Find Academic Supervisors

Amanda ProrokView profile

Professor

• Multi-Agent Systems
• Robotics
• Collective Intelligence
• +6 more

Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.

• Artificial Intelligence
• Electronic Commerce
• Intelligent Agents and Multi-Agent Systems
• +6 more

Thomas Tran is a Full Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Computer Science from the University of Waterloo (2004) and a B.Sc. (Double Major in Mathematics and Computer Science) from Brandon University (1999). His research focuses on Artificial Intelligence, Electronic Commerce, Multi-Agent Systems, Trust and Reputation Modeling, and Recommender Systems. He has published over 80 refereed papers and supervised 27 graduate students (4 PhDs and 23 Masters). Education: Ph.D. in Computer Science, University of Waterloo (2004) B.Sc. (Double Major in Mathematics and Computer Science), Brandon University (1999) Research Interests: AI Applications in E-Commerce and Mobile Business Trust Establishment Models in Multi-Agent Systems Recommender Systems and Deep Learning Clinical Data Analysis for Hidradenitis Suppurativa Awards: Governor General's Gold Medal (2004) NSERC Postgraduate Scholarships (PGS A/B) AAAI Doctoral Consortium Participant (2002) Advising and Grants: Supervised 27 graduate students Recipient of multiple research grants (details unspecified) Labs/Teams: Active in AI and E-Commerce research groups within EECS.

Ashish CherukuriView profile

Associate Professor

• Optimization
• Control Systems
• Multi-agent Systems
• +7 more

Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.

• Control Theory
• Digital Signal Processing
• Adaptive Signal Processing and Control
• +7 more

Dr. Allahyar Montazeri is a Senior Lecturer in Control and Electronics Engineering at Lancaster University's School of Engineering, specializing in advanced control systems and signal processing. His research focuses on adaptive signal processing, robust control, system identification, and applications in robotics, active noise/vibration control, and wave energy conversion. He has over 110 publications and serves on editorial boards such as Frontiers in Robotics and AI, and IFAC Technical Committees. Montazeri holds a Humboldt Research Fellowship (2011) and ERCIM Fellowship (2010). His industrial collaborations include Bosch for automotive noise control and Fraunhofer Institute. He has supervised PhD students in acoustic signal processing and leads projects on autonomous robotics and environmental monitoring. Notable awards include 'Outstanding Associate Editor' (2023) and 'Fellow of The Higher Education Academy.' He actively participates in conferences like IEEE CDC and chairs sessions on mechatronics systems. His work bridges theoretical control advancements with practical applications in extreme environments, including nuclear robotics and underwater systems.

Dilian GurovView profile

Professor

• Software Specification and Verification
• Multi-Agent Strategic Planning
• Formal Methods
• +3 more

Dilian Gurov is a Professor in Computer Science at KTH Royal Institute of Technology, associated with the Digital Futures Faculty and the Division of Theoretical Computer Science. He also coordinates the Doctoral Programme in Computer Science at the CSC school. Before joining KTH in 2002, he earned a Ph.D. from the University of Victoria, Canada (1998), and worked at the Swedish Institute of Computer Science (1997-2002). His research focuses on software specification and verification, including contracts, program models, logics, and tools, as well as multi-agent strategic planning involving knowledge-based strategies in imperfect information settings. Key contributions include the CAV Distinguished Paper Award 2023 for 'Automatic Program Instrumentation for Automatic Verification' and an EASST award for 'Checking Absence of Illicit Applet Interactions: A Case Study' (2004). He leads projects funded by VR (SEFROS, ContraST) and Vinnova (AVerT2) and collaborates with industries like Scania on formal verification of C programs. His service roles span over 30 conference committees and organization roles, including PC memberships for iFM, TAP, and ISoLA. Teaching responsibilities include courses such as 'Formal Methods,' 'Program Semantics and Analysis,' and 'Knowledge in Games with Imperfect Information.' His work emphasizes practical applications of formal methods, bridging academic research with industry needs through collaborations and tool development (e.g., CVPP, ProMoVer, TriCo).

Liz RatnamView profile

Associate Professor

• Control of Power Systems
• Optimization of Energy Systems
• Secure and Resilient Power Grids
• +3 more

Associate Professor Liz Ratnam is a leading academic in the Department of Electrical and Computer Systems Engineering at Monash University, serving as Deputy Postgraduate Director of Education ECSE. Her expertise spans power systems control, energy optimization, and resilient grid design. She holds a BEng (Hons I) and PhD in Electrical Engineering from the University of Newcastle (2006 and 2016), with postdoctoral research at UC San Diego and Berkeley. She previously served as Senior Lecturer and Sub-Dean for Educational Programs at ANU's College of Engineering & Computer Science, supported by a Future Engineering Research Leader (FERL) Fellowship. Her current research focuses on advancing transactive energy markets, EV coordination in distribution networks, and secure grid operations. She leads major grants including the National Facility for Electricity Grid Security (ARC LIEF 2023) and projects on EV fast-charging infrastructure. Education Background: - BEng (Hons I) in Electrical Engineering, University of Newcastle (2006) - PhD in Electrical Engineering, University of Newcastle (2016) Research Interests: Resilient, carbon-neutral power grid design Control and optimization of energy systems Synchrophasor-based estimation for grid stability Transactive multi-agent systems for energy markets Electric vehicle integration and coordination in unbalanced grids Grants & Projects: ARC Linkage Infrastructure Grant LE23010058 (2023): National Facility for Electricity Grid Security ARC Discovery Project DP22010135 (2022): Neural Architecture Search for Deep Learning ARC Linkage Project LP21200473 (2022): Building Australia's EV Fast-Charging Infrastructure Awards & Memberships: Fellow of Engineers Australia Senior Member of IEEE FERL Fellowship (ANU)

Aritra MitraView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Control Theory
• +6 more

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Peter ChinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +8 more

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Guanrui LiView profile

Assistant Professor

• Aerial Robotics
• Control Systems
• Robot Perception
• +3 more

Guanrui Li is an Assistant Professor at Worcester Polytechnic Institute's Robotics Engineering Department and the director of the Aerial-robot Control and Perception Lab (ACP Lab). He holds a Ph.D. in Electrical and Computer Engineering from NYU (2024), an M.S. in Robotics from the University of Pennsylvania (2018), and a B.E. in Theoretical and Applied Mechanics from Sun Yat-sen University (2016). His research focuses on aerial robotics, including control methodologies for collaborative transportation, human-robot interaction, and perception-aware systems. Key contributions include Hybrid Perception-Aware MPC frameworks, cooperative manipulation algorithms, and simulation tools like RotorTM. Education: Ph.D., Electrical and Computer Engineering, NYU (2024) M.S., Robotics, University of Pennsylvania (2018) B.E., Theoretical and Applied Mechanics, Sun Yat-sen University (2016) Research Interests: Control and perception of aerial robots, human-robot collaboration, cooperative manipulation, and autonomous systems. Notable work addresses challenges in payload transportation, sensor fusion, and safety-critical navigation. Awards: NSF CPS Rising Stars (2023) Outstanding Deployed System Paper Finalist (IEEE ICRA 2022) NYU Dante Youla Award (2022) NYU Outstanding Dissertation Award (2024) Lab & Team: ACP Lab focuses on advancing aerial robotics through interdisciplinary research. Current projects include mixed reality interfaces for human-robot interaction and fault-tolerant control systems. Recent collaborations include workshops on Rust for Robotics (ICRA 2025) and embodied-AI for aerial systems (ICUAS 2025).

Lacra PavelView profile

Professor

• Game Theory
• Control Theory
• Optimization
• +7 more

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.

Changhyun ChoiView profile

Assistant Professor

• Visual perception for robotic manipulation
• Deep learning for object grasping
• Assembly manipulation
• +8 more

Changhyun Choi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Minnesota (UofM), Twin Cities. His research focuses on visual perception for robotic manipulation using deep learning. Assistant Professor, UofM Electrical and Computer Engineering (2018–present) Postdoctoral Associate, MIT CSAIL (prior to 2018) Research Interests : Visual perception for robotic manipulation Deep learning for object grasping and assembly Soft manipulation techniques Object pose estimation and tracking Active perception and reinforcement learning Combining vision with manipulation Scientific Awards : NSF CAREER Award (2022) Sony Research Award (Faculty Innovation Award, 2021 & 2024) Russell J. Penrose Excellence in Teaching Award (2021) IEEE ICRA 2022 Outstanding Student Paper Award Advising & Collaborative Research : He advises 5 PhD students (Jiacheng Yuan, Alireza Rezazadeh, Houjian Yu, Ross Worobel, Mingen Li) and 2 Master's students (Chase Anderson, Nikhilanj Venkata Pelluri). His work involves grants from NSF, MnRI, and NRF (Korea).

Tim LynarView profile

Senior Lecturer

• Cyber Security
• Computer Science
• Machine Learning
• +6 more

Dr. Tim Lynar serves as a Senior Lecturer at the University of New South Wales Canberra within the School of Systems & Computing. With a strong background in both academic research and industry practice, he has established himself as a leading figure in cyber security and computer science. His work bridges theoretical research with practical applications, focusing on innovative solutions for complex computing challenges across multiple domains including IoT security, machine learning applications in cyber defense, and high-performance distributed systems. Dr. Lynar's research interests span a wide spectrum of cyber security applications, with particular emphasis on the application of machine learning techniques to security challenges and the innovative use of epidemiological approaches to understand and combat cyber threats. His work in modeling & simulation, statistical & data analysis, network & systems administration, and high-performance distributed computing demonstrates his commitment to developing comprehensive security frameworks that address evolving threats in digital environments. The interdisciplinary nature of his research connects computer science with biological modeling approaches, creating novel methodologies for understanding security vulnerabilities. Analysis of Dr. Lynar's recent publications reveals a strong trend toward applying advanced machine learning techniques to cyber security challenges, particularly in IoT environments. His work increasingly integrates epidemiological models with security frameworks, creating a unique approach to threat detection and mitigation. The research spans practical applications in network security, drone systems, and AI security, demonstrating both theoretical depth and real-world applicability. A notable pattern is the consistent application of cutting-edge deep learning architectures like Vision Transformers and Variational Autoencoders to solve specific security problems across diverse domains. IBM Master Inventor (2016) Multiple IBM Innovation Awards (2011-2018) Client Value Outstanding Technical Achievement Awards (2015-2016) High Value Patent Awards (2014-2016) Best Article Award – International Journal of Information Systems & Social Change (2010) Multiple research scholarships from 2007-2010 Dr. Lynar's extensive patent portfolio demonstrates significant industry impact, with numerous issued US patents spanning diverse applications from energy efficient supercomputing to vehicle collision avoidance and drone-based microbial analysis. His research has attracted substantial industry collaboration, particularly with IBM, where he received multiple prestigious awards including the IBM Master Inventor designation. The practical applications of his work are evident in the wide range of patented technologies addressing real-world security and optimization challenges across multiple industries. Dr. Lynar's work spans multiple research domains simultaneously, with active projects in cyber security, drone systems, AI safety, and maritime traffic analysis. His research methodology consistently combines theoretical modeling with practical implementation, often leveraging simulation environments to test and validate approaches before real-world deployment. The interdisciplinary nature of his work creates connections between traditionally separate fields, enabling innovative solutions to complex problems.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Technologies
• +5 more

Dr Zhe Wang is a Senior Lecturer at the School of Information and Communication Technology, Griffith University, focusing on artificial intelligence, knowledge graphs, and semantic technologies. He earned his PhD in Computer Science from Griffith University (2011) and previously worked as a Research Fellow at the University of Oxford (2011-2013) on ontology-based systems. Research: Specializes in knowledge graph construction, rule mining for explainable AI, and integrating machine learning with logical reasoning. Led development of the scalable RLvLR rule-mining system and contributed to the HermiT ontology reasoner. Teaching: Instructs undergraduate and postgraduate courses including Introduction to Artificial Intelligence, Secure Development Operations, and Software Engineering Fundamentals. Grants: Funded by Australia's Economic Accelerator Ignite Grant (2025) for AI-driven marine life survey systems and Office of National Intelligence projects (2021-2022). Publications: Active in top venues like AAAI, ICASSP, and ISWC, with recent work on temporal knowledge graph reasoning, auction design algorithms, and neurosymbolic AI systems.

Gedas AdomaviciusView profile

Professor

• Personalization Technologies
• Recommender Systems
• Data Mining
• +5 more

Gedas Adomavicius is a Professor at the University of Minnesota ’s Carlson School of Management , holding the Larson Endowed Chair for Excellence in Business Education. He obtained his PhD in Computer Science from New York University (2002) , with prior degrees in Mathematics (BS/MS, Vilnius University, 1995) and Computer Science (MS, NYU, 1998). Research Focus: Personalization technologies, recommender systems, and machine learning for decision-making in information-intensive environments. Key Contributions: Pioneered techniques for improving recommendation stability, reducing biases, and integrating contextual information into recommendation algorithms. Grants & Recognition: Recipient of the U.S. National Science Foundation CAREER award, INFORMS Information Systems Society Distinguished Fellow Award, and Association for Information Systems Fellow Award. Publications: His work spans leading journals across computer science, information systems, and biomedical informatics. Key trends include advancements in recommender systems (context-aware, multi-stakeholder, and bias-aware models), machine learning for health analytics , and e-market mechanism design . He has over 33,000 citations according to Google Scholar. Education: Diploma (BS/MS) in Mathematics from Vilnius University, 1995 MS in Computer Science from New York University, 1998 PhD in Computer Science from New York University, 2002 Awards & Grants: INFORMS Information Systems Society Distinguished Fellow Award Association for Information Systems Fellow Award National Science Foundation CAREER award Academic Leadership: Served as Senior Editor for Information Systems Research and MIS Quarterly , and as department chair and academic director of the MSBA program at the Carlson School.