash equilibrium seeking using passivity-based and operator-splitting methods, stability analysis for optical network power control with time-delays, and extensions to transportation systems like railway timetabling. Key subfields include graphical games, ADMM algorithms, and Lyapunov-based boundary control for distributed parameter systems. Scientific recognition includes: Fellow of the IEEE (2025) for contributions to game theory, control, and optimization for network systems Connaught New Staff Award, University of Toronto (2003) New Opportunities Infrastructure Award, CFI/OIT (2003) Award for Innovation, Solinet Systems (2001, 2002) Inventor Recognition Award, Nortel Networks (2000) She has advised over 20 graduate students including current PhD candidates and former students now at MIT, Princeton, Amazon, and Ciena. Major grants include the CFI/OIT New Opportunities Infrastructure Award (2003). Her research bridges theoretical game control with practical implementations in optical and transportation networks. As co-director of the System Control Group and member of the Photonics Group, she leads teams developing algorithms for autonomous network optimization. Current projects focus on stochastic approximation methods for multi-agent learning and energy-efficient network design.

Meyer Nahonمشاهده پروفایل

استاد

• Dynamics and Control
• Aerospace Engineering
• Unmanned Aerial Vehicles (UAVs)
• +۴ مورد دیگر

Meyer Nahon is a Professor at McGill University specializing in dynamics and control systems with cross-domain applications in aerospace, underwater vehicles, and robotics. He maintains active affiliations with the Aerospace Mechatronics Lab, Centre for Intelligent Machines (CIM), and McGill Institute for Aerospace Engineering (MIAE). His academic credentials include: Ph.D. from McGill University M.ASc. from University of Toronto B.ASc. from Queen's University Research spans fundamental mechanical systems (cable dynamics, vehicle dynamics), aeronautical systems (tethered aerostats, autonomous vehicles), underwater systems (ROVs/AUVs), and robotics (parallel mechanisms, flight simulators). His work integrates sensor-based motion measurement using accelerometers, rate gyros, and GPS for comprehensive dynamic analysis across platforms. Recent publications (2015-2019) reveal concentrated UAV research including aerobatic maneuvering, slipstream dynamics, and propeller modeling, while extending to marine structures through mooring system analysis. This demonstrates consistent application of control theory across aerial, marine, and mechanical domains with emphasis on extreme maneuvering and system validation. As leader of the Aerospace Mechatronics Lab, he directs research in cable-actuated systems (cranes, elevators), UAV motion planning, and underwater vehicle dynamics within McGill's collaborative research ecosystem.

Ilia Polushinمشاهده پروفایل

دانشیار

• Robotics
• Teleoperation
• Control Systems
• +۱ مورد دیگر

Ilia Polushin is an Associate Professor at the Department of Electrical and Computer Engineering , Western University . His research focuses on Robotics, Teleoperation, Control Systems, and Biomedical Applications . Education : Ph.D. in Electrical Engineering (Carleton University), Cand. Sci. in Automatic Control (Saint-Petersburg Electrotechnical University) His work primarily addresses stability and control of teleoperation systems with communication delays, using scattering transformation techniques and projection-based force reflection algorithms . He has contributed to haptic systems, surgical robotics , and networked control applications , including drilling systems and cooperative teleoperation. Recent publications emphasize stabilization of conic systems , multi-agent synchronization , and nonlinear networked control . His research spans Robotics, Control Theory, Biomedical Engineering, and Industrial Automation . Scientific Awards : Best Conference Paper Award, IEEE International Conference on Mechatronics and Automation (2006) He has advised students and collaborated with researchers in Robotics , Control Systems , and Biomedical Applications . His laboratory focuses on teleoperation systems and haptic interfaces .

Tamás Linderمشاهده پروفایل

استاد

• Information Theory
• Data Compression
• Source Coding
• +۵ مورد دیگر

Tamás Linder is a Professor of Mathematics and Engineering at Queen's University, Canada, affiliated with the Department of Mathematics and Statistics. He holds a P.Eng. designation and has been at Queen's since 1998. His academic journey includes a Ph.D. from the Hungarian Academy of Sciences (1992) and postdoctoral roles at the University of Hawaii and University of Illinois. Linder's research focuses on information theory, data compression, quantization, and machine learning, with notable contributions to source coding, joint source-channel coding, and privacy-aware estimation. Education: M.Sc. in Electrical Engineering (Technical University of Budapest, 1988), Ph.D. in Electrical Engineering (Hungarian Academy of Sciences, 1992). Research interests include information theory fundamentals, machine learning applications, statistical pattern recognition, and privacy-preserving techniques. His work bridges theoretical foundations and practical applications in communication systems, quantization theory, and distributed control. Key awards include IEEE Fellow (201x), Ontario Premier's Research Excellence Award (2002), and Queen's University Chancellor's Research Award (2003). He has advised numerous graduate students and postdocs, contributing to over 150 publications in top-tier venues like IEEE Transactions on Information Theory and Automatic Control. Linder's research groups and collaborations involve interdisciplinary projects in control systems, statistical inference, and signal processing. His labs focus on advancing zero-delay coding, distributed optimization, and privacy-aware machine learning systems.

Pirathayini Srikanthaمشاهده پروفایل

دانشیار

• Game Theory
• Large-scale Optimization
• Distributed Control
• +۵ مورد دیگر

Pirathayini Srikantha serves as Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering, holding a Tier 2 Canada Research Chair in Reliable and Secure Power Grid Systems with critical national security implications for Canada's power infrastructure. Her academic foundation includes: B.A.Sc. in Systems Design Engineering from University of Waterloo (2009) M.A.Sc. in Electrical and Computer Engineering from University of Waterloo (2013) Ph.D. from University of Toronto's Edward S. Rogers Sr. Department of Electrical and Computer Engineering (2017) Professor Srikantha's research centers on game theory, large-scale optimization, and distributed control to enable adaptive power grid operations. Her work addresses cybersecurity vulnerabilities in smart grids through distributed coordination frameworks and develops transactive energy market mechanisms that incentivize grid support during congestion. By integrating AI breakthroughs, she focuses on creating systems that automatically infer, predict, and respond to power disturbances while enhancing infrastructure resilience against evolving threats. Her publication trajectory reveals consistent innovation in grid security and optimization, with increasing emphasis on AI-driven solutions for grid resilience since 2015. Key thematic evolution shows progression from foundational distributed control (2015-2017) to sophisticated cybersecurity applications (2018-2021), consistently appearing in premier venues like IEEE Transactions on Smart Grid. Her scholarly impact is recognized through: Best paper award at IEEE Smart Grid Communications Runner-up best poster award at ACM Women in Computing As Canada Research Chair holder, she directs a federally funded research program addressing critical grid vulnerabilities, while serving as Associate Editor for IEEE Transactions on Smart Grid. Her work bridges theoretical advances in game-theoretic security with practical grid applications, though specific student mentorship details aren't publicly enumerated in source materials. Her research group operates at the intersection of electrical engineering and computer science, developing next-generation tools for grid operators through collaborations with Canadian energy infrastructure stakeholders.

Allison Jane Ouimetمشاهده پروفایل

دانشیار

• Anxiety Disorders
• Cognitive Behavioural Therapy
• Cognition and Psychopathology
• +۱۲ مورد دیگر

Allison Jane Ouimet is an Associate Professor in the Department of Psychology within the Faculty of Arts and Science at Concordia University. Her research focuses on cognitive mechanisms underlying anxiety disorders and the application of cognitive-behavioral therapy (CBT) to improve treatment outcomes. Education: Ph.D. in Clinical Psychology, Concordia University (2014) M.Sc. in Clinical Psychology, The University of Western Ontario (2007) B.A. (Honours) in Psychology, Concordia University (2004) Her research interests lie at the intersection of cognition and psychopathology, particularly examining attentional bias, dual-systems models of cognition (automatic vs. controlled processing), and emotion regulation in anxiety. She investigates how cognitive biases contribute to the development and maintenance of anxiety disorders and how such insights can refine therapeutic interventions like CBT. The most recent publications reflect a consistent focus on anxiety, emotion regulation, cognitive models, and sexual health. Her work integrates theoretical frameworks such as the Reflective-Impulsive Model with clinical practice, while also exploring applications in youth mental health, phobias, and trichotillomania. There is a strong emphasis on both implicit and explicit cognitive processes across diverse populations. While no scientific awards are listed in the provided text, her publication record in peer-reviewed journals and academic books indicates active scholarly engagement. Allison Ouimet has contributed to research mentoring through co-authorship with junior researchers (denoted by asterisks), suggesting involvement in student supervision, though formal advisees are not explicitly listed. Her work appears to be supported by academic research structures, though specific grants are not mentioned. She has contributed to edited volumes on anxiety and phobias, indicating recognition in her field. She is actively involved in research related to mental health, cognition, and behavior, with thematic clusters in anxiety disorders, emotion regulation, cognitive biases, and sexual health across developmental stages.

John Simpson-Porcoمشاهده پروفایل

استادیار مدعو

• Distributed control systems
• Decentralized control systems
• Optimal control
• +۴ مورد دیگر

John Simpson-Porco is an Adjunct Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He previously worked as a visiting scientist with the Automatic Control Laboratory at ETH Zurich, Switzerland. Education: Ph.D. in Mechanical Engineering (2015, University of California, Santa Barbara), B.Sc. in Engineering Physics (2010, Queen's University, Canada). His research focuses on control and optimization of multi-agent systems and networks, with applications in modernized power grids. Specific interests include distributed control systems, decentralized control systems, optimal control, smart grid technologies, microgrids, and networked frequency/voltage control mechanisms. Scientific Awards: 2012--2014 IFAC Automatica Prize, Center for Control, Dynamical Systems and Computation Best Thesis Award, Outstanding Scholar Fellowship. Prof. Simpson-Porco actively considers graduate student applications and emphasizes the completion of an online application process for admission.

Donald E. Lyonمشاهده پروفایل

استاد

• Automatic Control
• Rotating Machinery
• Signal Processing
• +۲ مورد دیگر

Donald E. Lyon is a Professor in the Department of Mechanical Engineering at the University of New Brunswick (UNB), located in Fredericton. His research focuses on automatic control and rotating machinery, with a particular emphasis on spectral analysis techniques such as FFT limitations, MV spectrum applications, and vibration-based diagnostics for machinery health monitoring. He has authored numerous publications since the 1990s, contributing to advancements in signal processing and machinery vibration analysis. No scientific awards or grants are explicitly mentioned in the text. While no advisees are listed, his work has been presented at conferences like Predictive Maintenance Technology and CMVA Seminars. His office is located in Head Hall E26, and he can be reached at lyon@unb.ca.