جست‌وجوی استاد و پژوهشگر

Philippe Ciblatمشاهده پروفایل

استاد

• Statistical Signal Processing
• Digital Communications
• Resource Allocation
• +۶ مورد دیگر

Philippe Ciblat is a Full Professor at Institut Polytechnique de Paris , affiliated with the Communications and Electronics Department (Comelec) and the Information Processing and Communication Laboratory (LTCI) . He obtained his Engineering Degree from Télécom Paris (1996), DEA in Automatic Control from Université Paris-Saclay (1996), PhD from Université Gustave Eiffel (2000), and HDR diploma (2007). After a postdoctoral fellowship at Université de Louvain (2001), he joined Télécom Paris as Associate Professor in 2001 and was promoted to Full Professor in 2011. Research Activities: His research spans three core areas: Statistical/Digital Signal Processing (blind equalization, frequency estimation, distributed estimation) Signal Processing for Communications (OFDM synchronization, UWB localization, cooperative communications) Resource Allocation (HARQ optimization, power allocation, distributed networks) Scientific Leadership: He has served as Associate Editor for IEEE Transactions on Signal and Information Processing over Networks (2018-2022), Senior Area Editor for IEEE Transactions on Signal Processing (2011-2012), and held editorial roles in IEEE Communications Letters and Transactions on Signal Processing. He chairs technical committees at EUSIPCO, SPAWC, and IEEE conferences. PhD Supervision: He has mentored 23 PhD students, including Current: Nils Reynaud (deep reinforcement learning for wind farms), Abdelghani Ghanem (multi-agent driving behavior), Sylvain Nerondat (deep reinforcement learning for ad hoc networks) Former: Yue Bi (distributed computing), Hakim Hafidi (robust graph ML), Arthur Louchart (nonlinear satellite systems), Vincent Corlay (lattice decoding), Alix May (optical network monitoring) Grants & Collaborations: Currently leads projects with TotalEnergies (2025-2028), PEPR Twinfarms (2025-2028), and ANR SUCCEN (2025-2027). Formerly secured ANR, Marie-Curie, and CIFRE grants with Thales, Nokia, Mitsubishi, and Huawei. Labs & Teams: Affiliated with LTCI (Signal, Statistics and Learning team) and has conducted sabbaticals at Technische Universität Berlin, Rutgers University, and Universidad Nacional de Colombia. Actively participates in international conferences as TPC member and reviewer.

Michael Y. Liمشاهده پروفایل

استاد

• Mathematical Biology
• Differential Equations
• Dynamical Systems
• +۷ مورد دیگر

Dr. Michael Y. Li is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , where he has been since 2004. He also serves as Director of the Information Research Laboratory (IRL) and the Course-based Master's Program in Modeling, Data, and Predictions (MDP@ualberta). Ph.D. in Applied Mathematics (University of Alberta, 1993) M.Sc. and B.Sc. in Mathematics (Jilin University, China) Research Interests: Dr. Li specializes in Nonlinear Differential Equations and Dynamical Systems , applying these to Mathematical Biology , Infectious Disease Modeling , and Public Health . His work focuses on: Viral Dynamics: HIV, HTLV-I, and influenza infections, including immune responses and treatment effects. Network Systems: Stability analysis, synchronization, and hierarchical control in coupled differential equations. Public Health Modeling: Tuberculosis in Aboriginal communities, HIV incidence estimation, and pandemic response strategies. Publication Trends: Recent articles emphasize COVID-19 dynamics , HIV persistence , and network stability , with applications to epidemiology , immunology , and robotics . His work bridges theoretical mathematics and real-world public health . Scientific Awards: McCalla Research Professor (2010) Tang Ao-Qing Professor (2009) NSERC Postdoctoral Fellowship (1993-94) Ralph E. Powe ORAU Award (1998) NSF Grants (1996-99, 2000-03) CFI New Opportunities Fund (2003-07) Advising and Grants: Dr. Li supervises graduate students in mathematical biology and network dynamics . He has secured NSERC Discovery Grants , NSF funding , and MITACS collaborations . His research integrates analytical , statistical , and numerical methods . Labs and Teams: Dr. Li leads the Information Research Laboratory (IRL) , which provides Alberta influenza projections , and the MDP@ualberta program , training students in modeling and data science . He collaborates with virologists at the Li Ka-Shing Institute of Virology and public health institutions in Alberta and China.

• Network Optimization
• Network Economics
• Game Theory
• +۷ مورد دیگر

ÖMER KORÇAK is an Associate Professor and Vice Chair at the Department of Computer Engineering, Faculty of Engineering, Marmara University in Istanbul, Turkey. He holds a B.S., M.S., and Ph.D. in Computer Engineering from Boğaziçi University (2002, 2004, 2009). He served as a visiting researcher at Deutsche Telekom Laboratories in Berlin in 2011. His research focuses on network optimization, game theory applications in wireless/broadband networks, and IoT systems. Education: B.S. (High Honors) in Computer Engineering, Boğaziçi University, 2002 M.S. in Computer Engineering, Boğaziçi University, 2004 Ph.D. in Computer Engineering, Boğaziçi University, 2009 Research interests include: Network Economics and Game Theory Wireless Network Optimization IoT Architectures and Applications Federated Learning and Edge Computing Secure Communication Protocols Recent work trends show strong focus on: Edge caching with machine learning Federated learning frameworks IoT-driven smart systems for waste/warehouse management Game-theoretic models for network resource allocation He has advised 4 theses and led 6 research projects. His lab (insyslab.com.tr) explores next-generation networking solutions.

Minh Quang Taمشاهده پروفایل

استادیار

• Robotics
• Control Theory
• Image Processing/Analysis
• +۳ مورد دیگر

Dr. Minh Quang Ta is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of West Florida (UWF), affiliated with the Hal Marcus College of Science and Engineering. His research focuses on robotics, control theory, image processing/analysis, and micro-manipulation. He holds a PhD in Robotics from Nanyang Technological University (NTU), Singapore (2018), and a BEng in Electrical and Electronic Engineering from Hanoi University of Science and Technology (2013). Dr. Ta has published over 30 articles in top-tier journals and conferences, including work on laser-driven micro-manipulation systems, stochastic control for robotics, and wireless sensor networks for rehabilitation. He has co-authored patents and a book chapter, and has been recognized with awards such as the Best Poster Award Finalist at IROS 2023. His research integrates robotics with biomedical applications, emphasizing precision and automation in micro-scale systems. Before joining UWF, Dr. Ta served as a Research Fellow at the Rehabilitation Research Institute of Singapore and NTU (2018–2021), and held roles as a Lecturer and Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign (2022–2024). He has supervised over 20 students and was honored as an Excellent Teacher by UIUC students in 2023. His contributions span academic service as a conference session chair, technical committee member, and reviewer for top journals.

• System Identification
• Stochastic Hybrid Systems
• Distributed and Data-Based Optimization
• +۳ مورد دیگر

Maria Prandini is a Full Professor at Politecnico di Milano, Italy, and holds a Visiting Professor title. She has held visiting roles at institutions including UC Berkeley, Cambridge University, and ETH Zurich. Her research focuses on control systems, stochastic hybrid systems, and optimization with applications in transportation and energy. She has served in leadership roles at IEEE (CSS Vice-President for Conference Activities, 2016-17) and IFAC (elected President for 2026-29). She is co-General Chair of IEEE CDC 2024 and an associate editor for IEEE Transactions on Network Systems and Automatica. Education: PhD in Information Technology (1998) Postdoctoral Research: UC Berkeley (1998-2000) Her research interests span system identification, multi-agent systems, and distributed optimization. She has organized major conferences like the IEEE Conference on Decision and Control (CDC 2021 as Program Chair). Awards include the IEEE CSS Distinguished Member Award (2018) and elevation to IEEE Fellow (2020). Professional Service: IFAC Vice-President Conferences (2020-23), IEEE CSS Board of Governors (2015-17)

Shiliang Zhangمشاهده پروفایل

پژوهشگر ارشد

• Smart Grid
• Transactive Energy Management
• Privacy Preservation
• +۴ مورد دیگر

Shiliang Zhang is a Postdoctoral Fellow at the University of Oslo working with the Networks and Distributed Systems research group. His research focuses on privacy preservation in smart grid and transactive energy management systems, including the Privacy preserving Transactive Energy Management (PriTEM) project. He teaches Energy Informatics and Artificial Intelligence for Energy Informatics courses while developing interactive visualization tools for Norwegian energy infrastructure. His primary research areas encompass Smart Grid systems, Transactive Energy Management, and Privacy Preservation through Differential Privacy techniques. He applies Artificial Intelligence and Energy Informatics to address renewable energy integration challenges, grid resilience, and autonomous navigation systems. Recent work explores bionic data-driven approaches for underwater navigation and anomaly-resistant control mechanisms. Publications from 2022-2025 reveal strong interdisciplinary focus on machine learning applications for energy systems and navigation. Key themes include privacy-preserving pricing schemes for smart grids, robust control under system uncertainties, and deep reinforcement learning for geomagnetic navigation. His work spans IEEE Transactions, SmartGridComm, and Mathematics journals with significant contributions to resilient energy infrastructure. No scientific awards were mentioned in available sources. Zhang has no listed advisees but actively contributes to the PriTEM project within Networks and Distributed Systems. His research leverages collaborations across energy informatics domains, evidenced by publications in high-impact venues and development of practical tools like Norway's energy consumption and solar panel distribution maps. He operates within the Networks and Distributed Systems research group, developing interactive visualizations for Norwegian energy infrastructure including municipal energy consumption (May 2025), Oslo's solar panel distribution (April 2025), and national power lines (May 2025). His work bridges theoretical control systems with real-world energy applications through the PriTEM project.

Gianluigi Pillonettoمشاهده پروفایل

استادیار

• System Identification
• Stochastic Systems
• Deconvolution Problems
• +۳ مورد دیگر

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 Zorziمشاهده پروفایل

دانشیار

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +۸ مورد دیگر

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 Hajiesmailiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Machine Learning
• Networking and Distributed Systems
• +۵ مورد دیگر

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
• +۶ مورد دیگر

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 Thiebauxمشاهده پروفایل

استاد

• Artificial Intelligence
• Energy Systems
• Optimization
• +۵ مورد دیگر

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
• +۵ مورد دیگر

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 Kilgourمشاهده پروفایل

استاد

• Game Theory
• Decision Analysis
• Conflict Resolution
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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 Okumuraمشاهده پروفایل

استاد مهمان

• Multi-Agent Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

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.