جست‌وجوی استاد و پژوهشگر

• Optimization
• Complex Networks
• Statistical Inference
• +۳ مورد دیگر

Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.

Anwar Hithnawiمشاهده پروفایل

استادیار

• Data Privacy
• Applied Cryptography
• Privacy-Preserving Systems
• +۵ مورد دیگر

Anwar Hithnawi is an Assistant Professor of Computer Science at the University of Toronto, where he leads the Privacy Preserving Systems Lab (PPS Lab). His research focuses on data privacy, applied cryptography, and secure systems, with emphasis on privacy-preserving machine learning, federated learning, and encrypted data processing. He holds a Ph.D. in Computer Science from ETH Zurich and was a postdoctoral researcher at UC Berkeley. Previously, he served as an Ambizione Fellow and research group leader at ETH Zurich. Research Interests: Data Privacy & Security Applied Cryptography (Homomorphic Encryption, Zero-Knowledge Proofs) Privacy-Preserving Systems (Federated Learning, Secure Analytics) IoT Security & Privacy Secure Collaborative Learning Awards: Google Research Award SNF Ambizione Grant ETH Medal for Outstanding Master Thesis (student Lukas Burkhalter) Microsoft Research Ph.D. Award (student Lukas Burkhalter) Lab Activities: The PPS Lab develops systems for privacy-preserving computation, secure collaborative learning, and encrypted data stream processing. Notable projects include Zeph, HECO, and Cohere. Recent achievements include acceptance of DPolicy at IEEE S&P 2025 and RoFL at Oakland 2023.

Wei Zhangمشاهده پروفایل

استاد

• Control Theory
• Optimization Theory
• Machine Learning
• +۵ مورد دیگر

Wei Zhang is a tenured Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, and a Senior Member of IEEE. He serves as Associate Editor for IEEE Transactions on Control System Technology and leads the CLEAR Lab (Control & Learning for Robotics and Autonomy) within the School of Automation and Intelligent Manufacturing (AiM). His career spans institutions including the University of California, Berkeley (postdoc), and The Ohio State University (Assistant/Associate Professor). Education: PhD in Electrical Engineering from Purdue University (2009), MS in Electrical and Computer Engineering from University of Kentucky (2005), BS in Automation from University of Science and Technology of China (2003) Research Interests focus on integrating control theory, optimization, and machine learning to develop intelligent systems. Key areas include: Legged Robots: Dynamic locomotion control, bio-inspired gait design, and push recovery mechanisms Autonomous Systems: Real-time motion planning, obstacle avoidance, and safe navigation in adversarial environments Smart Grids: Distributed control for energy systems and transactive energy optimization Machine Learning: Reinforcement learning for robotics, Q-learning convergence analysis, and hybrid control-learning frameworks Publication Trends highlight interdisciplinary work at the intersection of robotics and control systems. His recent 2024 papers address: Whole-body control for wheeled-quadrupedal robots Geometric object pose refinement in computer vision Task-space Riccati feedback for underactuated systems Teacher-student reinforcement learning architectures Scientific Awards include: 2016 : NSF CAREER Award 2015 : Lumley Research Award (Ohio State University) 2013 : AFOSR Summer Faculty Fellowship 2018 : National Distinguished Expert (Young, China) 2019 : International Underwater Robot Competition 2nd Prize (team advisor) Academic Leadership involves editorial roles at IEEE Transactions on Control System Technology and IEEE Transactions on Power Systems. His lab provides state-of-the-art robotics platforms including quadruped robots, Kuka manipulators, and UAVs for algorithm validation. Research collaborators span The Ohio State University , UC Berkeley , CMU , and The University of Hong Kong .

Alessandro Golkarمشاهده پروفایل

استاد

• Space Systems Engineering
• CubeSat and Nanosatellite Technology
• Satellite Constellations
• +۵ مورد دیگر

Prof. Alessandro Golkar is a Professor at the Technical University of Munich (TUM), leading the Chair of Picosatellites, Nanosatellites, and Satellite Constellations. He joined TUM in September 2022 and previously served as one of the founding faculty members at Skoltech, a Moscow-based graduate research university. His research focuses on advanced space mission concepts, systems engineering for picosatellites, and federated satellite systems. Prior to academia, he held roles at Airbus CTO, contributing to technology roadmapping and planning. His academic background includes expertise in aerospace engineering, systems design, and agile development methodologies for space hardware. Key research areas include CubeSat constellations, distributed satellite systems, and the integration of AI tools like Large Language Models (LLMs) into spacecraft design processes. He has pioneered projects such as the FSSCat mission, winner of the ESA Sentinel Small Satellite Challenge, and has explored applications of additive manufacturing for lunar missions. Prof. Golkar’s awards include the 2021 Karman Fellowship and IEEE Senior Membership (2018). His recent work emphasizes optimizing satellite networks, digital twin implementation, and orbital maneuvering for collision avoidance. He actively contributes to technology roadmapping, focusing on future human landing systems and lunar infrastructure development. Education: Ph.D. in Aerospace Engineering (details not explicitly stated). Grants & Funding: Extensive grants for CubeSat projects, federated systems research, and space technology innovation. Labs/Teams: Leads the Chair’s research group at TUM and collaborates with industry partners like Airbus on advanced mission concepts. His publications span over two decades, addressing topics like constellation design, machine learning in space, and agile processes for hardware development. He advocates for hybrid agile methodologies to bridge traditional systems engineering and modern product development.

Harwin de Vriesمشاهده پروفایل

دانشیار

• Health & Humanitarian Logistics
• Disaster Relief Logistics
• Healthcare Access in LMICs
• +۳ مورد دیگر

Dr. Harwin de Vries is an Associate Professor at the Department of Technology and Operations Management, Rotterdam School of Management (RSM), Erasmus University Rotterdam. His research focuses on health and humanitarian logistics, particularly disaster relief logistics and improving access to essential medicines in low- and middle-income countries (LMICs). He explores the role of data analytics and new operational models in healthcare systems, such as mobile health service delivery. Before joining RSM, he worked at INSEAD as a postdoctoral researcher and manager of the INSEAD Humanitarian Research Group. He holds a PhD in Operations Research from Erasmus University. Education: PhD in Operations Research, Erasmus University Rotterdam His research interests include optimizing supply chain decisions in humanitarian contexts, with over 20 collaborations with health organizations. He teaches courses on Health & Humanitarian Logistics, Business Analytics, and Spreadsheet Modelling across BSc, MSc, and EMBA programs. Dr. de Vries has received notable awards including the 2021 ERIM Award for Young Researcher and a 2022 NWO VENI Grant. His work has been published in leading journals like Production and Operations Management and Manufacturing & Service Operations Management, and has been transformed into teaching cases. Grants: NWO VENI Grant (2022) He serves on the board of the POMS College of Humanitarian Operations & Crisis Management, actively contributing to academic and practical solutions in humanitarian logistics.

Yang Zhouمشاهده پروفایل

دانشیار

• Big Data Algorithms
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Yang Zhou is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, part of the Samuel Ginn College of Engineering. His research focuses on big data algorithms, machine learning, data mining, and distributed computing. He has contributed to advancements in federated learning frameworks, graph mining tools, and spatial machine learning for environmental applications like flood mapping. Education includes a Ph.D. in Computer Science from Georgia Tech (2021), M.E. in Computer Application Technology from Chongqing University (2016), and B.E. in Engineering from Jiangnan University (2014). His work emphasizes scalable algorithms for large-scale systems, with tools like DirDense for dense subgraph mining and FedASMU for federated learning optimization. Recent publications explore adversarial robustness, blockchain strategies in IoT, and curriculum-based learning for large language models. He advises on interdisciplinary projects at the intersection of AI and environmental science.

Kevin M. Lynchمشاهده پروفایل

استاد

• Robotics
• Dynamics
• Motion planning
• +۷ مورد دیگر

Kevin M. Lynch is a Professor of Mechanical Engineering at Northwestern University's McCormick School of Engineering, where he also serves as Director of the Center for Robotics and Biosystems. His research spans multiple domains of robotics, with particular expertise in dynamics, motion planning, and feedback control of mechanical systems. Dr. Lynch received his Ph.D. in Robotics from Carnegie Mellon University in 1996, with a thesis on "Nonprehensile Robotic Manipulation: Controllability and Planning" under advisor Prof. Matthew T. Mason. He earned his B.S.E. with honors in Electrical Engineering from Princeton University in 1989. His research interests focus on robotics, particularly dynamics, motion planning, and feedback control of mechanical systems. He investigates mechanics, planning, and control of robotic manipulation (juggling, throwing, pushing, rolling, vibration, etc.) and locomotion. His work also explores self-organizing systems, particularly decentralized control of mobile sensor networks and swarm robotics, underactuated dynamic systems, and physical human-robot interaction with industrial applications. Recent research has expanded into bio-inspired active electrosense, underwater robotics, control and optimization for robot swarms, swarm shape control, and functional electric stimulation. Dr. Lynch's recent publications demonstrate a strong trend toward rehabilitation robotics and human-robot interaction, particularly in lower-limb exoskeletons for gait training and rehabilitation. His work bridges fundamental robotics research with practical applications in healthcare, showing increasing integration of swarm robotics principles with human-centered design. The publications also reveal continued strong contributions to fundamental robotics theory, particularly in swarm formation control and manipulation dynamics. George Saridis Leadership Award in Robotics and Automation (2022) Harashima Award for Innovative Technologies (2017) IEEE Fellow (2010) Charles Deering McCormick Professor of Teaching Excellence (2007-10) Society of Automotive Engineers Ralph R. Teetor Educational Award (2007) Early Career Award in Robotics and Automation (2001) McCormick School of Engineering and Applied Science Teacher Of The Year Award (1998-1999) NSF Career Award (1998) As Director of the Center for Robotics and Biosystems, Dr. Lynch oversees significant research grants and initiatives in robotics. He has made substantial contributions to robotics education through his "Modern Robotics" book and associated Coursera specialization, which has reached thousands of students worldwide. His professional service includes serving as Editor-in-Chief of IEEE Transactions on Robotics, where he oversaw a 90% increase in submissions during his tenure. Dr. Lynch leads research groups focusing on swarm robotics and rehabilitation robotics, with particular emphasis on the Center for Robotics and Biosystems at Northwestern University. His teams integrate expertise from mechanical engineering, electrical engineering, computer science, and rehabilitation medicine to develop innovative robotic systems for both industrial applications and healthcare solutions.

Vassilis Zikasمشاهده پروفایل

دانشیار

• Cryptography
• Blockchain Technologies
• Cryptocurrencies
• +۵ مورد دیگر

Vassilis Zikas is an Associate Professor at the School of Cybersecurity and Privacy and holds a courtesy appointment in the School of Computer Science at Georgia Tech. He previously held positions at Purdue University, the University of Edinburgh, and Rensselaer Polytechnic Institute (RPI). His research focuses on cryptography, blockchain technologies, cryptocurrencies, game theory, and distributed computing. He leads the Blockchain Lab at Georgia Tech and serves as Chief Scientist at Sunday Group, where he contributes to the Mobby blockchain architecture. His work is supported by the NSF, DoD, Swiss NSF, and industry partners like Sunday Group and Algorand Foundation. He has organized major conferences including PKC 2020 and has served on program committees for EUROCRYPT, CRYPTO, and TCC. He advises PhD students in cryptography and related fields and has mentored postdocs and master’s students. Zikas’ publications span topics like secure multi-party computation (MPC), blockchain security, and cryptographic protocol design. His recent work includes advancements in privacy-preserving protocols, adaptive security frameworks, and composability in blockchain systems. Notable awards include the 2023 Leadership Award from Purdue College of Science and an AnlytiXIN Fellowship. He actively contributes to both theoretical and applied aspects of cybersecurity and privacy through academic and industrial collaborations.

Phuong H. Nguyenمشاهده پروفایل

دانشیار

• Smart Energy Systems
• Power System Control
• Distributed Energy Resources
• +۳ مورد دیگر

Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.

Hossam Hassaneinمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Mobile Ad hoc Networks
• Edge Computing
• +۵ مورد دیگر

Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.

Qing Cindy Changمشاهده پروفایل

استاد

• Cyber-Physical Systems for Smart Manufacturing
• Real-time Production Control
• Knowledge-guided Machine Learning based Control
• +۲ مورد دیگر

Qing (Cindy) Chang is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia, where she directs the Intelligent Systems Lab. She joined UVA in 2019 after serving as an associate professor at Stony Brook University. Prior to academia, she spent a decade at General Motors R&D, receiving their highest innovation awards. Education: M.S. from University of Wisconsin-Madison Ph.D. in Manufacturing from University of Michigan Research Focus: Chang's work integrates math-based modeling and data-driven methods to optimize manufacturing systems. Key areas include: Adaptive control and machine learning for production efficiency Human-robot collaboration frameworks Sustainable manufacturing through energy management Real-time control of cyber-physical production systems Reinforcement learning applications in industrial automation Research Trends: Her recent publications (2024-2025) demonstrate strong focus on AI-driven manufacturing optimization, with 80% leveraging reinforcement learning/LLMs for robotic control. Key themes include multi-agent coordination (67% of papers), energy efficiency (53%), and flexible production systems (47%). Awards & Recognition: Inducted as SME Scholar (2024) 20 Most Influential Professors in Smart Manufacturing - SME (2020) NSF CAREER Award (2014) Three-time GM Boss Kettering Award winner (2005,2006,2008) ASME and SME Fellow Leadership & Funding: Serves on NAMRI/SME Board of Directors with editorial roles across ASME/IEEE/SME journals. Research supported by NSF (including CAREER), Department of Energy, and multiple industry partners. Leads projects on human-robot collaboration and sustainable manufacturing. Lab & Collaboration: Directs the Intelligent Systems Lab at UVA, focusing on industrial AI applications. Collaborates with automotive and energy sectors to translate research into practical solutions for smart factories.

Sean Foleyمشاهده پروفایل

استاد

• Cryptocurrencies
• Fintech
• Market Design
• +۴ مورد دیگر

Sean Foley is a Professor of Applied Finance at Macquarie University, specializing in Fintech, Cryptocurrencies, Trading, and Market Design. He leads the Decentralized Assets division at the Digital Finance Cooperative Research Centre (DFCRC), bridging academia, industry, and government. His research focuses on blockchain applications like automated market makers, DeFi protocols, and stablecoins. Education: PhD in Finance from the University of Sydney (2014), focusing on 'The Impact of Regulation on Market Quality'. Research Interests: Decentralized finance (DeFi) systems Cryptocurrency market dynamics and regulation Market microstructure and liquidity provision Energy market crises and policy Regulatory frameworks for financial markets Key Projects: Leading the DFCRC's Industrial PhD Scholarships (2021–2031), mentoring students like Arvind Rangarajan and Juuso Artturi Itkonen. Research on Australia's National Electricity Market (NEM) suspension and energy policy. Awards: Best Paper Award at the Cryptocurrency Conference (2019) Philip Brown Prize for Best Australian Paper (2021) Exceptional Research Prize (2019) Advising & Grants: Supervised over 10 PhD students through DFCRC scholarships. Secured $181 million for the Digital Finance CRC. Lead applicant in the Gunns Ltd shareholder class action. Labs/Teams: Head of Decentralized Assets at DFCRC, collaborating on policy and technology. Co-researcher on 'Electricity Markets in Crisis' and cryptocurrency illicit use studies.

Xiaoxiao Liمشاهده پروفایل

استادیار

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +۷ مورد دیگر

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

Andrea Bonfiglioمشاهده پروفایل

دانشیار

• Power Systems Analysis
• Smart Grids
• Renewable Energy Integration
• +۵ مورد دیگر

Andrea Bonfiglio is an Associate Professor at the Department of Naval, Electrical, Electronic and Telecommunication Engineering (DITEN) within the School of Engineering at the University of Genoa. His work focuses on power systems, smart grids, and renewable energy integration with a particular emphasis on innovative control strategies and energy storage solutions. Teaching areas: Electrical Systems, Industrial Measurements, Energy Security Key research themes: Smart distribution networks, synthetic inertia, microgrid control, vehicle-to-grid technologies, battery energy storage Recent work explores virtual energy partitioning, load flow optimization, and inertia allocation in transmission networks Contact: a.bonfiglio@unige.it His publications highlight advanced control methodologies using machine learning and sliding mode control for both transmission and distribution networks, with applications to renewable integration and grid stability challenges.

Rida Khatounمشاهده پروفایل

استاد

• Cybersecurity
• Network Security
• Cloud Computing Security
• +۶ مورد دیگر

Rida Khatoun is a Professor in Cybersecurity at the Computer Sciences and Networks (Infres) Department at Télécom Paris. He holds a M.Sc. in Computer Engineering and a Ph.D. from the University of Technology of Troyes (UTT), France, awarded in 2004 and 2008, respectively. His research focuses on cybersecurity in networks, including cloud computing security, IoT security, vehicular networks security, intrusion detection systems, and blockchain technology. He has taught at Telecom Paris since 2014, Shanghai Jiao Tong University (SJTU) since 2016, and other institutions in China and France since 2005. His work spans theoretical frameworks and practical solutions for network security challenges. Key research areas include DDoS attack detection, vehicular communication security, and cryptographic protocols. He leads the Cybersecurity and Cryptography (C²) research team and is affiliated with the Laboratoire Traitement et Communication de l'Information (LTCI). His contributions include developing secure routing protocols (e.g., ASROP), statistical trust systems in wireless networks, and blockchain-based authentication for IoT. He has authored over 115 publications, including peer-reviewed articles and conference proceedings, with recent work addressing vehicular platooning security, TLS handshake optimization for C-ITS, and machine learning for cyberbullying detection. Rida’s academic activities include teaching courses on network security protocols, wireless network fundamentals, and QoS mechanisms. He collaborates internationally, contributing to smart city cybersecurity architectures and 6G programmable communications. His research emphasizes practical cybersecurity solutions for emerging technologies, ensuring robust protection against evolving threats in vehicular, IoT, and cloud environments.