جست‌وجوی استاد و پژوهشگر

Hong Wanمشاهده پروفایل

دانشیار

• Complex Simulation Modeling
• Blockchain Analysis
• Data Science
• +۵ مورد دیگر

Hong Wan is an Associate Professor in the Department of Industrial and Systems Engineering (ISE) at North Carolina State University (NC State). She holds a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2004), with earlier degrees in Chemistry (Peking University, 1998), Materials Science (Northwestern, 2001), and Industrial Engineering (Northwestern, 2002). Before joining NC State, she was an Associate Professor at Purdue University’s School of Industrial Engineering, where she directed the Purdue Blockchain Lab and co-directed the Smart Design Lab. She is also affiliated with the SEED Center for Data Farming at the Naval Postgraduate School. Her research focuses on blockchain analysis, complex simulation modeling, healthcare systems simulation, and game theory applications in quality management. She leads NC State’s ISE Blockchain Lab, studying blockchain as a complex system using simulation, optimization, and statistical methods. Notable contributions include modeling Bitcoin network dynamics, developing sequential elimination procedures for optimization, and integrating machine learning with blockchain systems. Education: B.S. in Chemistry (Peking University, 1998) M.S. in Materials Science (Northwestern University, 2001) M.S. in Industrial Engineering & Management Sciences (Northwestern University, 2002) Ph.D. in Industrial Engineering & Management Sciences (Northwestern University, 2004) Awards: 2017 Featured Paper in IIE Transactions 2014 Best Student Paper Finalist (QSR) 2013 Military Application Society Koopman Prize (INFORMS) Labs/Teams: ISE Blockchain Lab (NC State) Purdue Blockchain Lab (former) SEED Center for Data Farming (Naval Postgraduate School) Her work bridges operations research and emerging technologies, emphasizing strategic decision-making in decentralized systems and healthcare applications. Recent projects explore generative AI for simulation and blockchain’s role in supply chain transparency.

Bethany Luschمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Dynamical Systems
• Supercomputing
• +۷ مورد دیگر

Bethany Lusch is an Assistant Computer Scientist in the Data Science Group at the Argonne Leadership Computing Facility, Argonne National Laboratory. She holds advanced degrees in applied mathematics and focuses on integrating artificial intelligence with scientific computing, particularly for dynamical systems and PDE-based simulations. Education: PhD in Applied Mathematics, University of Washington, 2016 MS in Applied Mathematics, University of Washington, 2011 BS in Mathematics (Honors), University of Notre Dame, 2010 Her research centers on scientific machine learning, with emphasis on developing machine-learning emulators for expensive simulations such as climate models and computational fluid dynamics. She pioneers methods that embed domain knowledge into deep learning frameworks, particularly through Koopman operator theory to linearize nonlinear dynamics. Her work spans representation learning, reduced-order modeling, and analysis of supercomputing system logs. The 15 most recent publications reflect a strong trend toward using deep learning—especially recurrent and autoencoder architectures—to model complex physical systems. Key themes include surrogate modeling, latent-space dynamics, closure in reduced-order models, and interpretable AI in scientific contexts. Her work bridges theoretical rigor with practical applications in engineering and geophysics. Scientific Awards: NPSC Fellow, National Physical Science Consortium (now GFSD), Aug 2010 – May 2016 Six-Year Graduate Fellowship, NSF-SUMR Scholarship University of Notre Dame Mathematics Department Scholarship, Aug 2007 – Jun 2010 Bethany Lusch has collaborated extensively with researchers at Argonne and the University of Washington. Her projects often involve interdisciplinary teams working on high-performance computing applications. She has contributed to the development of tools like MELA for visual analytics of HPC logs and has secured competitive fellowships and funding. Her prior role as a Research Associate at the University of Washington (2016–2018) preceded her current position at Argonne (2018–present), indicating a continuous research trajectory. She is actively involved in advancing AI-driven scientific discovery, with ongoing work in universal embeddings for PDEs and multifidelity modeling. Her lab affiliations include the Argonne Leadership Computing Facility, where she leverages world-class supercomputing resources to develop and test her models.

Shankar Dekaمشاهده پروفایل

استادیار

• Nonlinear systems
• Data-driven techniques
• Stability theory
• +۵ مورد دیگر

Shankar Deka is an Assistant Professor at the Department of Electrical Engineering and Automation, Aalto University, leading the Nonlinear Systems and Control research group. He is affiliated with the Finnish Center for Artificial Intelligence (FCAI). His research focuses on integrating nonlinear stability theory, optimal control, and machine learning to develop safe and robust robotic systems. Applications include telerobotic surgery, precision agriculture, and human-multirobot systems. His work explores cutting-edge topics such as Koopman operator theory for control design, adversarial attacks on neural networks, and human-in-the-loop robotics. Key contributions include advancements in convex optimization for state-constrained control, path-integral methods for system analysis, and safety-critical coordination of robotic manipulators. Recent publications highlight innovations like human-in-the-loop precision agriculture systems, robotic mirror therapy for stroke rehabilitation, and neural Lyapunov functions for nonlinear attractors. His research bridges theoretical foundations with practical applications in cyber-physical systems, medical robotics, and agricultural automation. Shankar Deka’s profile reflects a strong commitment to interdisciplinary research at the intersection of control theory, machine learning, and robotics. His work aims to create certifiably safe systems with broad societal impact.

Jason Bramburgerمشاهده پروفایل

استادیار

• Dynamical Systems
• Data-Driven Methods
• Pattern Formation
• +۳ مورد دیگر

Jason Bramburger is an Assistant Professor at Concordia University and an adjunct professor at McGill University. He is a member of the CRM Applied Math Lab and contributes to the Physica D Editorial Board. His research focuses on dynamical systems, data-driven discovery, pattern formation, and polynomial optimization applied to differential equations. He holds a Ph.D. from the University of Ottawa (2017), with postdoctoral fellowships at Brown University and the University of Victoria, and prior experience as an acting instructor at the University of Washington. Research interests include multiscale dynamics, nonlinear Floquet theory, and Koopman operator methods. Recent work emphasizes data-driven analysis of dynamical systems, with applications to Poincaré maps, invariant measures, and control systems. Key contributions include methods for discovering Poincaré maps via sparse regression and bounding long-term system behaviors using convex optimization. Notable publications span topics like real-time motion detection, localized patterns in networks, and ergodic optimization. Bramburger actively contributes to open-source projects, including a textbook on Data-Driven Methods for Dynamic Systems , and maintains GitHub repositories for computational tools in dynamical systems and neural networks.

Sandra Hircheمشاهده پروفایل

استاد

• Control Engineering
• Cyber-Physical Systems
• Robotics
• +۳ مورد دیگر

Prof. Sandra Hirche is the TUM Liesel Beckmann Distinguished Professor and Chair of Information-oriented Control at the Technical University of Munich (TUM). She leads the Professorship for Information-oriented Control within the TUM School of Computation, Information and Technology. Her research focuses on control engineering, system theory, and cyber-physical systems, with applications in robotics, human-machine interaction, and infrastructure systems. She holds a PhD from TUM and has conducted postdoctoral research at the Tokyo Institute of Technology and the University of Tokyo. Prof. Hirche coordinates the DFG Priority Program 'Cyber-Physical Networking' (2016–2023) and has held leadership roles in IEEE, including Vice-President of the IEEE Control Systems Society. Her awards include the Heinz Maier-Leibnitz Medal (2023), IEEE Fellow (2020), and ERC Consolidator and Starting Grants. She has authored numerous publications in top journals and conferences, focusing on topics like Koopman operator theory, Gaussian processes, and safety-critical control systems. Her research group explores networked control systems, data-driven methods, and human-centered robotics. Ongoing projects include the Konrad Zuse School of Excellence in reliable AI and the BMBF Research Hub 6G-Life. She actively mentors students and collaborates on interdisciplinary projects addressing societal challenges through control systems innovation.

• Nonlinear Systems
• Control Theory
• Reachability Analysis
• +۳ مورد دیگر

Jianqiang Ding is a Doctoral Researcher at Aalto University's School of Engineering, Department of Electrical Engineering and Automation. His work focuses on nonlinear systems and control theory, particularly in reachability analysis for dynamical systems verification and controller synthesis. His research interests include: Reachability analysis for safety-critical systems Formal verification of nonlinear control systems Developing computational tools like PyBDR for set-boundary based analysis Controller synthesis for multi-input multi-output systems Image processing techniques for discernible mosaics Recent publications demonstrate expertise in: Formal methods for system verification Control theory applications in engineering Computational tool development for control systems His collaborative work appears in venues like Formal Methods Symposium and Computer Aided Verification Conference, with particular emphasis on mathematical rigor in control design.

Christophe Zhangمشاهده پروفایل

پژوهشگر

• Control Theory
• Optimal Control
• Partial Differential Equations (PDEs)
• +۳ مورد دیگر

Christophe Zhang is a researcher currently seconded to the SPHINX team at INRIA Nancy Grand Est. His work focuses on control theory, specifically the stabilization, reachability analysis, and optimal control of partial differential equations (PDEs) and data-driven systems. Educational Background: PhD in Applied Mathematics (2019), Sorbonne Université Master's in Applied Mathematics (2016), Paris 6 University Engineering Degree (2015), École polytechnique Research Interests: His research spans the analysis and control of PDEs (hyperbolic and parabolic equations), data-driven modelling, Koopman operator methods, and reachability analysis with constraints. He explores techniques like backstepping, Fenchel duality, and computer-assisted proofs to tackle stabilization and controllability challenges. Publications: His recent work includes studies on conic-constrained reachability, non-reachability certification via duality, and stabilization of the heat equation and water wave systems. These papers emphasize rigorous mathematical frameworks combined with computational approaches. Experience: Researcher (2021–Present), INRIA Nancy Grand Est Postdoctoral Researcher (2020–2021), Friedrich-Alexander-Universität Erlangen Nürnberg PhD Candidate (2016–2019), Laboratoire Jacques-Louis Lions Teaching Assistant (2017), ETH Zurich

• Mitochondrial Physiology
• Redox Biology
• Cellular Biochemistry
• +۳ مورد دیگر

Werner Koopman is an external researcher in the Human and Animal Physiology department at Radboud University Medical Centre . His work focuses on mitochondrial physiology, redox homeostasis, and metabolic disorders, particularly in neurodegenerative diseases and cellular energy dynamics. Research Interests: Mitochondrial dysfunction, Leigh's disease, antioxidant therapy, metabolic substrates in disease models, and autophagy-dependent ferroptosis. Projects: Co-promotor for PhD research on nicotinamide nucleotide transhydrogenase (NNT), reactive oxygen species defense, and mitofusin 2's role in calcium signaling. Publications: Recent studies on mitochondrial antioxidants, complex I deficiency models, and guidelines for ferroptosis detection in autophagy contexts. Collaborations: Engaged in international interdisciplinary research with institutions in neuroscience, bioenergetics, and metabolic disease fields.

Lorenzo Rosascoمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Robotics
• +۵ مورد دیگر

Lorenzo Rosasco is a Full Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) at the University of Genoa. He holds roles including University contact for the thematic area "Artificial Intelligence" and membership on the Department Board. His research focuses on machine learning theory, kernel methods, optimization, robotics, and their applications in control systems, domain adaptation, and interdisciplinary fields like biomedical engineering. His educational background includes advanced expertise in mathematical foundations of machine learning, though specific academic credentials are not detailed here. Teaching responsibilities include courses such as MACHINE LEARNING, SEQUENTIAL PREDICTION AND REINFORCEMENT LEARNING, and INFORMATION THEORY AND INFERENCE at both undergraduate and graduate levels. Research interests revolve around developing efficient algorithms for large-scale machine learning, including iterative regularization, transfer learning, and control systems using Koopman operator theory. Notable contributions include work on fast kernel methods, optimization in neural networks, and applications in robotics for tasks like humanoid locomotion and tactile sensing. Recent publications explore topics such as double descent phenomena, sim-to-real transfer learning, and adaptive control strategies. His work bridges theoretical insights with practical applications, emphasizing computational efficiency and interdisciplinary impact.

• Power Systems
• Electricity Markets
• Distributed Energy Resources
• +۵ مورد دیگر

Subhonmesh Bose is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Lab and Health Care Engineering Systems Center . He holds the M. Stanley Helm Fellowship . His research focuses on power systems, optimization, machine learning, and their applications in energy markets and healthcare. Key areas include risk-sensitive market design, distributed optimization algorithms, and transient stability analysis using operator-theoretic methods. Education details are not explicitly provided in the text, but his affiliations suggest advanced training in electrical engineering and applied mathematics. His work bridges theoretical foundations (e.g., game theory, control systems) with practical challenges like pandemic management in schools and grid resilience. Research Trends : Recent articles emphasize adaptive control under uncertainty, incentive mechanisms in multi-agent systems, and data-driven approaches for power system stability. His work on test-to-stay programs for K-12 schools during the pandemic highlights interdisciplinary impact. Awards : NSF CAREER Award (2021) for Risk-Sensitive Market Design for Power Systems . Grants : Leads projects on AI for mitigating COVID-19, energy storage placement, and market design for distributed energy resources. Labs/Teams : Active in Coordinated Science Lab (CSL) and Health Care Engineering Systems Center (HCESC), collaborating on cross-disciplinary challenges.

Valentin Deaconuمشاهده پروفایل

دانشیار

• Groupoid C*-algebras
• K-theory
• Non-commutative ergodic theory
• +۳ مورد دیگر

Valentin Deaconu is an Associate Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR). He has held positions at UNR since 1995, progressing from a Lecturer to Assistant Professor (2004–2010) and then to his current rank. Previously, he was a Researcher at the Institute of Mathematics of the Romanian Academy (1984–1991) and a Teaching Assistant at the University of Iowa (1991–1995). Education: He earned a Ph.D. in Mathematics from the University of Iowa in 1995, under the supervision of Prof. Paul S. Muhly. Research Interests: Deaconu’s work focuses on operator algebras, specifically groupoid C*-algebras, K-theory, and non-commutative ergodic theory. His research explores symmetries in C*-algebras, groupoid representations, and dynamical systems. Recent studies include cohomology of ample groupoids, self-similar groupoid actions, and Koopman representations. Publications: His articles span topics like groupoid actions, Cuntz-Pimsner algebras, and topological graph algebras. Notable works include studies on noncommutative geometry, ergodic theory, and functional analysis, with a focus on algebraic and topological structures in operator theory. Teaching and Mentorship: Deaconu has contributed to education through courses and a textbook, A Bridge to Higher Mathematics (2016), aiding students’ transition to advanced mathematical concepts.

Justin Tatch Mooreمشاهده پروفایل

استاد

• Set Theory
• Mathematical Logic
• Infinite Combinatorics
• +۴ مورد دیگر

Justin Tatch Moore is a Professor in the Department of Mathematics at Cornell University's College of Arts and Sciences. He holds his office in Malott Hall and has been an active member of the mathematics faculty since completing his Ph.D. at the University of Toronto in 2000. Moore's research spans several areas of mathematical logic and its applications: Set theory and infinite combinatorics Applications to topology, functional analysis, and algebra Groups related to piecewise linear homeomorphisms Forcing axioms and the continuum hypothesis Ramsey theory of infinite sets His work has significantly contributed to solving long-standing problems in set theory, including the basis problem for uncountable linear orders and the L space problem from general topology. Moore's research often explores the connections between set theory and other mathematical disciplines, revealing deep structural relationships. Moore serves as an editor for the Archive of Mathematical Logic, handling papers specifically in set theory. His publications demonstrate a consistent focus on foundational questions in mathematics, with recent work exploring connections between large cardinals, Ramsey theory, and geometric group theory. As an educator, Moore teaches courses including Multivariable Calculus, Supervised Research, and a Seminar in Logic. He has supervised numerous graduate students through research and reading courses, contributing to the next generation of mathematical researchers.

Evren Mert Turanمشاهده پروفایل

مدرس

• Process Systems Engineering
• Machine Learning
• Optimization
• +۳ مورد دیگر

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

Robert Woodمشاهده پروفایل

استاد

• Robotics
• Materials Science
• Mechanical Engineering
• +۷ مورد دیگر

Robert Wood is the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at Harvard University, affiliated with the School of Engineering & Applied Sciences. He holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His research focuses on robotics, particularly bio-inspired systems and soft robotics applications in natural environments and biomedicine. He emphasizes interdisciplinary approaches, combining engineering with biological insights to advance robotic design and functionality. Wood teaches courses such as 'How Music Works: Engineering the Acoustical World,' 'Microrobotics and Bio-inspired Autonomous Robotic Systems,' and 'Advanced Topics in Robotics Research.' He advocates for hands-on learning and the value of iterative failure in education, stating, 'Making mistakes and learning from them is an absolutely essential part of the learning process.' His lab, the Microrobotics Lab, explores topics like compliant actuators, bioinspired locomotion, and soft robotic systems. While no formal scientific awards are listed, Wood’s contributions to robotics have been highlighted through his work on projects like RoboBee and the Cetacean Translation Initiative (Project CETI). He directs graduate studies in his department and has advised students across his research and teaching activities. His lab’s current and future work includes advancements in soft wearable sensors, modular robotic systems, and biomimetic mechanisms for medical and environmental applications.

Matt Hodgsonمشاهده پروفایل

مدرس

• Quantum Mechanics
• Computational Physics
• Condensed Matter Physics
• +۳ مورد دیگر

Matt Hodgson is a Lecturer at the University of York's School of Physics, Engineering and Technology, specializing in theoretical and computational physics. With a PhD in Physics (2016) and prior roles at Durham University (2019-2021) and Max Planck Institute (2016-2019), he focuses on advancing quantum theory applications for electron excitation properties in materials. His research bridges fundamental Density Functional Theory (DFT) developments with many-body quantum mechanics , particularly through his creation of the iDEA code for exact solutions in 1D systems. Key contributions include clarifying exchange-correlation potential behavior, resolving self-screening errors in GW approximations, and integrating machine learning techniques for non-analytic energy discontinuities. Pioneered DFT methods for excited state calculations Developed nearsighted effective potential for molecular junctions Explored electron localization in time-dependent systems Scientific recognition includes the K. M. Stott Prize for his PhD work. Teaching responsibilities span advanced theoretical techniques and computational labs, with leadership roles in academic misconduct panels and careers coordination . External activities include EPSRC Peer Review College membership and contributions to the European Theoretical Spectroscopy Facility .