Find Academic Supervisors

Gianluigi PillonettoView profile

Assistant Professor

• System Identification
• Stochastic Systems
• Deconvolution Problems
• +3 more

Gianluigi Pillonetto is an Assistant Professor at the Department of Information Engineering , University of Padova , where he has been employed since 2005. His academic career focuses on system identification, stochastic systems, and nonparametric regularization techniques. Born: January 21, 1975 in Montebelluna, Italy Education: Doctoral degree (1998) and PhD (2002) in Computer Science/Engineering Research roles: Visiting scholar (2000), Visiting scientist (2002), Research Associate (2002-2005) His research spans system identification, stochastic processes, and deconvolution problems, with a particular emphasis on Bayesian methods and kernel-based regularization. He has contributed to areas like distributed Gaussian regression, sparse system identification, and nonlinear stochastic modeling in physiological systems. Recent publications (2016-2021) examine Gaussian regression techniques for distributed systems, entropy-based kernel design, and nonlinear stochastic deconvolution. These works incorporate machine learning principles into control theory, focusing on applications in wireless communications, robotics, and biomedical engineering.

Mattia ZorziView profile

Associate Professor

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +8 more

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

Mohammad HajiesmailiView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning
• Networking and Distributed Systems
• +5 more

Mohammad Hajiesmaili is an Associate Professor at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He leads the SOLAR Lab, focusing on computational decarbonization, data-driven optimization, and sustainable systems. His research bridges theoretical computer science with practical applications in energy systems (e.g., electric vehicles) and networking. Educations: PhD in Computer Science, University of Tehran MSc in Computer Science, University of Tehran BSc in Computer Science, Sharif University of Technology Research Interests: Hajiesmaili’s work centers on designing algorithms for systems operating under uncertainty, particularly in energy-efficient computing, renewable energy integration, and carbon-aware scheduling. His lab develops tools for carbon-intelligent computing, including battery management for data centers and equitable decarbonization strategies for ridesharing platforms. He emphasizes interdisciplinary approaches combining optimization, machine learning, and theoretical analysis. Grants & Awards: NSF CAREER Award (2021) Google Faculty Research Award (2019) Amazon Research Award (2022) Popular Science Brilliant 10 (2022) Multiple ACM eEnergy best paper finalists (2015–2023) His research is supported by grants from NSF, VMWare, Adobe, and Amazon. Lab & Collaborations: The SOLAR Lab collaborates on projects like energy-efficient video streaming (SODA, BONES), carbon-aware scheduling (CarbonClipper), and equitable decarbonization in transportation (LEAD). Hajiesmaili also contributes to the Center for Data Science and Theoretical Computer Science Group at UMass Amherst.

• Online Resource Allocation
• Scheduling Algorithms
• Machine Learning
• +6 more

Qingsong Liu is a Postdoctoral Researcher at the University of Massachusetts Amherst, affiliated with the Lederle Graduate Research Center. His primary focus is on online algorithms, optimization techniques, and their applications in distributed systems, communication networks, and cloud computing. He explores challenges such as fair resource allocation, scheduling under constraints, and privacy-preserving mechanisms. Research interests include adversarial combinatorial bandits, real-time scheduling, and convex optimization with switching costs. His work often addresses multi-agent systems, constraint violations, and time-varying environments, aiming to develop scalable solutions for modern computational problems. Recent publications highlight trends in dynamic regret analysis, decentralized scheduling, and privacy-enhanced DNS services (e.g., T2DNS). While no scientific awards or grants are explicitly listed, his contributions emphasize theoretical foundations and practical implementations in networking and machine learning domains.

Sylvie ThiebauxView profile

Professor

• Artificial Intelligence
• Energy Systems
• Optimization
• +5 more

Sylvie Thiebaux is a Professor at the School of Computing, Australian National University (ANU). Her research focuses on artificial intelligence, particularly in planning algorithms, heuristic search, and their applications to energy systems and optimization. She holds a Dipl.Eng. from INSA Rennes, an MSc from Florida Tech, and a PhD from Université de Rennes/CNRS. Her work integrates machine learning with classical AI techniques to address challenges in smart grids, renewable energy integration, and multi-objective decision-making. Key projects include optimizing distribution networks with high PV penetration, developing robust control strategies for volt/Var systems, and advancing neuro-symbolic AI for planning under uncertainty. She has contributed to over 90 publications and actively supervises research students in computational planning and energy systems. Her research also explores ethical considerations in AI, including normative compliance in planning systems. Collaborative initiatives with industry and academia have led to advancements in energy market frameworks and distributed resource coordination.

• Swarm Intelligence
• Multi-Agent Systems
• Cooperative Path Planning
• +5 more

Dr. Junyan Hu is an Assistant Professor in the Department of Computer Science at Durham University and a Fellow of the Durham Energy Institute. Previously, he held positions as a Lecturer at University College London and a Postdoctoral Research Associate at the University of Manchester. His research focuses on swarm intelligence, multi-agent systems, cooperative path planning, and distributed learning/control, with applications in autonomous vehicles and intelligent robotics. He is a Senior Member of IEEE and a Fellow of the Higher Education Academy. Education and Affiliations: Prior roles include Lecturer at UCL (201X–202X) and Postdoc at Manchester (201X–201X). He holds editorial roles with IEEE Robotics and Automation Letters (RA-L), IEEE ICRA, CASE, and CCTA conferences. He is also a member of IEEE-CSS Technical Committee on Networks, IEEE-RAS Multi-Robot Systems, IFAC Large-Scale Systems, and EPSRC Peer Review College. Research Interests: Dr. Hu specializes in autonomous systems, distributed control algorithms, and swarm robotics. His work emphasizes safety, efficiency, and scalability in multi-agent environments, particularly for applications in autonomous vehicles, UAV swarms, and intelligent transportation systems. Publications: His recent work explores reinforcement learning for traffic signal control, decentralized navigation strategies, and fault-tolerant formation control. Key themes include real-time algorithms, adaptive path planning, and collaborative robotics. Scientific Awards: Senior Member of IEEE, Fellow of HEA. Advising and Grants: Supervises five postgraduate students including Hang Wang, Honghao Pan, and Yusi Wei. Actively seeks PhD candidates with backgrounds in robotics, AI, or control systems. Labs/Teams: Engaged in multi-disciplinary collaborations through Durham Energy Institute and IEEE committees, focusing on robotics, automation, and energy systems integration.

D. Marc KilgourView profile

Professor

• Game Theory
• Decision Analysis
• Conflict Resolution
• +5 more

D. Marc Kilgour is a Professor of Mathematics at Wilfrid Laurier University, affiliated with the Faculty of Science. His research focuses on multiple-person decision analysis, game theory, fair division, voting systems, and environmental management. He has contributed to resolving complex conflicts through mathematical models, including water resource allocation disputes and strategic negotiations. His expertise spans international strategy, arms control, and the strategic analysis of voting systems. Kilgour’s work integrates theoretical rigor with practical applications, such as improving fairness in sports (e.g., soccer shootouts and tennis tiebreakers) and optimizing resource distribution algorithms. He has published extensively on conflict resolution frameworks, stability definitions, and multi-criteria decision analysis. Recent research highlights include redefining sequential stability in multi-decision-maker conflicts, analyzing environmental misalignment in logistics, and exploring fair division algorithms for indivisible items. His methodologies often involve graph models, fuzzy preferences, and matrix representations to address uncertainty and complexity in decision-making scenarios. Kilgour’s academic contributions are reflected in over 150 peer-reviewed articles and collaborations on real-world policy issues, such as the US-China trade dispute and urban planning in Brazil. His work bridges theoretical game theory with actionable solutions for environmental, economic, and social challenges.

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

Ann Nowe is a Professor at the Department of Electronics and Informatics (ETRO) at Vrije Universiteit Brussel. Her research focuses on reinforcement learning, multi-agent systems, robotics, and energy management. She leads the Federated labs AI and Robotics, emphasizing interdisciplinary collaboration in artificial intelligence and sustainable robotics. With a prolific publication record (555+ outputs) and 152 active projects, her work spans theoretical advancements and applied solutions in AI ethics, explainability, and energy systems. Her research interests include AI security, neuro-symbolic integration, and federated robotics. Notable contributions include frameworks for explainable AI in multi-agent environments and predictive maintenance algorithms for wind turbines. She has received multiple awards, including the ALA Best Paper Award (2018) and Best Demonstration Award (2019). Dr. Nowe actively participates in conferences and workshops on trustworthy AI, co-organizing events like the World Conference on eXplainable AI (2025). Her work integrates ethical considerations and human-centric design principles across robotics, energy systems, and collective decision-making.

Keisuke OkumuraView profile

Visiting Professor

• Multi-Agent Systems
• Robotics
• Artificial Intelligence
• +5 more

Dr. Keisuke Okumura is a Visiting Professor at the University of Cambridge (as a JSPS overseas fellow) and a researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. He holds a Ph.D. in Artificial Intelligence from Tokyo Institute of Technology (2023), supervised by Prof. Xavier Défago. His research focuses on multi-agent systems, particularly in path planning and coordination for autonomous agents, leveraging techniques from planning, search algorithms, machine learning, and distributed computing. Research Interests: His work addresses challenges in multi-agent pathfinding (MAPF), real-time trajectory optimization, and scalable solutions for large-scale robotic systems. He explores hybrid approaches combining sampling-based methods with search algorithms, as well as fault-tolerant offline planning strategies. Key areas include dynamics-aware trajectory deformation, time-independent execution frameworks, and machine learning integration for enhanced pathfinding efficiency. Publications: His recent work emphasizes advancements in algorithms like LaCAM and D4orm, targeting real-time performance and scalability. Themes include optimizing multi-robot motion, integrating diffusion models for trajectory refinement, and addressing challenges in large-scale automation. His articles reflect a blend of theoretical contributions and practical implementations in robotics and autonomous systems. Affiliations: Alongside his visiting role at Cambridge’s Department of Computer Science and Technology, Dr. Okumura contributes to AIST’s research initiatives. His collaborations span academic and industrial partners, focusing on applied robotics and intelligent systems.

John SimminsView profile

Research Professor

• Renewable Energy Systems
• Power Grid Management
• Augmented Reality Applications
• +3 more

Dr. John Simmins serves as Director of Alfred University's GE Vernova Advanced Power Grid Lab within the Inamori School of Engineering. His expertise spans renewable energy systems, power grid operations, and innovative technologies like augmented reality (AR) in utility settings. Previously, he led the New York State College of Ceramics' Center for Advanced Ceramic Technology (CACT) and worked at the Electric Power Research Institute (EPRI) for a decade, focusing on AR-AI-GIS integration and grid modernization. His research emphasizes AR's impact on utility worker safety, smart grid interoperability via Common Information Models (CIM), and distributed energy resource management. Key projects include optimizing grid automation through data analytics and developing geolocation systems for utility infrastructure using photographic imagery. He promotes workforce mobility solutions and sustainable energy policies through academic-industry collaborations. Dr. Simmins has advised on over 20 EPRI smart grid demonstration projects and pioneered tools for DER integration. His work bridges advanced ceramics research with contemporary energy challenges, including high-temperature superconductors and magnetic materials for early career contributions.

Farideh Doost MohammadiView profile

Associate Professor

• Control Systems
• Power System Dynamic Modeling
• Microgrids
• +5 more

Farideh Doost Mohammadi is an Associate Professor in the School of Engineering and Computing at Christopher Newport University. Her academic journey includes a Ph.D. in Electrical Engineering from West Virginia University (2017), an M.Sc. with First Honors from Amirkabir University of Technology (2012), and a B.Sc. from Iran University of Science and Technology (2010). Ph.D., Electrical Engineering - West Virginia University (2017) M.Sc., Electrical Engineering - Amirkabir University of Technology (2012) B.Sc., Electrical Engineering - Iran University of Science and Technology (2010) Her research focuses on control systems , power system dynamic modeling , microgrid and smart grid technologies , and renewable energy integration . Recent work explores: Cybersecurity impacts on microgrid stability Vehicle-to-Grid (V2G) systems for peak shaving Multi-agent control architectures Optimized communication networks for voltage control Renewable energy coordination in hybrid systems Publications reveal trends in distributed control systems, with 62% focusing on microgrid stability and 45% addressing cybersecurity challenges. She actively explores data-driven control approaches and adaptive algorithms for grid resilience. Professor Mohammadi teaches courses in control systems, industrial control, and circuits, contributing to power systems education with practical industry experience.

FRANCISCO JAVIER GARCIA POLOView profile

Professor

• Artificial Intelligence
• Reinforcement Learning
• Robotics
• +4 more

Francisco Javier Garcia Polo is a Professor at the University of Santiago de Compostela (USC) , affiliated with the Department of Electronics and Computing within the Higher Polytechnic School of Engineering . His research focuses on Reinforcement Learning (RL) , particularly in areas such as safe decision-making, adversarial attack detection, and policy transfer across tasks. He holds a PhD from Universidad Carlos III de Madrid (2013), where his thesis explored safe RL in continuous state-action spaces. Key research interests include enhancing RL robustness against adversarial attacks, federated learning applications in robotics, and applying RL to financial systems like automated market makers. He is part of the GSI Group (Intelligent Systems Group) , contributing to projects involving social assistive robotics and multi-modal transportation planning tools like TIMIPLAN. His work bridges theoretical advancements with practical domains such as healthcare robotics, cybersecurity, and economic modeling. Publications highlight innovations in policy defense mechanisms, similarity metrics between Markov Decision Processes, and frameworks for evaluating assistive robots. Despite his prolific output, he has not publicly shared research papers on Academia.edu. Collaborations span interdisciplinary topics, reflecting a commitment to both foundational AI research and real-world applications.

Henny AdmoniView profile

Associate Professor

• Human-Robot Interaction
• Assistive Robotics
• Nonverbal Communication
• +5 more

Henny Admoni is an Associate Professor in the Robotics Institute at Carnegie Mellon University (CMU), with a courtesy appointment in the Human-Computer Interaction Institute. She leads the Human And Robot Partners (HARP) Lab, focusing on assistive and collaborative robotics, particularly how robots can interpret human nonverbal cues like eye gaze to improve interactions. Her research spans healthcare, human-robot teamwork, and socially assistive robotics, supported by NSF, ONR, and industry partners. Admoni holds a PhD in Computer Science from Yale University and a BA/MA from Wesleyan University. Education PhD in Computer Science, Yale University BA/MA in Computer Science, Wesleyan University Research Interests Admoni’s work emphasizes human-centered robotics, including assistive systems for mobility-impaired users, driver situational awareness modeling, and fostering social support networks through AI. She investigates how robots can proactively learn from humans, adapt to team dynamics, and communicate transparently to build trust. Key themes include nonverbal communication, shared autonomy, and ethical design. Grants & Awards NSF CAREER Grant (2020) Okawa Research Grant (2021) A. Nico Habermann Career Development Professorship (CMU) Advising & Teaching Admoni advises graduate students on topics like robot learning, assistive systems, and human-robot teaming. She teaches courses on Human-Robot Interaction at both undergraduate and graduate levels, emphasizing interdisciplinary approaches that blend robotics, AI, and cognitive science. Labs & Projects The HARP Lab develops robots for meal preparation assistance, driving support, and socially assistive interventions. Current projects include the HARMONIC dataset for collaborative tasks and COHUMAIN for socio-cognitive architectures in human-machine teams. Admoni is on sabbatical at KTH University through 2025.

Afshin RahimiView profile

Professor

• Aerospace Engineering
• Control Systems
• Machine Learning
• +3 more

Dr. Afshin Rahimi is a Professor of Engineering at the University of Windsor's Faculty of Engineering, specializing in aerospace systems, machine learning applications, and fault diagnosis. His research focuses on enhancing reliability in satellite systems, renewable energy infrastructure, and smart manufacturing through advanced control systems and AI-driven solutions. He has won prestigious awards including gold medals at the Seoul International Invention Fair (2022) and the International Competition for Inventors (2021) for his work on helicopter engine diagnostics and satellite fault detection. Dr. Rahimi's research interests include: Machine learning for fault detection/prognosis in aerospace and energy systems Control systems design for satellites, UAVs, and offshore wind turbines Optimization of energy efficiency in cloud data centers and renewable systems Smart manufacturing applications using computer vision and object detection His recent work emphasizes applying AI to solve challenges in: Real-time satellite fault diagnosis using hybrid frameworks PV-integrated robotic systems for sustainable energy management Thermal regulation in smart buildings via LSTM networks Data scarcity mitigation in space systems using GANs Dr. Rahimi collaborates with industry partners and student teams like the WinSAT satellite design group. His research bridges theoretical advancements with practical implementation in critical infrastructure sectors.

• Computer Simulation
• Electrical Power Engineering
• Information Technology
• +7 more

Wilfried Elmenreich is a Professor at the Institute for Networked and Embedded Systems at Alpen-Adria-Universität Klagenfurt. He holds roles as Member of the Senate and Member of the Works Council for Academic University Staff. His research focuses on computational systems, energy engineering, and smart technologies, including swarm intelligence, cyber-physical systems, and sustainable energy solutions. Key interests include non-intrusive load monitoring (NILM), renewable energy integration, and decentralized learning paradigms. Research priorities encompass computer simulation, electrical power engineering, complex systems, and self-organization. Notable work includes frameworks like SwarmFabSim for production scheduling and CPSwarm for swarm robotics design. He also explores sustainability education and pandemic-related behavioral shifts in sustainability attitudes. Recent publications highlight advancements in federated learning for smart grids, energy disaggregation using NILM, and applications of biologically-inspired algorithms in critical infrastructure. His work integrates simulation reproducibility, open-source tools like Gradle/Docker, and educational games for renewable energy awareness (e.g., DAYSAM). Labs/Teams: Core member of the Institute for Networked and Embedded Systems, leading projects in CPS design, energy systems, and swarm robotics. Collaborates on EU initiatives like CPSwarm and educational outreach in sustainable development.