Find Academic Supervisors

Zhongguo LiView profile

Lecturer

• Distributed Control
• Optimization
• Robotics
• +5 more

Zhongguo Li is a Lecturer in Robotics, Control, Communication & AI at the University of Manchester. He holds a B.Eng. (2017) and Ph.D. (2021) in Electrical and Electronic Engineering from the University of Manchester. Prior to his current role, he was a Lecturer at University College London (2022-2023) and a Research Associate at Loughborough University (2020-2022). His research focuses on distributed control, optimization, and reinforcement learning, particularly in robotics and autonomous systems. Key areas include multi-agent coordination, networked systems, and applications in autonomous vehicles. He has authored over 40 papers in top journals/conferences and co-authored a book on Distributed Optimization and Learning (2024). Teaching responsibilities include courses such as Control Systems II, Nonlinear and Adaptive Control, and Embedded Systems Project. He serves as an Associate Editor for Drones and Autonomous Vehicles and Guest Editor for Machines and Frontiers in Control Engineering. Dr. Li actively mentors PhD students, offering guidance on funding opportunities and research projects in distributed algorithms, robotics, and control systems. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure.

Shuangning LiView profile

Assistant Professor

• Econometrics
• Statistics
• Machine Learning
• +5 more

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

Guillaume A SARTORETTIView profile

Assistant Professor

• Robotics
• Multi-Agent/Multi-Robot Systems
• Distributed/Decentralized Control
• +4 more

Guillaume A SARTORETTI is an Assistant Professor in the Mechanical Engineering Department at the National University of Singapore (NUS), part of the College of Design and Engineering. He specializes in distributed/decentralized coordination of multi-agent systems, with a focus on robotics, stochastic modeling, and reinforcement learning. His work spans applications in multi-robot systems, articulated robots, and swarm intelligence. Joined NUS in 2019 after a postdoctoral fellowship at Carnegie Mellon University (CMU) and a PhD from EPFL. Education: PhD in Robotics (EPFL, 2016); MSc and BSc in Mathematics/Computer Science (University of Geneva). Research interests include: Multi-agent pathfinding Decentralized control policies Swarm intelligence Reinforcement learning applications in robotics Recent publications emphasize scalable solutions for multi-agent systems, traffic signal control, and safe robotic exploration. His 2018 MFI postdoctoral fellowship recognized work on distributed reinforcement learning for pathfinding. Current projects involve bio-inspired locomotion and collaborative learning frameworks for heterogeneous robots.

Nashid ShahriarView profile

Assistant Professor

• Computer Networks
• 5G Mobile Networks
• Resource Allocation
• +5 more

Nashid Shahriar is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research addresses resource allocation challenges in next-generation networks including 5G, elastic optical networks, cloud infrastructures, and IoT systems. He holds a Ph.D. in Computer Science from the University of Waterloo, an M.Sc. from Bangladesh University of Engineering and Technology (BUET), and a B.Sc. from BUET. His work leverages optimization, machine learning, and AI for network management. Recent publications focus on 5G network slicing, intrusion detection, and NFV security. Research emphasizes practical AI-driven solutions for telecommunications and cloud systems.

Hédi HadijiView profile

Associate Professor

• Online Decision-Making
• Adaptivity in Bandits
• Online Learning
• +3 more

Hédi Hadiji is an Associate Professor at the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. His research focuses on the mathematics of online decision-making, particularly adaptivity in bandits and online learning. Prior to this role, he was a postdoctoral researcher at the University of Amsterdam under Tim van Erven. He holds a PhD in Mathematics from Université Paris-Saclay, supervised by Gilles Stoltz and Pascal Massart. Education: PhD in Mathematics, Université Paris-Saclay (2020) Masters in Probability and Statistics, Université Paris-Saclay (2017) Part III Mathematical Tripos, University of Cambridge (2016) École Polytechnique (2012–2016) Research Interests: Hédi’s work addresses theoretical foundations of online learning, including bandit algorithms, reinforcement learning, and optimization. He explores adaptive strategies in dynamic environments, with applications to stochastic and adversarial settings. Key themes include minimax optimal algorithms, regret analysis, and the interplay between exploration and exploitation. Recent Publications: His recent work includes minimax optimal algorithms for linear bandits, tracking solutions in time-varying systems, and diversity-preserving strategies in K-armed bandits. These contributions advance theoretical understanding of adaptive decision-making in complex systems. Teaching: He teaches courses on the theoretical principles of deep learning, reinforcement learning, and mathematical foundations at the graduate level. His lectures emphasize rigorous analysis of generalization, optimization, and the neural tangent kernel. Labs & Affiliations: His primary affiliation is with L2S, where he engages in collaborative research groups such as MODESTY, COMEDY, and SYCOMORE. His work intersects signal processing, control systems, and communication networks.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +5 more

Nisar Ahmed is an Associate Professor at the University of Colorado within the Aerospace Engineering Sciences department. His research focuses on the intersection of Artificial Intelligence , Robotics , and Autonomous Systems , emphasizing decision-making under uncertainty, sensor fusion, and human-machine collaboration. Key research interests include: Active Inference for autonomous planning Decentralized Data Fusion in multi-robot systems Machine Self-Confidence and competency assessment Reinforcement Learning for spacecraft and robotic guidance Uncertainty Quantification in dynamic environments Recent publications highlight trends in Pareto-optimal decision-making , Bayesian optimization , contextual bandits , and trust calibration for UAS and planetary rovers. His work integrates probabilistic modeling with real-time autonomy , ensuring robustness in applications like search-and-rescue missions and lunar exploration. Contact: Nisar.Ahmed@Colorado.EDU

Zoran GajicView profile

Professor

• Control Systems
• Energy Systems
• Wireless Communications
• +4 more

Zoran Gajic is a Professor of Electrical and Computer Engineering at Rutgers University, where he has taught since 1984. He holds academic leadership roles including Graduate Program Director for the Electrical and Computer Engineering Department and President of the Rutgers AAUP-AFT Faculty Union. His expertise spans controls systems, energy systems (including solar, wind, and smart grids), wireless communications, and networking. Education: B.S. and M.S. in Electrical Engineering from University of Belgrade, followed by M.S. in Applied Mathematics and Ph.D. in Systems Science Engineering from Michigan State University (1984). Research focuses on control theory applications for energy systems and communication networks. He has authored/coauthored nearly 100 journal papers and eight books, including best-selling titles like Linear Dynamic Systems and Signals (translated into Chinese) and Lyapunov Matrix Equation in Systems Stability and Control (republished by Dover). His work includes innovations in multirate control systems, singular perturbation methods, and reinforcement learning applications. Professional recognitions include editorial roles across nine journals, five guest-edited special issues, and plenary lectures at international conferences. Ten of his 17 Ph.D. advisees hold faculty positions globally. Beyond academia, he is a chess master with Life Master ranking from the U.S. Chess Federation and World Chess Federation certification. Key contributions include foundational work on optimal control for renewable energy systems, sliding mode control algorithms, and system decomposition techniques. His research has been supported by NSF and industry partners like AT&T Bell Labs. He leads the Rutgers Center for Systems and Controls (SYCON) as Associate Director and actively contributes to standards through IEEE and IEC initiatives, particularly in power system protection and control system reliability.

• Wireless Sensor Networks
• Cyber-Physical Systems
• Network Protocols
• +3 more

John Harrison Kurunathan is an Integrated PhD Researcher affiliated with the CISTER Research Centre at the University of Porto, Portugal. He holds a PhD in Electrical and Computer Engineering (2021), a Master's in Very Large-Scale Integration (2014), and a Bachelor's in Electronics and Communication (2012). Education: PhD (2021) - University of Porto, Portugal MSc (2014) - SSN College of Engineering, Anna University BSc (2012) - SRM University His research focuses on Wireless Sensor Networks (WSNs) , Cyber-Physical Systems (CPS) , and Automotive Networks , with an emphasis on Quality-of-Service (QoS) optimization, secure communication, and vehicular platooning. Notable projects include SafeCOP for safety-related CO-CPS and work on IEEE 802.15.4e DSME networks. Recent publications (2023-2025) span areas like Visible Light Communication , Vehicular Security , and Machine Learning in UAV Operations , reflecting his interdisciplinary work bridging embedded systems and transportation technologies. Scientific Awards: Best oral communication Award (in ex aequo) at DCE 2019 Reviewing Roles: Conference: ICCPS, EWSN, MSN, RTN Journal: IEEE ACCESS, IEEE Transactions on Vehicular Technology, ACM Sigbed Harrison is actively involved in workshops and conferences, including chairing roles at WIN-WIN-4S 2024 and technical demonstrations at WoWMoM 2023. His work appears in venues like IEEE Transactions on ITS, IEEE COMST, and PDP 2025.

• Robotics
• Control Systems
• Human-Robot Interaction
• +3 more

Cristian Secchi is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia. His research focuses on robotics, control systems, and human-robot collaboration with an emphasis on safety, automation, and industrial applications. He teaches courses such as Industrial and Collaborative Robotics and Control of Robotic Systems. Research Interests: Design and implementation of control architectures for collaborative robots. Development of safe human-robot interaction frameworks compliant with ISO/TS 15066 standards. Integration of AI (e.g., large language models) into robotic motion planning. Autonomous systems for urban environments and surgical robotics. Publications highlight advancements in: Energy-efficient trajectory planning. Adaptive control strategies for uncertain environments. Multimodal human-robot communication. Diagnosis of mechanical systems via signal analysis. He leads the ARSControl research group and actively contributes to evolving production systems through smart modular technologies. His work bridges theoretical control frameworks with practical industrial and medical applications.

Yue HuView profile

Assistant Professor

• Robotics
• Human-Robot Interaction
• Assistive Robotics
• +3 more

Yue Hu is an Assistant Professor at the University of Waterloo, affiliated with the Faculty of Engineering. His research focuses on Human-Robot Interaction (HRI), assistive robotics, and control systems with a particular emphasis on safety, adaptability, and user experience. Key areas include robot emotional expressions, physical interaction safety, and real-time systems for social robots. He leads the Active and Interactive Robotics Lab , developing solutions for mobility assistance, teleoperation systems, and cybersecurity in robotics. His work integrates biomechanical modeling, computer vision, and machine learning to create robots that better understand and adapt to human needs. Notable projects include real-time pose estimation for mobility support, encrypted network traffic analysis for robot security, and personality shaping in social robots. He emphasizes ethical design and human factors in robotics, conducting studies on refugee education and unanticipated robot actions. Yue Hu holds a full-time faculty position and collaborates with industry and academic partners to advance assistive technologies and interactive systems. His research bridges theoretical foundations with practical applications, aiming to improve quality of life through innovative robotic solutions.

Lokesh DasView profile

Assistant Professor

• Autonomous Vehicles
• Traffic Control Systems
• Deep Learning
• +4 more

Dr. Lokesh Das is an Assistant Professor in the School of Computing at Wichita State University’s College of Engineering. His research focuses on developing advanced algorithms for autonomous vehicle traffic control systems using deep learning and V2X communication technologies. Key interests include reinforcement learning applications in real-world traffic scenarios, IoT security frameworks, and smart infrastructure integration. Research Interests: Autonomous Vehicle Coordination Reinforcement Learning for Traffic Systems IoT Security and Edge Computing Dynamic Wireless Charging Solutions Pedestrian Safety Prediction His recent work emphasizes multi-agent systems for traffic optimization and safety-aware adaptive control mechanisms. No specific awards are listed, but his publications reflect impactful contributions to intelligent transportation systems and machine learning applications. Advising and grants information is not explicitly provided in the text. Dr. Das’s research lab likely focuses on real-world implementation of AI-driven traffic solutions.

Sandip HarimkarView profile

Professor

• Laser Additive Manufacturing
• Spark Plasma Sintering
• Advanced Materials Processing
• +3 more

Sandip Harimkar is a Professor and Albert H. Nelson, Jr. Chair in the Department of Mechanical and Aerospace Engineering at Oklahoma State University (OSU), part of the College of Engineering, Architecture, and Technology (CEAT). His research focuses on advanced materials processing, including laser additive manufacturing, spark plasma sintering, and surface engineering. He holds a Ph.D. from the University of Tennessee, Knoxville (2007), an M.E. from the Indian Institute of Science (2003), and a B.E. from Visvesvaraya National Institute of Technology (1999). His research interests include laser additive manufacturing of structural materials, spark plasma sintering of amorphous alloys and ceramics, laser surface processing, and pulse electrodeposition of composite coatings. He has pioneered studies on nanocrystallization in metallic glasses and wear-resistant coatings for aerospace applications. His work integrates experimental techniques with computational modeling to optimize material properties and processing parameters. Harimkar has authored over 150 peer-reviewed publications and holds multiple patents. His articles span topics like additive manufacturing quality control, electrochemical strain analysis in battery materials, and thermal oxidation quantification in metallic glasses. Research trends in his work emphasize interdisciplinary approaches to materials design, combining advanced characterization (e.g., neutron/X-ray scattering) with machine learning for process optimization. Awards: NSF CAREER Award (2012) Outstanding Young Researcher Award (OSU, 2014) ASM-IIM Visiting Lectureship Award (2013) Young Leaders International Scholar-JIM Award (2013) He teaches courses such as Mechanical Metallurgy (MAE 4333) and Phase Transformations in Materials (MAE 5693). His professional service includes leadership roles in TMS committees, including Chair of the Surface Engineering Committee (2010–2016). His labs focus on advanced materials synthesis and characterization for energy, aerospace, and biomedical applications.

• Compliant Mechanisms
• Robotics
• Precision Manufacturing
• +5 more

Dr. Guangbo Hao is a Professor of Mechanical Engineering at University College Cork (UCC), where he leads the UCC CoMAR research group and directs the Mechatronics/Robotics Lab. He holds dual PhDs from Northeastern University (China, 2008) and Heriot-Watt University (UK, 2011). His research focuses on compliant mechanisms, robotics, and their applications in precision manufacturing, energy harvesting, and medical devices. He has secured over €1.3M in research funding, including grants from SFI, EU Horizon 2020, and Enterprise Ireland. He is an ASME Fellow and has received multiple accolades, including the ASME Compliant Mechanisms Award (2017, 2018, 2022) and the UCC President’s Excellence in Teaching Award (2023). Education: BSc (2004), MSc (2007), PhD (2008) from Northeastern University; second PhD (2011) from Heriot-Watt University Research interests include compliant mechanisms, mechatronics, and robotics with emphasis on deployable structures, medical devices, and energy harvesting systems. His work has resulted in over 200 peer-reviewed publications and 30 invited talks. He supervises students in PhD, research-master, and taught-master programs, with notable student achievements in international competitions and awards. Professional roles include Associate Editorships at Advanced Equipment , ASME Journal of Mechanisms and Robotics , and IEEE Robotics and Automation Letters . He chairs major conferences like ASME IDETC/CIE 2025 and co-chairs the 2026 IEEE/ASME AIM conference. His labs, including the UCC Engineering Maker Lab, focus on innovation in robotics and mechatronics.

Zhong-Ping JiangView profile

Professor

• Nonlinear Control Theory
• Dynamical Networks
• Data-Driven Optimization
• +7 more

Dr. Zhong-Ping Jiang is a Professor in the Department of Electrical and Computer Engineering and an Affiliate Professor in the Department of Civil and Urban Engineering at the Tandon School of Engineering, New York University. He has been with NYU since 2007, previously serving as Associate Professor (2002-2007) and Assistant Professor (1999-2002) at Polytechnic University (now part of NYU). Dr. Jiang received his MSc degree in Statistics from the University of Paris XI in 1989 and his PhD degree in Automatic Control and Mathematics from the Ecole des Mines de Paris in 1993. He has maintained strong international collaborations with researchers across Europe throughout his career. Internationally recognized as a key contributor to nonlinear small-gain theory, Dr. Jiang is also well-known for his seminal work in robust adaptive dynamic programming and robust reinforcement learning for real-time safety-critical control applications. His research spans dynamical networks, nonlinear control theory, data-driven optimization, and reinforcement learning with applications to mechanical, information and biological systems. Dr. Jiang's publications have had significant impact in the field, with over 500 peer-reviewed journal and conference papers, more than 34,300 citations, and an h-index of 90 on Google Scholar. He has also published six research monographs since 2011. His notable awards and honors include: Election to European Academy of Sciences and Arts (2023) Stanford's Top 2% Most Highly Cited Scientists (2023) Election to Academia Europaea (2021) Highly Cited Researchers by Clarivate Analytics (2018) Fellow of Chinese Association of Automation (2017) Fellow of International Federation of Automatic Control (2013) Fellow of IEEE (2008) National Distinguished Professor, Chinese Ministry of Education (2009) Dr. Jiang serves as Deputy Editor-in-Chief for the IEEE/CAA Journal of Automatica Sinica and has held editorial positions for numerous prestigious journals including IEEE Transactions on Automatic Control. He has delivered more than 20 plenary, semi-plenary and keynote speeches at major international conferences and workshops.

Marc ToussaintView profile

Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Marc Toussaint is Full Professor leading the Learning & Intelligent Systems Lab at TU Berlin's EECS Faculty. His research integrates machine learning, optimization, and AI reasoning to solve fundamental robotics problems like physical reasoning and human-robot interaction. He holds a physics diploma from University of Cologne and PhD from Ruhr-Universität Bochum. Key research themes include: Task-motion planning integration Reinforcement learning for robotics Physical simulation and control Probabilistic inference methods Recent publications focus on efficient kinodynamic planning, belief space planning under uncertainty, and neural policy learning. He develops open-source robotic tools like the 'robotic python package' used in academic courses worldwide. Toussaint collaborates with Amazon Robotics and MIT CSAIL, and has held positions at Max Planck Institute and University of Stuttgart.