Find Academic Supervisors

• Adaptation and learning theories
• Data and network sciences
• Statistical inference
• +7 more

Ali H. Sayed is the Dean of the School of Engineering (Faculté des sciences et techniques de l'ingénieur - STI) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he also directs the Adaptive Systems Laboratory (Laboratoire de systèmes adaptatifs). Previously, he served as an emeritus professor and chair of the Electrical Engineering Department at UCLA. He is a highly cited researcher and a member of the US National Academy of Engineering and the World Academy of Sciences. Sayed served as president of the IEEE Signal Processing Society in 2018 and 2019. Professor Sayed's research focuses on adaptation and learning theories, data and network sciences, statistical inference, multi-agent systems, adaptive networks, and optimization. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and network science. He has made significant contributions to distributed learning algorithms, social learning over networks, and adaptive signal processing techniques that have influenced both academic research and practical implementations. His recent publications demonstrate a strong focus on multi-agent systems, distributed learning, privacy-preserving techniques, and social learning over networks. The research trends show increasing emphasis on federated learning with privacy guarantees, graph-based learning approaches, and the intersection of social dynamics with information processing. His work consistently addresses fundamental theoretical questions while maintaining relevance to practical applications in communication networks, social media analysis, and distributed artificial intelligence systems. Professor Sayed has received numerous prestigious awards throughout his career, including: IEEE Fourier Award (2022) Norbert Wiener Society Award (2020) IEEE Signal Processing Society Education Award (2015) Papoulis Award from the European Association for Signal Processing (2014) Technical Achievement Award from IEEE Signal Processing Society (2012) Terman Award from the American Society for Engineering Education (2005) IEEE Donald G. Fink Prize (1996) Multiple Best Paper Awards from IEEE and EURASIP Sayed has authored or co-authored over 570 publications and six monographs. He has mentored numerous PhD students and researchers in the fields of signal processing and adaptive systems. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Signal Processing (2003-2005) and EURASIP Journal on Advances in Signal Processing (2006-2007), as well as Founding Editor-in-Chief of the Open Access Book Series on Information and Learning Sciences. At EPFL, Professor Sayed leads the Adaptive Systems Laboratory, which focuses on developing theoretical frameworks and practical algorithms for adaptive systems, networked learning, and distributed signal processing. The lab's research encompasses both fundamental theoretical investigations and applications to real-world problems in communications, social networks, and computational biology.

• Probabilistic Verification
• Quantitative Model Checking
• Formal Methods
• +4 more

Marta Kwiatkowska is a Professor of Computing Systems at the University of Oxford and a Fellow of Trinity College. Her research focuses on probabilistic verification , quantitative model checking , and formal methods for complex systems including autonomous robots, medical devices, and biological systems. She leads the development of the PRISM and PRISM-games probabilistic model checkers. Key research areas: Probabilistic systems, formal verification, autonomous robotics, medical device analysis, systems biology Grants: ERC Advanced Grant VERIWARE, EPSRC Programme Grant Mobile Autonomy Awards: 2024 ETAPS Test-of-Time Tool Award for PRISM Students: Current and former advisees in topics spanning formal methods, robotics, and quantitative verification The PRISM-games extension enables verification of stochastic multi-player games with applications in network protocols, autonomous systems, and game theory. Her work bridges theory, algorithms, and practical implementation, with real-world applications in ubiquitous computing and nanotechnology.

• Artificial Intelligence
• Privacy and Data Protection
• Trustworthy AI
• +4 more

Prof. Dr. Pinar Yolum Birbil is a leading researcher in Responsible AI at the Faculty of Science , Utrecht University . Her work bridges Artificial Intelligence , Privacy and Data Protection , and Software Agents , focusing on Human-Centered AI and Trustworthy Systems . She is part of the AI & Data Science and Responsible AI research groups. PhD in Computer Science, North Carolina State University (2003) MS in Computer Science, North Carolina State University (2000) BSc in Computer Engineering, Marmara University (1998) Her research explores Privacy Preservation in collaborative systems, Computational Theory of Mind for human-agent coordination, and Norm-Based AI Systems . Recent projects include the Hybrid Intelligence Center and tools like PANOLA for privacy support. She investigates how AI can balance user autonomy , ethical norms , and societal values in applications ranging from urban planning to healthcare. Her scientific publications (2023-2025) span topics like Explainable Privacy , Trust in Hybrid Teams , and AI for Diabetes Management , appearing in venues such as JAIR , AAMAS , and ACM TOIT . She emphasizes collaborative AI , with contributions to multiagent simulations and privacy-preserving mechanisms . Scientific Awards : NC State University Alumni Hall of Fame (2017) Woman Entrepreneur of the Year (Microsoft Turkey & KAGIDER, 2017) Bogazici University Academic Encouragement Award (multiple years) Best Paper Award at ESAW 2009 She supervises a dynamic research group with PhD students working on Hybrid Intelligence , Computational Ethics , and Privacy Modeling . Her projects often involve interdisciplinary collaboration with institutions like TNO and the Transforming Cities Hub .

• Smart Grids
• Distributed Control Systems
• Renewable Energy Integration
• +6 more

Geert Deconinck is a full professor at KU Leuven , leading the Electrical Energy Systems and Applications (ELECTA) research group within the Department of Electrical Engineering (ESAT). He also serves as scientific leader of the EnergyVille research center's algorithms domain, focusing on smart electrical networks and thermal systems. M.Sc. and Ph.D. from KU Leuven Head of ELECTA since 2012 (10 professors, 8 postdocs, 70+ PhDs) Over 8 million EUR research budget in last 5 years 44 completed PhDs and 10 current advisees IEEE Transactions editorial board member His research spans smart grid architectures , distributed control , and cyber-physical security , with recent focus on EV-grid integration , renewable energy democratization , and multi-carrier energy systems . Current projects include: Smart Charging - E-Mobility meets Renewable Energy Early Detection and Defense Systems for Smart Grids Open-source P2P energy sharing platforms Microgrid control strategies for PV-battery systems Awarded IET Fellow and IEEE Senior Member status, his work combines machine learning with power systems engineering through both theoretical modeling and experimental validation . He has contributed over 575 publications with 9800+ Google Scholar citations.

Qingguo LiView profile

Professor

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +4 more

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

• Artificial Intelligence
• Computer Security
• Multi-Agent Systems
• +3 more

Dr. Chunyan Mu serves as a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen, actively contributing to both academic instruction and cutting-edge research in computing science while currently accepting new PhD students. Her research program centers on Trustworthy AI and Safe Autonomy, with specialized expertise in formal verification of responsibility, accountability, and privacy mechanisms within multi-agent systems. She investigates resilience frameworks for autonomous intelligent systems and develops advanced methodologies for information flow security analysis, bridging theoretical computer science with practical security implementations. Analysis of her publication trajectory (2014-2025) reveals consistent innovation in applying formal methods to security-critical systems. Key thematic developments include probabilistic strategy logic for observability analysis, quantitative verification of opacity properties, and game-theoretic approaches to security verification, demonstrating increasing sophistication in handling multi-agent accountability and system resilience challenges. Dr. Mu currently supervises PhD candidates and offers a fully funded doctoral position focused on formal verification of safety properties in autonomous systems, providing comprehensive financial support including tuition coverage, £20,780 annual stipend, and dedicated research funding for candidates with strong backgrounds in formal methods and artificial intelligence.

Ning NanView profile

Associate Professor

• Digital business strategy
• Evolvable IT infrastructure
• Online community
• +3 more

Ning Nan is an Associate Professor at the University of British Columbia's Sauder School of Business, specifically within the Department of Accounting and Information Systems. With a BA from Peking University, an MA from the University of Minnesota, and a PhD from the University of Michigan, Dr. Nan focuses on digital business strategy, evolvable IT infrastructure, and complex adaptive systems. His research explores blockchain applications, agent-based modeling, and the intersection of AI with societal challenges like sustainability. Education: PhD in Information Systems, University of Michigan MA in Information Systems, University of Minnesota BA in Computer Science, Peking University Research Interests: Dr. Nan investigates how technology shapes organizational and societal systems, with a focus on AI ethics, sustainability through personalized recommendations, and blockchain's role in decentralized supply chains. He employs agent-based modeling to simulate complex phenomena like innovation diffusion and online community dynamics. Recent work addresses explainable AI in smart cities and carbon-reduction gamification strategies. Teaching: In 2024-2025, he teaches Data Management for Business Analytics , emphasizing practical applications of data-driven decision-making. Key Research Themes: His articles highlight trends in AI sustainability (e.g., carbon footprint impacts of recommendation systems), digital business strategy (blockchain governance, app update strategies), and foundational IS theory (hyperturbulence and IS strategy).

• Artificial Intelligence for Power Systems
• Smart Grids
• Energy Management
• +4 more

Zita Vale is a Full Professor at the Institute of Engineering (ISEP) of the Polytechnic of Porto (IPP), where she holds the first Full Professor position since 2017. She is a co-founder of GECAD (1999) and coordinates GECAD's Power and Energy (PES) activities. GECAD is recognized by FCT since 2004 and classified as Excellent. She has served as GECAD director (2010-2017), vice-director (1999-2009, 2017-present), and is a member of the administration board. She is also co-founder and member of the coordination board of the National Associated Laboratory on Intelligent Systems. Her educational background includes a PhD (1993) and Agregação/Habilitation (2003) in Electrical and Computer Engineering from the University of Porto. She began her academic career at the University of Porto as a Teaching Monitor (1985), Assistant (1985-1993), and Professor (1993-1998) before moving to ISEP in 1998. Zita Vale's research focuses on the design and development of artificial intelligence-based models for Power and Energy Systems. Her work spans knowledge-based systems, multiagent systems, machine learning, metaheuristics, and semantics, with applications in smart grids, energy management, electricity markets, and renewable energy integration. She has an extensive international network and has participated in 80 R&D projects, raising over 22 million Euros for GECAD. Her recent publications demonstrate a strong emphasis on optimization techniques, explainable AI, energy storage systems, and the integration of distributed energy resources in power systems. She serves as Editor-in-Chief of Applied Energy (Elsevier), a leading journal in the field with an Impact Factor of 11.2. Her citation metrics are impressive, with over 17,000 citations on Google Scholar and an H-index of 64. Editor-in-Chief of Applied Energy (Elsevier) Over 17,000 citations on Google Scholar H-index of 64 Zita Vale has supervised 29 PhD students (25 completed) and 72 MSc students (66 completed), demonstrating her strong commitment to academic mentorship. She has also been involved in numerous international and national evaluation processes, including project proposals, faculty positions, and PhD juries across 15+ countries. She has contributed to over 225 evaluation processes from 2018-2023, including 150+ project proposals/execution, 50+ Faculty/Researchers positions, 2 Habilitation juries, and 25+ PhD juries. She leads GECAD's involvement in several major research initiatives, including the National Associated Laboratory on Intelligent Systems and various European projects such as IoTalentum, TRADERES, DOMINOES, and EcoRural-IoT from Horizon 2020, as well as PRODUTECH EU DIH from Horizon Europe. Her leadership extends to international organizations where she serves as President of Intelligent Systems Applications in Power (ISAP) and Technical Committee Program Chair of IEEE PES Analytic Methods for Power Systems Committee.

Christoforos MavrogiannisView profile

Assistant Professor

• Human-robot interaction
• shared autonomy
• multiagent and multirobot systems
• +4 more

Christoforos Mavrogiannis is an Assistant Professor of Robotics at the University of Michigan, leading the Fluent Robotics Lab within the Department of Robotics. He holds a Ph.D. and M.S. from Cornell University and a Diploma in Mechanical Engineering from the National Technical University of Athens. His research focuses on enabling robots to seamlessly integrate into dynamic, unstructured environments through advancements in human-robot interaction, shared autonomy, multiagent systems, and navigation algorithms. Education: Ph.D. and M.S., Cornell University Diploma in Mechanical Engineering, National Technical University of Athens Research Interests: His work spans human-robot interaction , shared autonomy , and multiagent systems . He develops algorithms for navigating dynamic environments , decentralized control , and behavior prediction , with applications in robotic manipulation and collaborative transport . Recent projects include the HOUND off-road robot and pixel-art generation with mobile robots. Awards: Best paper award at the RSS Social Navigation Workshop (2024) Grants & Advising: He advises the Fluent Robotics Lab and contributes to organizing conferences like ICRA and HRI. His lab focuses on human-centered robotics and socially competent navigation . Labs & Teams: Fluent Robotics Lab at the University of Michigan, collaborating on projects like the HOUND platform and electrostatic brake systems for manipulation.

Yu XiaoView profile

Associate Professor

• Edge Computing
• Extended Reality (XR)
• Wearable Computing
• +7 more

Yu Xiao is an Associate Professor at the Department of Information and Communications Engineering, Aalto University, specializing in edge computing, extended reality (XR), wearable computing, and crowdsensing. Their research contributes to the UN Sustainable Development Goals, particularly in education and technology innovation. Active in mobile cloud computing and decentralized systems Principal Investigator in EU-funded projects (EMIL, TUTL) Expert in 5G networks, autonomous systems, and human activity recognition Yu Xiao's work spans interdisciplinary domains, including healthcare (cardiovascular resuscitation devices) and urban mobility (autonomous vehicle interactions). They have received multiple awards, including Best Paper Awards and Nokia Foundation Scholarships. Focus on low-latency communication and multiagent reinforcement learning Developed frameworks like FediLive for decentralized social networks Contributed to 128+ publications and software tools Recent collaborations include institutions like Pontificia Universidad Católica de Chile and participation in IEEE committees. Their research integrates blockchain for secure IoT communication and advanced AR applications.

Qing ChangView profile

Professor

• Smart Manufacturing
• Cyber-Physical Systems
• Real-time Production Control
• +5 more

Qing (Cindy) Chang is a Professor in the Department of Mechanical Engineering at the University of Virginia. Her research focuses on cyber-physical systems for smart manufacturing, real-time production control, and human-robot collaboration. Prior to academia, she worked at General Motors, earning three Boss Kettering Awards for innovation. She holds an M.S. from the University of Wisconsin-Madison and a Ph.D. in Manufacturing from the University of Michigan. Education: M.S. in Mechanical Engineering, University of Wisconsin - Madison Ph.D. in Manufacturing, University of Michigan – Ann Arbor Research Interests: Cyber-Physical Systems for Smart Manufacturing Real-time Production Control Knowledge-guided Machine Learning-based Control Human-Robot Collaboration in Industrial Settings Intelligent Maintenance and Energy Management Awards: 20 most influential professors in smart manufacturing (2020) NSF CAREER Award (2014) General Motors Boss Kettering Awards (2005, 2006, 2008) GM R&D Charles L. McCuen Special Achievement Awards (2005, 2006, 2008) Leadership & Grants: She serves on the board of NAMRI/SME and holds editorial roles in ASME, IEEE, and SME journals. Her work bridges AI, robotics, and manufacturing systems, with notable grants including the NSF CAREER Award. Labs & Teams: Her Intelligent Systems Lab develops AI-driven solutions for manufacturing efficiency and sustainability, focusing on energy management, predictive analytics, and human-robot collaboration.

Tim BaarslagView profile

Professor

• Automated Negotiation
• Multi-Agent Systems
• Artificial Intelligence
• +4 more

Tim Baarslag is a Senior Researcher and group leader of the Intelligent and Autonomous Systems group at CWI (The Dutch research institute for Mathematics and Computer Science). He holds the title of Professor of Mathematics of Cooperative AI at Eindhoven University of Technology and serves as a Visiting Associate Professor at Nagoya University of Technology, Visiting Fellow at the University of Southampton, and Visiting Scholar at MIT. His research focuses on automated negotiation systems for collaborative decision-making in smart energy trading, IoT, autonomous vehicles, and digital privacy. Education : MSc (cum laude) and BSc (cum laude) from Utrecht University; PhD (cum laude) from Delft University of Technology Tim pioneered the COMBINE project (NWO Vidi grant) for coordinating multi-deal negotiations and developed the widely-used Genius negotiation environment. His work appears in prestigious venues like Science Magazine , Artificial Intelligence , and MIT Technology Review . He also leads the International Automated Negotiating Agent Competition and contributes to policy through memberships in The Young Academy and Netherlands Academy of Engineering . Recent research trends emphasize multi-deal negotiation protocols (2024), preference uncertainty modeling in privacy negotiations (2022), and scalable algorithms for handling outcome spaces as large as 10²⁵⁰ possibilities. His 2023 work on search algorithms for large negotiation domains has applications in energy trading and supply chain management. Scientific Awards : Cor Baayen Young Researcher Award (2017), Springer Theses Award (2016), multiple Best Paper Awards (AAMAS 2022, WI-IAT 2015, IJCAI 2014), and recognitions as Science Talent (2018), Academic Pioneer (2020), and Young Talent (2019) As a grant recipient , Tim leads NWO Vidi project COMBINE and previously held a Veni grant for preference uncertainty research. He mentors through organizing competitions, serving on conference PCs (AAAI, IJCAI), and reviewing in top journals like Artificial Intelligence . His work bridges theory and practice through the Genius framework and real-world implementations in smart grid and vehicular platooning.

• Signal Processing
• Communications
• Bioimaging
• +5 more

Amir Asif is a Professor at the Lassonde School of Engineering, York University, and concurrently serves as Vice President, Research and Innovation. His academic leadership roles include Dean of the Gina Cody School of Engineering and Computer Science at Concordia University (2014-2020). He specializes in signal processing, communications, and their applications in healthcare, power grids, and distributed systems. Asif holds a PhD from Carnegie Mellon University and a Harvard certification in executive leadership. Education: PhD, Electrical and Computer Engineering, Carnegie Mellon University (1996) MS, Electrical and Computer Engineering, Carnegie Mellon University (1993) BSc, University of Engineering and Technology Lahore (1990) Harvard Certificate in Leadership for Senior Executives (2018) Research Interests: Asif’s work spans signal processing for medical imaging (e.g., ultrasound elastography), smart grid optimization, and cybersecurity in power systems. His recent publications address hydrogen energy systems, EMG-based gesture recognition, and resilient control frameworks against cyberattacks. Grants & Leadership: He leads NSERC-funded projects on federated learning and resilient algorithms. He chairs the Ontario Council of University Research and serves on TRIUMF Innovations and the Richmond Hill Board of Trade. His grants include SSHRC funding for equity initiatives and NSERC support for distributed signal processing. Teaching & Mentorship: Asif has supervised over a dozen graduate students and taught courses like Digital Communications and Statistical Signal Processing Theory. Notable advisees include Arash Mohammadi (PhD, 2014) and Nick Sajadi (PhD, 2017).

Kevin Leyton-BrownView profile

Professor

• Artificial Intelligence
• Machine Learning
• Game Theory
• +5 more

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.

• Security and Privacy
• Active Cyber Defense
• Security Analytics
• +6 more

J. Haadi Jafarian is an Assistant Professor in the Department of Computer Science and Engineering at the University of Colorado Denver, where he leads the Active Cyber and Infrastructure Defense (ACID) Lab. He earned his Ph.D. from the University of North Carolina Charlotte in 2017. Research Interests: Active Cyber Defense (Moving Target Defense, Cyber Deception) Big Data Analytics for Cyber Threat Intelligence Security for Cyber-Physical Systems & Critical Infrastructures Cyber Resilience and Automation Recent Publications highlight innovations in traffic obfuscation, adversarial machine learning, and deception-based threat detection. His work spans network security, cybersecurity analytics, and scalable defense frameworks. Teaching includes: CSCI 4743/5743: Cyber and Infrastructure Defense (Fall 2023) CSCI 4742/5742: Cyber Programming and Analysis (Spring 2023) CSCI 4741: Cybersecurity Principles (Spring 2022) CSCI 4800: Web Application Development (Spring 2021) CSCI 3761: Computer Networks (Spring 2020) Labs & Teams: The ACID Lab focuses on developing proactive cyber defense strategies, including moving target defense, deception techniques, and security analytics for critical infrastructure.