جست‌وجوی استاد و پژوهشگر

• Wave Energy Conversion
• System Identification
• Optimal Control
• +۶ مورد دیگر

Mauro Bonfanti is a Fixed-term Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino, Italy. His academic work focuses on wave energy conversion systems, mechatronics, and applied mechanics within the field of industrial and information engineering. Dr. Bonfanti's primary research interests include wave energy conversion systems, system identification, optimal control, mechatronics, and renewable energy systems. His work centers on developing advanced control strategies for wave energy converters, with particular emphasis on improving energy extraction efficiency through innovative mechanical designs and control algorithms. He has made significant contributions to the understanding of wave-structure interactions, hydrodynamic modeling of floating bodies, and optimization of wave energy converter systems under various sea conditions. His research bridges theoretical modeling with practical implementation, often involving high-fidelity numerical simulations validated through experimental testing. His publication record demonstrates a strong focus on advancing wave energy conversion technology, with particular emphasis on control systems, hydrodynamic modeling, and optimization techniques. The research shows progression from fundamental modeling approaches to increasingly sophisticated control strategies and system integration. Recent work has expanded to include hybrid renewable energy systems that combine wave and wind energy technologies. Dr. Bonfanti serves as a Scientific Responsible for several research projects including OCEANGLIDE, AQUALEV, and WISE, which focus on innovative wave energy conversion technologies. He holds multiple patents related to wave energy conversion systems, including the WISE (Water-air Injectable Swath Elevator) and AQUALEV magnetic support systems. He actively supervises PhD students including Alessandro Brusasco, Matteo Mastorakis, Francesco Balestrieri, and Domenico Edoardo Sfasciamuro, guiding research in sustainable materials, mechanical engineering, and wave energy conversion systems. His teaching responsibilities include courses on System Identification and Optimal Control of Wave Energy Conversion Systems, as well as Mechatronics across multiple academic years. Dr. Bonfanti is a member of the Mechatronics and Servosystems research group at DIMEAS, where he collaborates on projects related to marine renewable energy systems and advanced control technologies.

Gareth Taylorمشاهده پروفایل

استاد

• Power Systems
• Smart Grids
• Renewable Energy Systems
• +۷ مورد دیگر

Professor Gareth Taylor is a Professor of Power Systems and Director of the Brunel Interdisciplinary Power Systems (BIPS) Research Centre at Brunel University London's College of Engineering, Design and Physical Sciences. He serves as Module Leader for the MSc Sustainable Electrical Power program and has been actively involved with the university since May 2000, progressing from National Grid Post-doctoral Scholar to his current position as Professor (appointed in 2012). He previously served as Head of the Department of Electronic and Electrical Engineering from May 2019 to June 2023 and holds a Visiting Professor position at Imperial College London (2023-2026). Professor Taylor earned his BSc in Applied Physics from Royal Holloway College, University of London (1987), followed by an MSc in Scientific and Engineering Software Technology from the University of Greenwich (1992), and completed his PhD in Computational Solid Mechanics at the University of Greenwich in March 1997. His doctoral research focused on finite volume methods for material non-linearity within multi-physics frameworks. His research spans power systems engineering with particular emphasis on smart grid technologies, renewable energy integration, and advanced computational methods. Professor Taylor has contributed to over 250 research publications in areas including power system operation and management, reactive power control, voltage regulation, and high-performance computing applications in electrical power systems. His work addresses critical challenges in modern power systems, particularly those related to the integration of renewable energy sources and the development of more resilient grid infrastructure. Analysis of his recent publications reveals a strong focus on addressing contemporary power system challenges, particularly the integration of renewable energy sources, smart grid technologies, and advanced computational methods. His work spans from fundamental power system analysis to practical applications in grid operation, with increasing emphasis on cybersecurity aspects of power system monitoring and the challenges posed by reduced system inertia in grids with high renewable penetration. Senior Member of IEEE Fellow of the Institute of Engineering and Technology (FIET) Chartered Engineer Fellow of the Higher Education Academy (FHEA) UK Regular Member for CIGRE Study Committee D2 (2016-2022) Member of Strategic Advisory Group for CIGRE Study Committee D2 (2023) Professor Taylor has led numerous significant research projects including TDX-ASSIST (€5.2M), e-HIGHWAY2050 (€8.2M), and HiPerDNO (€5.4M), with funding from EPSRC, European Commission, National Grid, and other major organizations. His current research portfolio includes projects on novel decoupled active/reactive power oscillation response, digitalization of power systems operation, and examining net zero policy in European energy markets. He also directs the BIPS Research Centre, which focuses on interdisciplinary power systems research with strong industry connections.

Howard Schwartzمشاهده پروفایل

استاد

• Machine Learning
• Adaptive Control
• Multi-Agent Machine Learning
• +۳ مورد دیگر

Professor Howard Schwartz is a distinguished academic in Electrical and Computer Engineering at Carleton University , with a career spanning academia, industry, and robotics research. He earned his BEng in Civil Engineering from McGill University (1982), followed by MSc (1984) and PhD (1986) in Aerospace and Mechanical Engineering from MIT. His academic leadership includes serving as Department Chairman (2009-2013) and authoring the seminal text Multi-Agent Machine Learning: A Reinforcement Approach (Wiley, 2014). As an Associate Editor for IEEE Transactions on Cybernetics , he shapes discourse in cybernetic systems and machine learning. Research Focus Professor Schwartz's research bridges theoretical and applied domains: Machine learning for robotics and autonomous systems Adaptive control theory and multi-agent systems Reinforcement learning algorithms with fuzzy logic integration Real-time video analytics and GPS receiver development Nonlinear control systems for UAVs and wind energy optimization Industry Experience His career includes impactful industry engagements: Early development of high-performance GPS receivers at Canadian Marconi Co. (1982-1984) Sabbatical at March Networks (2001-2002) for video analytics Software quality control at CMC Electronics (2008) Software development for TV set-top boxes at Espial Inc. (2014-2015)

Marta Zanioloمشاهده پروفایل

استادیار

• Water Resources Planning and Management
• Drought Modeling
• Climate Change Adaptation
• +۵ مورد دیگر

Marta Zaniolo serves as an Assistant Professor in Civil and Environmental Engineering at Duke University since January 2024, addressing critical water sustainability challenges through integrated hydrology, machine learning, and systems modeling approaches. Her work focuses on ensuring equitable water access amid climate change, scarcity, and competing demands across diverse spatial scales. Her academic foundation includes: B.S. in Environmental Engineering from Politecnico di Milano (2014) M.S. in Environmental Engineering from Politecnico di Milano (2017) Ph.D. in Information Technology (Environmental Intelligence Lab) from Politecnico di Milano (2020) Postdoctoral Research at Stanford University (2020-2023) Dr. Zaniolo's research integrates water resources planning , drought modeling , and climate adaptation with cutting-edge machine learning techniques including reinforcement learning and feature extraction. The ZEDD Lab develops computational models that synthesize climate, hydrological, and socio-economic data to enhance decision-making for water security. Her work emphasizes real-world applicability through stakeholder engagement and participatory design, particularly in vulnerable regions like the Omo-Turkana basin where mismanaged infrastructure operations can trigger socio-ecological conflicts. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) development of synthetic weather generators (e.g., FIND model) for precise drought characterization, (2) neuro-evolutionary algorithms for multi-objective reservoir control under climate uncertainty, and (3) quantification of flexibility value in water infrastructure planning. These studies consistently bridge technical innovation with equity considerations, particularly in transboundary water systems. She leads the Zaniolo Lab for Environmental Data and Decisions (ZEDD Lab), which operates at the intersection of hydrology, systems modeling, and machine learning. The lab's framework combines computational water system dynamics with stakeholder-inclusive approaches to develop decision support tools for extreme event preparedness. Current projects focus on urban drought resilience, dam reoperation strategies for climate extremes, and heat-substance use risk intersections in rural communities, all grounded in case studies spanning California, Ethiopia, and the American South.

• Neuroscience
• Brain Networks
• Functional Connectivity
• +۴ مورد دیگر

Richard F Betzel is an Associate Professor in Neuroscience at University of Minnesota and affiliated with Indiana University and Northwestern University through collaborative research grants. As a leading network neuroscientist, he develops edge-centric approaches for analyzing brain network organization and dynamics. Current affiliations: University of Minnesota (primary), Indiana University (collaboration), Northwestern University (collaboration) Active research grants from: National Science Foundation (NSF), NIH National Institute on Aging, NIH National Institute of Neurological Disorders & Stroke His research focuses on: Understanding brain network reconfiguration during aging and cognitive tasks Developing computational tools for analyzing connectome architecture Exploring relationships between structural and functional connectivity Investigating network mechanisms in neurodegenerative disorders like Parkinson's Advancing mindfulness meditation research through network neuroscience Recent publications emphasize: Edge-centric network analysis methods Connectome organization across species Dynamic network approaches to social cognition Functional MRI analysis of co-fluctuations Network controllability and modular architecture Applications to both healthy aging and pathological conditions His work contributes to UN Sustainable Development Goals including healthy aging (SDG 3) and scientific knowledge advancement. Current projects include NCS-FO for edge-centric brain mapping (NSF-funded), social cognitive aging research (NIH), and TMS therapy network analysis (NIH).

Michael K. Reiterمشاهده پروفایل

استاد

• Computer Security
• Distributed Systems
• Applied Cryptography
• +۳ مورد دیگر

Michael K. Reiter is a Professor at Duke University in the Department of Computer Science and Department of Electrical & Computer Engineering . He received his Ph.D. in Computer Science from Cornell University (1993) and held academic positions at Carnegie Mellon University and UNC Chapel Hill. Research Focus: Computer and network security, distributed systems, and applied cryptography. Notable Contributions: Pioneering work in Byzantine fault tolerance, malware detection, privacy-preserving protocols, and human-verifiable authentication. Leadership: Founding Technical Director of CyLab at Carnegie Mellon, program committee roles at top security conferences. Recent Publications emphasize: Machine learning security (adversarial training, backdoor detection) Blockchain consensus mechanisms (asynchronous BFT, random beacons) Privacy-enhancing technologies (differential privacy, private set intersection) Side-channel mitigation in cloud environments Awards & Recognition: ACM Fellow (2008) James B. Duke Distinguished Professor

Hideyuki Suzukiمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Hybrid Systems
• Many-Body Dynamics
• +۵ مورد دیگر

Hideyuki Suzuki is a Professor at the Department of Information and Physical Sciences , Graduate School of Information Science and Technology , Osaka University , where he has been employed since April 2016. His research spans nonlinear dynamics , hybrid systems , and many-body dynamics , with applications to power systems , brain modeling , and epidemic networks . 2001 : Ph.D. in Mathematical Engineering and Information Physics, University of Tokyo 1998 : M.Eng. in Mathematical Engineering and Information Physics, University of Tokyo 1996 : B.Sc. in Mathematics, University of Tokyo His research interests focus on nonlinear dynamical systems , particularly those with discontinuities or large-scale interactions , such as chaotic billiards , hybrid systems , and spatio-temporal chaos . He explores computational applications in machine learning (e.g., chaotic Boltzmann machines ), epidemiology (e.g., vaccine allocation models ), and power grid stability . The 15 most recent articles highlight his work in photonic computing , nonlinear sampling algorithms , and chaotic dynamics in engineering and biology . Key trends include interdisciplinary applications of nonlinear mathematics to renewable energy , neuroscience , and epidemic spread . Scientific awards include: 2022 Osaka University Prize 2018 JSIAM Best Paper Award for Hamiltonian Monte Carlo (2017) He leads the Nonlinear Mathematics Course laboratory, which accepts graduate and undergraduate students. His team investigates hybrid dynamical systems , chaotic computation , and real-world modeling in fields like traffic dynamics and epidemic networks . Research is supported by grants from JST CREST and ALCA-Next programs.

Eli Ben-Michaelمشاهده پروفایل

استادیار

• Causal Inference
• Statistical Methods
• Computational Methods
• +۵ مورد دیگر

Eli Ben-Michael is an Assistant Professor in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He is also affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, he was a Post-Doctoral Fellow at Harvard University in the Institute for Quantitative Social Science and the Department of Statistics. Ben-Michael's research focuses on developing statistical and computational methods to solve practical issues in public policy and social science research. His work brings together ideas from statistics, optimization, and machine learning to create methods for credible and robust causal inference and data-driven decision making. His research spans multiple domains including healthcare policy, criminal justice reform, reproductive health, and education. His scholarly work shows a strong emphasis on causal inference methodology, with particular attention to synthetic control methods, balancing weights, policy learning, and sensitivity analysis. His recent publications demonstrate applications across diverse fields including healthcare, economics, social policy, and criminal justice. Ben-Michael completed his PhD in Statistics from UC Berkeley and earned his undergraduate degree in Computer Science and Statistics from Columbia University. His technical expertise includes developing open-source software, with contributions to the R packages 'augsynth' and 'multical' for synthetic control methods and multilevel calibration.

Jiabao Heمشاهده پروفایل

پژوهشگر

• Control Theory
• System Identification
• Information Theory
• +۵ مورد دیگر

Jiabao He is a Ph.D. Student in the Division of Decision and Control Systems at KTH Royal Institute of Technology , supervised by Prof. Håkan Hjalmarsson. He holds a Master's degree in Control Engineering (2021) and a Bachelor's degree in Mechanical Engineering (2018) from Tsinghua University. Education M.S. in Control Engineering (Tsinghua University, 2021) B.S. in Mechanical Engineering (Tsinghua University, 2018) His research interests lie in systems, control theory, and information theory , with current focus on subspace identification, finite-sample analysis , and data-driven control . Past work explored fuzzy control and descriptor systems . His publications reflect expertise in subspace methods , statistical learning , and Markov parameter identification . Conference Participation includes Reglermötet 2025 (Lund) , 63rd IEEE CDC 2024 (Milan) , 32nd ERNSI Workshop 2024 (Venice) , and SYSID 2024 (Boston) . He is a reviewer for journals and conferences like IEEE Transactions on Automatic Control and IFAC Symposium on System Identification . Scientific Awards IEEE TC SIAC Student Paper Award (2025) Travel Scholarship from Karl Engvers Foundation (2024) Travel Scholarship from Björns stiftelse (2024) He has served as a Teaching Assistant for courses such as EL1020 Automatic Control and EL2520 Control Theory and Practice , and supervised Bachelor theses on Learning in Dynamical Systems (2023-2024). His work bridges theoretical control methods with practical applications in descriptor systems and space robotics.

• Networked Epidemic Modeling
• Control Theory
• Machine Learning
• +۴ مورد دیگر

Philip E. Paré is the Rita Lane and Norma Fries Assistant Professor of Electrical and Computer Engineering at Purdue University's College of Engineering. His research focuses on networked epidemic modeling, control theory, and data-driven systems analysis, with applications to public health and product demand dynamics. Research Areas: Networked Epidemic Modeling, Control Theory, Machine Learning, Privacy in Cyber-Physical Systems Recent work explores multi-virus spread, differential privacy in epidemic models, and optimal curing strategies. A 2023 NSF CAREER Award recipient, he develops frameworks for counterfactual analysis and feedback control in societal-scale challenges. Scientific Awards: NSF CAREER Award (2023)

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Eunji Limمشاهده پروفایل

دانشیار

• Operations Management
• Supply Chain Management
• Business Analytics
• +۲ مورد دیگر

Eunji Lim is an Associate Professor in the Department of Decision Sciences and Marketing at the Robert B. Willumstad School of Business, Adelphi University, located in Garden City, New York. She has been a faculty member at Adelphi since 2018, initially as an Assistant Professor and promoted to Associate Professor in 2023. Her prior academic appointments include Assistant Professor roles at Kean University (2013–2018) and the University of Miami (2008–2013). Education Ph.D. in Operations Research (implied by department) , Stanford University (2008) Research Interests Lim’s research lies at the intersection of operations research, statistics, and management science. She focuses on simulation-based decision making for operations and supply-chain systems, leveraging large-scale stochastic simulation to optimize complex processes. A second major theme is nonparametric function estimation under shape constraints , where she develops statistically rigorous methods to estimate convex, monotonic, or smooth functions from noisy data. These methodologies find applications in business analytics, revenue management, inventory control, and engineering design. Publication Trends Her recent articles (2013–2025) reveal a consistent emphasis on convex and isotonic regression , simulation analytics , and stochastic optimization . A clear trajectory emerges from theoretical foundations—convergence rates, consistency, and asymptotic distributions—toward practical algorithms for high-dimensional and noisy environments. Interdisciplinary impacts are evident in publications targeting both statistics and operations-research audiences, with cross-cutting keywords such as simulation optimization, shape-restricted inference, and robust estimation. Scientific Awards No specific awards or fellowships are listed in the provided materials. Advising & Grants No information on current or former PhD/Master’s students, postdocs, or funded research grants is provided. Labs & Teams No laboratory or research-group affiliations beyond her departmental appointment are mentioned.

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

Ayan Mukhopadhyayمشاهده پروفایل

پژوهشگر

• Multi-Agent Systems
• Cyber-Physical Systems
• Smart Cities
• +۵ مورد دیگر

Dr. Ayan Mukhopadhyay serves as a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. Previously, he was a Post-Doctoral Research Fellow at Stanford Intelligent Systems Lab where he received the 2019 CARS post-doctoral fellowship. His academic journey includes a Ph.D. from Vanderbilt University's Computational Economics Research Lab with a doctoral thesis nominated for the Victor Lesser Distinguished Dissertation Award 2020. His research spans critical domains in smart infrastructure systems with particular focus on: Developing robust decision-making frameworks for cyber-physical systems under uncertainty Creating multi-agent solutions for emergency response optimization Designing machine learning approaches for urban mobility and energy management Building proactive incident detection pipelines using heterogeneous data sources Analysis of his recent publications reveals strong thematic continuity in applying artificial intelligence to real-world infrastructure challenges, particularly in transportation systems, emergency response, and energy management. His work consistently bridges theoretical AI advances with practical implementation in smart city contexts, demonstrating expertise in both algorithmic innovation and systems integration. Award highlights include: CARS Post-Doctoral Fellowship (2019) Best Paper Award at ICLR's AI for Social Good Workshop Victor Lesser Distinguished Dissertation Award Nomination (2020) Dr. Mukhopadhyay leads significant research initiatives through ScopeLab, focusing on creating deployable solutions for public transit, emergency response, and energy systems. His work on vehicle-to-building charging, traffic incident localization, and equitable transit network design demonstrates commitment to solving high-impact urban challenges through rigorous computational methods. Current projects involve developing simulation environments for non-stationary environments (NS-Gym) and explainable planning frameworks integrating formal logic with large language models.

Simon Sponbergمشاهده پروفایل

دانشیار

• Neuromechanics
• Biomechanics
• Insect Flight
• +۴ مورد دیگر

Simon Sponberg is the Dunn Family Associate Professor at Georgia Institute of Technology, holding joint appointments in the School of Physics and School of Biological Sciences within the College of Sciences. He directs the Agile Systems Lab and serves as Physics & Biological Sciences Director. His research bridges physics, biology, and engineering to understand the principles of animal locomotion. Dr. Sponberg received his Ph.D. in Integrative Biology from UC, Berkeley and completed postdoctoral research at the University of Washington. His academic journey began with undergraduate studies at Lewis & Clark College, where he first explored biomechanics research focusing on gecko adhesion. Dr. Sponberg's research centers on neuromechanics - an integrative science examining how physics and physiology enable animals to achieve remarkable stability and maneuverability. His work specifically investigates insect flight mechanics, particularly in hawkmoths (Manduca sexta), exploring how nervous systems interact with muscle mechanics to produce locomotion. Key research areas include: Mechanisms of Maneuverability: How animals maintain stable flight during perturbations Sensing in Complex Environments: Multisensory integration of vision and mechanosensation Multiscale Physics of Muscle: How muscle structure relates to function across scales Evolution of Flight: Comparative studies of different insect flight strategies His publication record reveals a strong focus on the intersection of biomechanics, neuroscience, and physics, with recent work emphasizing resonant mechanics in insect flight, precise neural control of movement, and multisensory integration for robust performance across varying environmental conditions. A notable trend is the integration of experimental biology with computational modeling and robotics to extract general principles of movement. Dr. Sponberg's scientific achievements have been recognized with numerous awards and fellowships: Hertz Fellow (since 2002) National Science Foundation Fellowships American Physical Society Awards Society of Integrative and Comparative Biology Awards Woods Hole Marine Biological Institute Fellowships University of California Fellowships International Association of Physics Students Awards As an advisor, Dr. Sponberg mentors a diverse team of graduate students and postdoctoral researchers through the Quantitative Biosciences Graduate Program, Neuroscience and Neurotechnology Graduate Program, and Bioengineering Graduate Program. His lab has received significant funding, including an NSF-funded Biological Integration Institute (the Integrative Movement Sciences Institute) and a FLAP MURI grant. His mentoring philosophy emphasizes interdisciplinary collaboration and hands-on research experience, with over 120 undergraduate students having participated in his lab through Georgia Tech's Vertically Integrated Projects program. The Agile Systems Lab, housed in the Howey Physics Building at Georgia Tech, brings together researchers from physics, biology, engineering, and neuroscience to study the fundamental principles of movement. The lab features state-of-the-art equipment for high-speed videography, electrophysiology, robotic flower tracking systems, and X-ray diffraction studies of living muscle. Current collaborative projects include the NSF-funded Integrative Movement Sciences Institute, which explores movement across scales from molecules to organisms, and the FLAP MURI grant investigating resonant mechanics in flapping flight.