جست‌وجوی استاد و پژوهشگر

Eugene Feinbergمشاهده پروفایل

استاد

• Markov Decision Processes
• Stochastic Optimization
• Inventory Control
• +۳ مورد دیگر

Eugene Feinberg is a Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University's College of Engineering and Applied Sciences. He is renowned for his extensive contributions to Markov Decision Processes (MDPs), stochastic optimization, and inventory control. Research Interests: His work spans theoretical and applied aspects of Markov Decision Processes , stochastic optimization , inventory control , healthcare decision-making , and machine learning . He has particularly focused on solving complex decision-making problems under uncertainty, with applications ranging from operations research to medical decision-making. Scientific Awards: He has been honored with the title of Distinguished Professor , recognizing his outstanding contributions to his field. Advising and Grants: While specific details on students and grants are not provided, his prolific publication record and faculty status suggest active involvement in advising and securing research funding. Contact and Resources: His university webpage can be accessed at http://www.ams.sunysb.edu/~feinberg/ , and his Google Scholar profile is available at https://scholar.google.com/citations?user=LLt--pgAAAAJ&hl=en .

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +۷ مورد دیگر

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

Deepa Kundurمشاهده پروفایل

استاد

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +۴ مورد دیگر

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

YING-TSONG LINمشاهده پروفایل

استاد

• Applied Ocean Sciences
• Autonomous Ocean Platforms
• Internal Waves and Ocean Mixing
• +۳ مورد دیگر

YING-TSONG LIN is an Acting Professor at the Scripps Institution of Oceanography (SIO), UC San Diego. His research focuses on applied ocean sciences, autonomous ocean platforms, internal waves, ocean acoustics, and instrumentation. He leads projects like the New England Shelf Break Acoustics (NESBA) experiment, emphasizing real-time acoustic modeling and environmental interactions. Research interests include 3D acoustic propagation modeling, ocean mixing dynamics, and seabed characterization. His work integrates high-performance computing and distributed sensor networks for oceanographic studies. Recent studies address underwater explosions, renewable energy impacts, and vessel localization using acoustic coherence. Publications emphasize advancements in hydroacoustic modeling, seabed inversion techniques, and environmental asymmetry effects. His contributions span interdisciplinary areas like bioacoustics and seismic-to-acoustic wave conversions. Labs/Teams: Involved with SIO's Acoustics and Oceanography research groups, focusing on autonomous platforms and global observing systems.

Che-Wei Changمشاهده پروفایل

استادیار

• Coastal Engineering
• Coastal Resilience
• Nature-based Solutions
• +۶ مورد دیگر

Che-Wei Chang is an Assistant Professor in the Department of Ocean Engineering at the University of Rhode Island (URI) , where he joined in August 2023. Prior to URI, he was an Assistant Professor at the Disaster Prevention Research Institute of Kyoto University in Japan. He earned his Ph.D. in Civil and Environmental Engineering from Cornell University in 2017, along with an M.S. in Civil Engineering from National Taiwan University (2008) and a B.S. in Soil and Water Conservation from National Chung Hsing University (2006). Ph.D., Civil and Environmental Engineering, Cornell University, 2017 M.S., Civil Engineering, National Taiwan University, 2008 B.S., Soil and Water Conservation, National Chung Hsing University, 2006 Dr. Chang specializes in coastal engineering , coastal resilience , and nature-based solutions for mitigating coastal hazards. His research focuses on water waves and nearshore hydrodynamics , particularly how mangroves and other natural features reduce wave impacts from tsunamis, storm surges, and rising sea levels. He integrates numerical modeling , laboratory flume experiments , and field observations to advance understanding of coastal morphodynamics under climate change. His recent work, including 2025 publications on SPH simulations and field studies , emphasizes mangrove resilience against breaking wave forces and critical wave conditions leading to mangrove failure. Earlier studies (2015–2022) explore Boussinesq modeling , wave-vegetation interactions , and coastal forest functional evaluation . These articles highlight his commitment to sustainable shoreline management and science-based coastal design. Dr. Chang received the Coastal Engineering Journal (CEJ) Citation Award 2024 for his impactful review paper and was appointed a Senior Fellow of the Coastal Institute (URI) in 2025 . He actively mentors graduate students like Felipe Espinoza and Ramin Safari , who investigate wave-mangrove dynamics and vegetation impacts on coastal systems . His lab at URI, the Chang Coastal Lab , collaborates on conferences (e.g., ICCE 2024) and NSF-funded projects like NHERI RAPID Facility workshops.

David M. Higdonمشاهده پروفایل

استاد

• Bayesian Statistical Modeling
• Uncertainty Quantification
• Space-Time Modeling
• +۷ مورد دیگر

David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.

Jean Ponceمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Jean Ponce is a Professor at Ecole Normale Supérieure - PSL and a Global Distinguished Professor at New York University's Courant Institute and Center for Data Science. He serves as Scientific Director of PRAIRIE Interdisciplinary AI Research Institute and co-founded Enhance Lab, commercializing super-resolution imaging software. His research focuses on computer vision, machine learning, robotics, and image processing. Ponce has held roles at Inria, MIT, Stanford, and the University of Illinois, and is an IEEE and ELLIS Fellow. He has served as chair of major conferences like CVPR, ECCV, and ICCV, and authored the textbook 'Computer Vision: A Modern Approach.' Research interests include statistical models for exoplanet detection, neural networks for 3D reconstruction, and self-supervised learning. His work combines theoretical foundations with practical applications in astrophysics, robotics, and imaging. Notable awards include the IEEE CVPR Longuet-Higgins Prize (2016, 2020) and ICML Test-of-Time Award (2019). Key projects include Enhance Lab's high dynamic range imaging and PRAIRIE's interdisciplinary AI initiatives. Ponce's articles explore cutting-edge topics like neural object priors, geodesic motion planning, and satellite image analysis. His contributions bridge academic research and industrial applications, emphasizing both fundamental theory and real-world impact.

Christopher T. Middlebrookمشاهده پروفایل

استاد

• Integrated Optical Devices
• Electronic Substrate Manufacturing
• Photonics
• +۳ مورد دیگر

Christopher T. Middlebrook is a Professor of Electrical and Computer Engineering at Michigan Technological University (MTU), with an affiliated appointment in the Physics department. He holds a visiting faculty research engineer position at Scientific Applications International Corporation (SAIC) supporting the DoD Executive Agent for Printed Circuits and has served as visiting faculty at the Naval Surface Warfare Center Crane (2016–2020). His expertise spans integrated optical devices, electronic substrate manufacturing, and photonics. Middlebrook leads the Plexus Innovation Laboratory, a campus electronics maker space, and has pioneered PCB fabrication education through courses and media contributions. Education: PhD in Optics from the University of Central Florida, MS in Applied Optics from Rose-Hulman Institute of Technology, and BS in Electrical Engineering from MTU. His research focuses on electro-optic polymers, optoelectronic integration, and advanced manufacturing techniques. He has published 49 papers, holds two patents, and secured grants totaling over $970K, including the Michigan Economic Development Corporation-funded 'Back-End Semiconductor Curriculum' initiative (2024). Research highlights include developing UV resin printer methods for PCB prototyping, optimizing polymer waveguides, and advancing quantum communication technologies. Awards include the HKN Professor of the Year (multiple years), Michigan Tech Graduate Mentor Award, and IPC Carano Teacher Excellence Award. His work bridges academia and industry, emphasizing hands-on learning and innovation. Key Grants: Back-End Semiconductor Curriculum for Advanced Substrates: $970K (2024) Mesosphere Observation Mission (MOMBO): $38K (2022–2023) Labs/Teams: Plexus Innovation Lab, MTU's Electronics Maker Space Courses Taught: EE2230 PCB Fabrication, EE3190 Optical Sensing, EE5500 Stochastic Processes, and 15+ others emphasizing photonics and optoelectronics.

Antonio Griffoمشاهده پروفایل

استاد

• Power Electronics
• Electric Drives
• SiC Power Converters
• +۵ مورد دیگر

Professor Antonio Griffo holds the position of Professor of Power Electronics and Electric Drives at the University of Sheffield's School of Electrical and Electronic Engineering. He leads the Electrical Machines and Drives Research Group and is involved in the High Reliability Drives Group. His academic journey includes a MSc (2003) and PhD (2007) in Electrical Engineering from the University of Naples, followed by research roles at Bristol and Sheffield Universities before becoming a Lecturer in 2013 and later a Professor. His research focuses on advanced control of electric drives, SiC-based power electronics for aerospace/renewables, fault detection in machines, and thermal management. Key projects include modeling hybrid AC/DC power systems for 'More Electric Aircraft', sensorless control techniques, and real-time simulation methodologies. He has pioneered work on SiC converter reliability, insulation monitoring, and condition-based maintenance systems. Publications (15+ in top journals like IEEE Transactions) emphasize innovative solutions for power electronics challenges, including voltage stress mitigation, thermal modeling, and fault tolerance. His work bridges theory and application, addressing critical issues in aerospace, renewable energy, and electric vehicle systems. Griffo also contributes to educational advancements through modular training platforms for power electronics education. Labs/Teams: Active in the Electrical Machines and Drives Research Group, focusing on high-reliability drive systems and sustainable energy technologies. Collaborates with industry on projects like the EPSRC Offshore Wind Prosperity Partnership.

• Fuzzy Systems
• Computational Intelligence
• Nonlinear Control
• +۴ مورد دیگر

Michio Sugeno is a distinguished Professor at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, Department of Computational Intelligence. With a career spanning over four decades, he has established himself as a leading figure in fuzzy systems and computational intelligence. His research interests encompass Fuzzy Systems, Computational Intelligence, Nonlinear Control, Choquet Integral theory, Brain-Computer Interfaces, and Linguistic Computing. Sugeno's work has fundamentally shaped modern fuzzy control theory, particularly through his development of the Takagi-Sugeno fuzzy model which has become a standard approach in industrial applications. Analysis of his recent publications reveals a continued focus on piecewise nonlinear modeling, stability analysis of fuzzy systems, and the application of Choquet calculus to various computational problems. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations in control systems and intelligent computing. IEEE Pioneer Award in Fuzzy Systems IFSA Fellow Emanuel R. Piore Award Sugeno has mentored numerous researchers who have become prominent in their own right, including Tadanari Taniguchi, Luka Eciolaza, and Anh-Tu Nguyen. His laboratory has been instrumental in developing novel approaches to nonlinear control systems using piecewise bilinear models and fuzzy logic. Current research directions include brain-computer interfaces using EEG analysis and the development of everyday language computing systems that enable more natural human-computer interaction.

Ellen Zweibelمشاهده پروفایل

استاد

• Plasma Astrophysics
• Astrophysical Magnetic Fields
• Interstellar Medium
• +۶ مورد دیگر

Ellen Zweibel is the W. L. Kraushaar Professor of Astronomy and Physics at the University of Wisconsin–Madison , where she has been a faculty member since 2003. She holds a joint appointment in the Department of Astronomy and Physics . Zweibel earned her undergraduate degree in Mathematics from the University of Chicago and her Ph.D. in Astrophysical Sciences from Princeton University. Her research focuses on plasma astrophysics , particularly the evolution of astrophysical magnetic fields , cosmic ray feedback in galactic and intergalactic environments, and stellar differential rotation dynamics. Recent work examines cosmic ray interactions with the interstellar medium, magnetic instabilities in galaxy clusters, and turbulence-driven dynamo processes. Zweibel's publications highlight collaborations on missions like HelioSwarm and SOFIA/HAWC+ , including the discovery of a magnetized dust ring in the Galactic Center . She leads NSF-funded research on microscale plasma processes in high-beta environments and contributes to understanding magnetic reconnection across astrophysical contexts.

• Switched and hybrid systems
• Nonlinear control theory
• Control with limited information
• +۵ مورد دیگر

Daniel M Liberzon is the Richard T. Cheng Professor in the Department of Electrical and Computer Engineering and a Professor at the Coordinated Science Laboratory (director of the Decision and Control group) at the University of Illinois Urbana-Champaign. He also holds an affiliate appointment in the Department of Mathematics. Ph.D. in Mathematics from Brandeis University (1998), advised by Roger W. Brockett (Harvard) Undergraduate studies in Mathematics at Moscow State University (1989-1993) His research interests focus on theoretical and applied aspects of nonlinear, switched, and hybrid systems with limited information. Key areas include: Stability analysis via Lyapunov functions and Lie algebras Finite-data-rate control and topological entropy Robust synchronization and observers Stochastic switched systems Supervisory control for uncertain systems Recent scientific awards include: IFAC Fellow (2016) IEEE Fellow (2013) AACC Donald P. Eckman Award (2007) IFAC Young Author Prize (2002) NSF CAREER Award (2002) He has taught graduate courses such as ECE 517 (Nonlinear and Adaptive Control), ECE 553 (Optimum Control Systems), and ECE 586 DL (Hybrid Systems and Control). Current sponsored projects include NSF grants on switching control and AFOSR MURI on hybrid dynamics.

Dennis Dukeمشاهده پروفایل

استاد

• High Performance Computing and Networking
• Computational Ancient Astronomy
• Nonlinear Time Series
• +۳ مورد دیگر

Dennis Duke is a Professor of Physics at the Florida State University (FSU), affiliated with the College of Arts and Sciences and the Department of Scientific Computing. His academic career spans over four decades, with roles including Director of the Supercomputer Computations Research Institute (SCRI) and leadership in numerous national and international computing initiatives. He holds a B.S. from Vanderbilt University (1970) and a D.Phil. from Iowa State University (1974). His research focuses on High Performance Computing, Computational Ancient Astronomy, and Nonlinear Dynamics. Notable contributions include reconstructing ancient astronomical models (e.g., Ptolemaic planetary systems) and analyzing historical star catalogs. He has authored over 150 publications, including seminal work on Babylonian-Greek mathematical transmission and Indian astronomical traditions. Awards include multiple COFRS Awards (1985, 1986) and the President's Continuing Education Award (1992). Duke has organized major supercomputing conferences (e.g., SC98) and served on committees for DOE supercomputing initiatives. His work bridges computational science with historical scholarship, exemplified by animations of ancient planetary models and statistical analyses of precession effects. Professional affiliations include the American Physical Society and Sigma Xi.

Sapna Sharmaمشاهده پروفایل

استاد

• Climate Change
• Aquatic Ecology
• Environmental Science
• +۳ مورد دیگر

Sapna Sharma is a Full Professor in the Department of Biology at York University's Faculty of Science. Her research focuses on predicting the effects of environmental stressors—such as climate change, invasive species, and habitat alteration—on lakes and aquatic ecosystems. She leads the Sharma Lab, emphasizing big data and machine learning to address global change biology challenges. Her work explores lake ice dynamics, water quality, and biodiversity responses to climate shifts, with a focus on Arctic and Northern Hemisphere lakes. Key research areas include cryosphere studies, aquatic ecology, and climate modeling. She has developed quantitative frameworks to predict ecosystem responses and collaborates on long-term projects like the Community Lake Ice Collaboration, spanning over 1,000 lakes. Her lab's contributions include datasets like the Global Lake area, Climate, and Population (GLCP), enhancing global environmental monitoring. Sharma's research highlights societal impacts of environmental changes, such as reduced lake ice safety and economic consequences for communities. She advocates for interdisciplinary approaches to limnology and emphasizes freshwater biodiversity conservation. Her work bridges ecological and social sciences, addressing climate adaptation and policy implications. Contact details: Email sharma11@yorku.ca , Office: Lumbers Building 326. She is eligible to supervise graduate students in the Biology Graduate Program at York University.

Jack Puleoمشاهده پروفایل

استاد

• Coastal Engineering
• Sediment Transport
• Hydrodynamics
• +۵ مورد دیگر

Jack Puleo is a Professor and Chair in the Department of Civil and Environmental Engineering at the University of Delaware (UD), and a core faculty member of the Center for Applied Coastal Research (CACR). He holds a Ph.D. from the University of Florida, a Master’s from Oregon State University, and a Bachelor’s from Humboldt State University. His research focuses on coastal hydrodynamics, sediment transport, and nature-based solutions for coastal resilience. He has served as Associate Chair and Director of CACR, and was a Fulbright Scholar and Visiting Professor at Plymouth University (2011-2012). Research Interests: Small-scale hydrodynamic processes and sediment transport in coastal zones Remote sensing and sensor networks for coastal monitoring Nature-based solutions for coastal protection Munitions mobility in nearshore environments Climate change impacts on coastal flooding Awards and Honors: NSF CAREER Award (2007) ASCE Teaching Awards University of Delaware Teaching Awards (twice) Chi Epsilon Advising Award ASBPA Robert G. Dean Award German DAAD Scholarship Labs and Collaborations: Core member of the Center for Applied Coastal Research (CACR), collaborating on projects such as UXO mobility studies, coastal flooding modeling, and military infrastructure resilience. Active in interdisciplinary work with the Naval Research Laboratory and joint bases like Langley-Eustis.