Find Academic Supervisors

• Electric Machines and Drives
• Power Network Simulation and Control
• Power Network Dynamics and Stability
• +7 more

Louis-A. Dessaint is a Professor at the Département de génie électrique at École de technologie supérieure (ÉTS). He holds B.Ing., M.Sc.A., and Ph.D. degrees from Polytechnique Montréal. His research focuses on power electronics, renewable energy integration, and smart grid technologies, with affiliations to the GREPCI research group. He has supervised numerous theses and projects, addressing topics like microgrid optimization, energy storage, and voltage stability. Education: B.Ing., M.Sc.A., Ph.D. (Polytechnique Montréal). Research interests include electric machines, power network dynamics, hybrid energy systems, and sustainable development. His recent work emphasizes real-time control of power systems, renewable energy integration, and smart building energy management. Publications highlight advancements in microgrid topology optimization, battery storage systems, and ADRC-based control strategies. Awards include IEEE Fellow (2013) and membership in the Canadian Academy of Engineering (2012). He has advised over 50 students and contributed to projects on smart grids, energy storage, and grid stability. His lab, GREPCI, focuses on power electronics and industrial control.

Anastasios P. VassilopoulosView profile

Adjunct Professor

• Composite Materials
• Offshore Renewables
• Wind Turbine Technology
• +5 more

Anastasios P. Vassilopoulos is an Adjunct Professor and Head of the Composite Mechanics Group (GR-MEC) at the École Polytechnique Fédérale de Lausanne (EPFL) within the School of Architecture, Civil and Environmental Engineering (ENAC). He holds additional roles as Director of the Doctoral Program in Civil and Environmental Engineering (EDCE) and is affiliated with multiple research groups and committees at EPFL. His expertise spans composite materials, structural adhesives, and offshore renewable energy systems. Education: PhD in Mechanical Engineering, University of Patras, Greece (2001) Dipl. Mechanical Engineer, University of Patras, Greece (1995) Erasmus Exchange, University of Bristol, UK (1994–1995) Research Interests: Offshore renewables, particularly wind turbine blade materials and joints Fatigue analysis of composite materials and adhesive joints Experimental and analytical methods for composite structures Design of composite constructions and structural integrity His work emphasizes practical applications in engineering, including wind energy systems and fiber-reinforced polymer (FRP) composites. Teaching & Labs: Teaches courses on structural mechanics, advanced composites, and floating offshore renewables Leads the GR-MEC lab, focusing on composite mechanics and joint behavior Supervised over 15 PhD students at EPFL Grants & Projects: Swiss National Science Foundation-funded projects on bonded composite structures and wind turbine blade materials Research on fire-resistant composite bridge decks and lightning effects on composite structures

Asu OzdaglarView profile

Professor

• Optimization
• Machine Learning
• Game Theory
• +4 more

Asu Ozdaglar is the EECS Department Head and MathWorks Professor at MIT, serving as Deputy Dean of Academics in the MIT Schwarzman College of Computing. Her research bridges optimization theory, machine learning, and network science with societal implications, focusing on AI ethics, data-driven decision systems, and strategic interactions in networked environments. Her technical contributions include foundational work on large-scale optimization algorithms (e.g., distributed methods, first-order methods), game-theoretic models for network systems, and federated learning frameworks. Recent work addresses critical societal challenges like misinformation dynamics, data market inefficiencies, and algorithmic fairness in AI systems. Publications from 2023-2025 highlight advancements in graphon-based network game analysis, privacy-preserving data mechanisms, and multi-agent learning dynamics. She co-leads initiatives in MIT's AI+D program, emphasizing interdisciplinary education and ethical AI development. Notable institutional roles include oversight of MIT's computing education strategy and contributions to pandemic-related research on infection control through testing optimization. Her work integrates technical rigor with policy-relevant insights, influencing both academic and real-world systems.

H. Jane BaeView profile

Assistant Professor

• Turbulence Modeling
• Computational Fluid Dynamics
• Fluid Mechanics
• +3 more

H. Jane Bae is an Assistant Professor of Aerospace at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. Her research focuses on turbulence modeling, particularly developing high-fidelity computational methods to simulate high-Reynolds-number flows for applications in aircraft design, wind farms, and atmospheric predictions. She integrates machine learning, information theory, and numerical techniques to enhance turbulence modeling efficiency. Education: B.S. in Aerospace Engineering from Caltech (2011), Ph.D. in Mechanical Engineering from Stanford University (2018). She joined Caltech in 2021. Research interests include near-wall turbulence dynamics, resolvent analysis, sparse identification of nonlinear dynamics, and reinforcement learning for wall models in LES. Her work addresses computational cost reduction and model accuracy in complex flow simulations. Awards: 2023 Outstanding Referee Award from Physical Review. Teaching includes courses on fluid mechanics (Ae/APh/CE/ME 101 abc) and turbulence (Ae 239 ab). Her lab combines turbulence theory, high-performance computing, and data-driven methods to study unsteady flows over complex surfaces. Notable contributions include machine learning-based wall models and resolvent analysis frameworks for non-stationary flows.

David ParkerView profile

Professor

• Formal Verification
• Probabilistic Model Checking
• Quantitative Analysis
• +2 more

David Parker is Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Trinity College. His research focuses on formal verification methods for checking system correctness, particularly quantitative verification techniques for probabilistic systems. As leader of the PRISM and PRISM-games projects, he develops tools for analyzing safety, reliability, and performance properties in complex systems. Current research explores verification of AI systems, robust decision-making under uncertainty, and multi-agent systems using stochastic games. His work bridges theoretical foundations with applications in autonomous systems, robotics, and healthcare technology. Recent publications demonstrate advancements in probabilistic temporal logic, robust policy learning, and bisimulation techniques for Markov models. These works consistently emphasize formal guarantees for safety-critical applications and novel approaches to model checking. ETAPS Test-of-Time Tool Award (2024) HVC Award (2016) Professor Parker mentors PhD students in verification, control synthesis, and AI safety, with research funded by ERC, EPSRC, and industrial partners. He serves on editorial boards for Formal Aspects of Computing and ACM Transactions on Autonomous Systems.

Hatem Abou-ZeidView profile

Assistant Professor

• AI for 6G
• Wireless Sensing
• Extended Reality Networking
• +5 more

Dr. Hatem Abou-Zeid is an Assistant Professor in the Department of Electrical and Software Engineering at the University of Calgary, directing the WAVES Research Group. He holds adjunct positions at Queen’s University, Carleton University, and Ontario Tech University. His research focuses on AI-driven 6G networks, wireless sensing, extended reality (XR), and brain-computer interfaces (BCI). Prior to academia, he spent 7 years in industry at Ericsson and Cisco, leading projects in 5G radio access and network intelligence, resulting in 20+ patents and $3.5M+ collaborative research projects. Education: PhD in Electrical and Computer Engineering (Queen’s University), M.Sc. and B.Sc. in Electronics and Communications Engineering (Arab Academy, Egypt). Research interests span trustworthy AI for 6G, joint sensing & communication systems, and AI for immersive networking. Notable work includes foundational models for 6G radios, self-supervised learning for spectrograms, and safe reinforcement learning for network slicing. His lab explores pediatric BCI, IoT edge computing, and low-power wireless prototyping. Publications highlight contributions to AI-driven resource allocation, 5G/6G slicing, and federated learning in tactical networks. Awards include the 2023 Early Research Excellence Award and 2023 Software Engineering Professor of the Year. Advising involves 15+ students and postdocs, with collaborations spanning academia (e.g., Hotchkiss Brain Institute) and industry (Ericsson, Telus). Current openings exist for PDF/PhD researchers in AI/6G wireless systems and pediatric BCI. Labs/Teams: Director of WAVES Lab; collaborates with Ericsson Canada, Alberta Innovates, and Canadian Space Agency on applied research projects. Teaching includes advanced networking, machine learning systems, and IoT courses.

• Data Science
• Machine Learning
• Harmonic Analysis
• +7 more

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Vijay K. ShahView profile

Assistant Professor

• Networking
• Communications
• Controls
• +7 more

Vijay K. Shah is an Assistant Professor in the Electrical and Computer Engineering Department at North Carolina State University, leading the NextG Wireless Lab. His research focuses on advancing wireless communication and network technologies for beyond 5G/6G systems, including O-RAN architecture, spectrum management, and AI-driven network optimization. Education: Ph.D. in Computer Science, University of Kentucky (2019) Bachelor's in Computer Science and Engineering, National Institute of Technology, Durgapur (2013) Research emphasizes open radio access networks (O-RAN), mmWave testbeds, and cross-layer optimization. Recent work highlights include ORAN-Bench-13K (LLM benchmarking), ZT-RIC (zero-trust security frameworks), and Milli-O-RAN (reconfigurable mmWave networks). His contributions span O-RAN applications (xApps/rApps), satellite-terrestrial coexistence, and AI-driven positioning systems. Experimental validations include 3GPP-compliant 5G positioning and adversarial attack defenses. Publications reflect expertise in O-RAN architecture evolution, spectrum policy tools (ASCENT), and UAV-based network coordination (GLIDE). Current projects explore LEO satellite constellations and resilient disaster response networks. Labs/Teams: Head of the NextG Wireless Lab at NC State, focusing on prototype development in O-RAN, 6G, and secure AI-driven networks.

Jinhan KimView profile

Researcher

• Software Engineering
• Mutation Testing
• Fault Localization
• +4 more

Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.

Rajendran JeyavijayanView profile

Associate Professor

• Hardware Security
• Computer Security
• Hardware Fuzzing
• +4 more

Jeyavijayan 'JV' Rajendran is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He is an ASCEND Fellow and leads the Secure and Trustworthy Hardware (SETH) Lab. His research focuses on hardware security, computer security, and novel applications of AI in secure hardware design. Education: PhD in Electrical Engineering (NYU 2015), MS in Computer Engineering (NYU Tandon 2010), BE in Electronics and Communication Engineering (Anna University 2008). Research Interests: Hardware Security, Computer Security, Logic Locking, Hardware IP Protection, and Reinforcement Learning for Security. He explores AI-driven approaches to detect vulnerabilities, protect intellectual property, and enhance secure hardware design through fuzzing, obfuscation, and formal verification. Notable Awards: 2022 Office of Naval Research Young Investigator Award, 2021 IEEE CEDA Ernest Kuh Early Career Award, 2017 NSF CAREER Award. Lab and Teams: The SETH Lab focuses on trustworthy hardware design, developing techniques to secure integrated circuits against reverse engineering and IP theft. Current projects include LLM-based hardware code generation, formal approaches for hardware fuzzing, and AI-driven vulnerability detection.

Zixiang XiongView profile

Professor

• Machine Learning
• Image/Video Processing
• Network Information Theory
• +7 more

Zixiang Xiong is a Professor and Associate Department Head in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Robert M. Kennedy '26 Endowed Professorship II. He earned his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1996. His career includes roles at Princeton University, University of Hawaii, and Texas A&M since 1999. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign, 1996 Visiting Research Associate, Princeton University, 1995–1997 University of Hawaii, 1997–1999 Research Interests: Focuses on machine learning, image/video processing, federated learning, network information theory, biomedical engineering, and communications. His work spans distributed source coding, genomic signal processing, and energy-efficient systems. Publications & Awards: Over 200 publications, including seminal works on distributed video coding and network information theory. Notable awards include the NSF Career Award (1999), ONR Young Investigator Award (2001), IEEE Fellow (2006), and the ECE Outstanding Faculty Award (2024). His research has led to patents in video compression and multimedia systems. Grants & Advising: Active in NSF-funded projects on coding theory and energy-delay tradeoffs. Advises numerous PhD and MS students, with over 50 alumni in academia and industry. Collaborates on biomedical imaging, remote sensing, and federated learning initiatives. Labs & Teams: Leads a dynamic research group at Texas A&M, focusing on cutting-edge projects in signal processing and machine learning applications. Collaborates with industry and governmental agencies on applied research.

Felix MotzoiView profile

Associate Professor

• Quantum Technologies
• Optimal Control Theory
• Machine Learning
• +6 more

Prof. Felix Motzoi is an Associate Professor at the University of Cologne and Division Leader & Head of the 'Automatic Optimization, Control and Design' group at the Peter Grünberg Institute (PGI-8) in Jülich. His research focuses on advancing quantum technologies, including superconducting and semiconducting architectures, trapped cold atoms/ions, Rydberg qubits, and long-range entanglement. He leads theoretical efforts in quantum control theory, machine learning applications, hardware co-design, and error mitigation strategies. Key research areas include developing optimal control methodologies (e.g., DRAG, STA), numerical optimization, and dynamics modeling for quantum systems. His work bridges theoretical frameworks with experimental implementations, emphasizing practical solutions for scalable quantum computing. Recent publications highlight innovations in quantum gate design, error suppression via pulse shaping, and hybrid optimization techniques combining machine learning with physics-driven approaches. His team collaborates across disciplines to address challenges in qubit coherence, entanglement stabilization, and robust quantum processing.

Tijani CHAHEDView profile

Professor

• Network Optimization
• Edge Computing
• Machine Learning in Networks
• +4 more

Prof. Tijani CHAHED is a Professor at Telecom SudParis, part of Université Paris-Saclay, affiliated with the SAMOVAR laboratory and the NeSS research group. His work focuses on network optimization, edge computing, machine learning applications in telecommunications, and game-theoretical frameworks for distributed systems. He holds a position in the Department of Computer Science and Telecommunications. His research spans resource allocation in 5G/6G networks, energy efficiency strategies for mobile infrastructure, reinforcement learning for dynamic systems, and coalitional game theory for multi-agent systems. Key contributions include optimization of cache allocation in edge computing, latency-critical traffic management (URLLC), and strategic investment models for distributed computing infrastructures. Selected articles highlight advances in edge computing resource management, metaverse data transport over 5G, and energy-efficient sleep mode control for base stations. His work often intersects with industrial applications in green networks and smart grid integration for mobile infrastructure. Collaborations involve institutions like École Polytechnique, INRIA, and industry partners in telecommunications. Current projects include 6G network architectures, metaverse-enabled edge services, and decentralized resource allocation frameworks. Labs/Teams: SAMOVAR Lab (Signal and Media Access Networks, Optical and Radio Networks), NeSS Group (Networked Systems and Services).

Riyadh BaghdadiView profile

Assistant Professor

• Compilers
• Programming Languages
• Deep Learning
• +9 more

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Kiyohito IigayaView profile

Assistant Professor

• Computational Psychiatry
• Neuroscience
• Machine Learning
• +7 more

Kiyohito Iigaya is an Assistant Professor at the Department of Psychiatry at Columbia University, affiliated with the Neurobiology and Behavior Program, Center for Theoretical Neuroscience, and Mortimer B. Zuckerman Mind Brain Behavior Institute. He is also connected to the Health Analytics, Computing Systems for Data-Driven Science, and Computational Social Science centers. Ph.D. in Physics, Columbia University (advisor: Stefano Fusi) Postdoctoral training at University College London (Gatsby Computational Neuroscience Unit, Max Planck Centre for Computational Psychiatry and Ageing) and Caltech (advisor: John O’Doherty) The Iigaya lab specializes in computational psychiatry, studying computational principles of learning and decision-making in mental disorders. They integrate reinforcement learning models, neural circuit models, and empirical research (e.g., fMRI) through collaborations with clinical and basic neuroscience labs. Kyo’s research bridges algorithmic and neural implementation levels of analysis, focusing on mental health applications. His work aligns with data-driven science and computational social science. Key affiliations include: Neurobiology and Behavior Program Center for Theoretical Neuroscience Mortimer B. Zuckerman Mind Brain Behavior Institute Health Analytics Computing Systems for Data-Driven Science Computational Social Science His academic journey combines physics, theoretical neuroscience, and translational psychiatry research. The lab’s website ( iigayalab.com ) provides further details.