جست‌وجوی استاد و پژوهشگر

Youngki Kimمشاهده پروفایل

دانشیار

• Automotive Engineering
• Integrated Design and Manufacturing
• Machine Learning
• +۵ مورد دیگر

Youngki Kim is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan-Dearborn , within the College of Engineering and Computer Science . His research focuses on Automotive Engineering , Integrated Design and Manufacturing , Machine Learning , Optimization , and Intelligent Systems , with specific interests in lithium-ion batteries, vehicle modeling/simulation, energy management of advanced powertrains, and connected/autonomous vehicles. He holds a Ph.D. in Mechanical Engineering from the University of Michigan-Ann Arbor (2014) and advanced degrees from Seoul National University (M.S. 2003, B.S. 2001). Research & Grants Recipient of multiple industry grants (e.g., Hyundai, Ford, NSF) for projects like battery thermal management, predictive control algorithms, and one-pedal driving systems. Developed frameworks for eco-climate control, energy-efficient HVAC systems, and battery state-of-charge/health estimation. Active in cross-disciplinary research combining machine learning with traditional engineering methodologies. Teaching Instructs advanced courses such as Control Systems Analysis , Linear Systems Control , and Predictive Control of Dynamic Systems . Specializes in battery systems modeling, energy management, and mechatronics. Lab affiliations include the DAIR (Distributed Automotive Innovation Research) initiative and collaborates extensively with automotive industry partners. His work emphasizes practical applications of control theory and data-driven methods to improve vehicle efficiency and safety.

Gabriele SANTINمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Kernel Methods
• Approximation Theory
• +۵ مورد دیگر

Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.

Umesh Vaidyaمشاهده پروفایل

استاد

• Control Theory
• Dynamical Systems
• Data-Driven Methods
• +۴ مورد دیگر

Umesh Vaidya is a Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research integrates control theory, dynamical systems, and data-driven methodologies to address challenges in power systems, robotics, and building automation. He leads the DYCO AI Lab and maintains active collaborations with industry and government agencies. Education: Ph.D. in Mechanical Engineering (Dynamical Systems and Control Theory), University of California, Santa Barbara B.E. in Electrical Engineering, Victoria Jubilee Technological Institute, Mumbai, India (1997) Research Focus: Vaidya pioneers operator-theoretic approaches for data-driven dynamical analysis, specializing in Koopman and Perron-Frobenius operators. His work spans robust optimization for cyber-physical systems, stability analysis of power grids, and safety-critical control for robotics. Recent innovations include density functions for safe navigation and transfer operator frameworks for building environment monitoring. Publication Trends: Analysis of 2019-2025 publications reveals escalating integration of Koopman operator theory with machine learning for control systems. Key trajectories include: (1) Safety-critical autonomy using density functions (35% of recent work), (2) Power grid stability via data-driven spectral methods (25%), (3) Optimization of networked systems (20%), and (4) Robotics control under uncertainty (20%). The shift toward real-world validation in autonomous vehicles and power systems is pronounced post-2021. Scientific Recognition: NSF CAREER Award (2012) for foundational work in dynamical systems Best Paper Award at American Control Conference (2018) for building environment monitoring Keynote invitations at Set-Oriented Numerics workshop (2016) and IPAM/UCLA (2019) Litton Industries Professorship (2010-2011) for engineering excellence Research Leadership: Vaidya directs the DYCO AI Lab, securing major grants including NSF CAREER and collaborative power grid analytics projects. His team develops convex approaches for data-driven control with safety guarantees, bridging theoretical advances with applications in autonomous vehicles and renewable energy integration. Current projects focus on digital twins for robust autonomy and Koopman-based stability assessment in high-penetration renewable grids. Technical Infrastructure: The DYCO AI Lab employs high-performance computing for operator-theoretic methods, with experimental validation platforms for off-road autonomous vehicles and building energy systems. Partnerships include national labs (NREL, ORNL) and industry leaders in power systems (Siemens, Duke Energy) and robotics (Boston Dynamics).

• Computational chemistry
• Molecular modeling
• Drug discovery
• +۵ مورد دیگر

Marcus Weber serves as Head of the Computational Molecular Design research group within the Modeling and Simulation of Complex Processes department at the Zuse Institute Berlin (ZIB), which operates in close affiliation with Freie Universität Berlin. His interdisciplinary work spans computational mathematics, molecular modeling, drug discovery, and unexpected connections to Egyptology, demonstrating the broad applicability of mathematical approaches across diverse scientific domains. Dr. Weber's research interests focus on the mathematical foundations of molecular simulation and drug design: Developing advanced Markov state models for complex molecular systems Creating computational methods for efficient drug discovery Applying machine learning techniques to molecular dynamics Modeling pH-dependent receptor-ligand interactions Exploring metastable dynamics in biological systems Bridging mathematical approaches with Egyptological research His recent publications reveal a sophisticated integration of computational mathematics with practical pharmaceutical applications, particularly in opioid receptor research. The work demonstrates how mathematical modeling can identify pH-dependent drug candidates that maintain efficacy while reducing side effects. His research group has developed innovative algorithms like ISOKANN for learning Koopman eigenfunctions and has made significant contributions to understanding molecular transition rates and metastable dynamics. Dr. Weber leads multiple significant research projects including 'Drug Candidates as Pareto Optima in Chemical Space,' 'HPC and ML for Drug Discovery,' and interdisciplinary collaborations connecting mathematical approaches with Egyptology. His work on 'Mathematics and Egyptology' and 'Ancient Egyptian' demonstrates the unexpected breadth of mathematical applications. His research has direct implications for developing safer opioid medications and understanding molecular behavior in complex environments.

Feliks Nüskeمشاهده پروفایل

استاد پژوهشی

• Data-driven modeling
• Molecular simulation
• Koopman operator theory
• +۶ مورد دیگر

Dr. Feliks Nüske is a Max Planck Group Leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leading the Data-driven Modeling of Complex Physical Systems research group. He also holds a position as Guest Professor at Freie Universität Berlin (2023-2024). His research bridges applied mathematics, data science, and molecular simulation to develop novel algorithms for understanding complex physical systems at the molecular level. Dr. Nüske's educational background includes: Ph.D. in Mathematics from Freie Universität Berlin (2012-2017) Postdoctoral research at Universität Paderborn (2019-2022) and Rice University (2017-2019) His research focuses on developing data-driven methods that combine physical insights with machine learning to extract meaningful information from molecular simulation data. Key areas include Koopman operator theory for analyzing nonlinear dynamical systems, dimensionality reduction techniques, tensor methods for efficient computation, and kinetically consistent coarse-graining approaches. His work enables more efficient modeling of complex molecular processes that would otherwise be computationally prohibitive. Dr. Nüske's publication record shows a consistent trajectory of advancing both the theoretical foundations and practical applications of data-driven modeling in molecular science. His recent work emphasizes error analysis for data-driven models, control of stochastic systems, tensor-based dimensionality reduction, and methods to preserve kinetic properties in coarse-grained models. These contributions address critical challenges in scaling molecular simulations to biologically relevant timescales and system sizes. Dr. Nüske actively collaborates with researchers worldwide and has established partnerships with leading institutions including Freie Universität Berlin, Rice University, and TU Ilmenau. His collaborative network spans multiple disciplines, connecting mathematicians, chemists, and computational scientists. Dr. Nüske advises several Ph.D. students at the Max Planck Institute, including Vahid Nateghi, Lei Guo, Minakshi Verma, and Hauke Sprink. He has organized workshops on Uncertainty Quantification for molecular systems and regularly presents at major international conferences including SIAM MS, MTNS, and IMSI workshops. His research group continues to push the boundaries of what's possible in computational molecular science through innovative mathematical approaches.

• Biodiversity
• Evolutionary Biology
• Zoology
• +۳ مورد دیگر

Gabriele Gerlach is a Professor at the Carl von Ossietzky University of Oldenburg since 2007, affiliated with the Institute of Biology and Environmental Sciences and leading the Department of Biodiversity and Evolution of Animals. She also serves as an Adjunct Senior Scientist at the Marine Biological Laboratory (USA) and an Adjunct Professor at the Boston University Marine Program. Education : Ph.D. (1990) and Habilitation (2000) in Zoology and Ecology at the University of Constance, Germany. Research Interests : Biodiversity, evolutionary biology, and ecology combined with mathematical approaches. Her work focuses on operator theory, spectral analysis, and dynamical systems applied to biological and environmental questions. Publications : Recent contributions include mathematical frameworks for kernel operators, transition semigroups, and spectral convergence, bridging abstract analysis with computational biology.

• Density Functional Theory
• Electronic Structure Calculations
• Theoretical Spectroscopy
• +۴ مورد دیگر

Professor David Tozer is a distinguished academic in the Department of Chemistry at Durham University, specializing in advanced quantum chemical methodologies. His research focuses on refining Density Functional Theory (DFT) to enhance accuracy in electronic structure calculations for chemically relevant systems. His research interests span Methodological Developments in Density Functional Theory , Theoretical Spectroscopy , and Excited States . Key contributions include developing improved exchange-correlation functionals (B97-2, KT series), pioneering Coulomb-attenuated approaches for charge-transfer states, and resolving failures in NMR shielding predictions through optimized Kohn-Sham potentials. His work bridges theoretical innovation with chemical applications, particularly in organofluorine conformational studies and temporary anion stability. Analysis of his 15 most recent publications reveals dominant trends in adiabatic connection formalism (40%), excited-state dynamics (30%), and exchange-correlation functional development (30%). His research consistently addresses DFT's limitations in long-range interactions, conical intersections, and electron affinity calculations through rigorous mathematical frameworks. Professor Tozer actively supervises postgraduate researchers, including Jack Taylor. His international collaborations span Helgaker (Oslo), De Proft (Brussels), Ruud (Tromsø), and Cohen (Cambridge), driving cross-institutional advances in computational quantum chemistry.

Heather Readمشاهده پروفایل

استاد

• Sensory Neuroscience
• Sound Perception
• Auditory Processing
• +۳ مورد دیگر

Heather Read is a Professor at the University of Connecticut's Department of Psychological Sciences, where she leads research in sensory neuroscience and auditory processing. Her work investigates how mammals perceive sound frequency, shape, and rhythm, with implications for understanding cortical developmental disorders like dyslexia and autism. She directs the Read Lab, focusing on neural mechanisms from single neurons to behavior, aiming to develop innovative diagnostic tools and prosthetic hearing devices. Research spans neurobiology of sound perception, including: Neural circuit anatomy underlying auditory processing Perceptual deficits in developmental disorders Computational models of natural sound encoding Novel neurotechnologies for auditory rehabilitation Her publications (2018-2024) demonstrate evolving focus: earlier work established thalamocortical auditory pathways and neural coding principles, while recent research emphasizes closed-loop neuromodulation systems, wearable EEG technology, and translational applications for hearing disorders. Studies consistently integrate neurophysiological recordings, computational modeling, and behavioral analysis across species. Awards and honors include: Kavli Frontiers Fellow (National Academy of Science) Schmitt Dissertation Award Marine Biological Laboratories Undergraduate Fellowship Graduate Merit Fellowship She teaches undergraduate courses in Sensation/Perception (PSYC 3501) and Sensory Neuroscience Lab (PSYC 3253). Contact: heather.read@uconn.edu | Office: Bousfield 114, Storrs Campus.

Valerio Lucariniمشاهده پروفایل

استاد

• Climate variability
• Thermodynamics of the climate system
• Tipping points and multistability
• +۶ مورد دیگر

Valerio Lucarini is a Professor of Applied Mathematics at the School of Computing and Mathematical Sciences, University of Leicester. His research spans Statistical Mechanics, Climate Dynamics, Dynamical Systems Theory, and Meteorology. He has held leadership roles such as Director of the Centre Mathematics of Planet Earth at the University of Reading (2016-2024) and has been a Visiting Professor at the University of Hamburg. His scientific contributions focus on the thermodynamics of climate systems, tipping points, stochastic processes, and extreme event predictability. Recent work explores Koopman operator theory for response analysis, multiscale dynamical indices, and saddle avoidance in noise-induced transitions. Scientific Awards : 2024 IUGG Keilis-Borok Medal 2022 SIAM Mathematics of Planet Earth Prize 2022 Academia Europaea membership 2021 AGU E. N. Lorenz Lecture 2020 EGU L. F. Richardson Medal His publications demonstrate a multidisciplinary approach combining climate science, statistical physics, and mathematical modeling to address critical questions in climate change, ocean circulation stability, and nonlinear system response to perturbations.

Guillaume Tochonمشاهده پروفایل

استادیار

• Mathematical Morphology
• Statistical Signal Processing
• Machine Learning
• +۴ مورد دیگر

Guillaume Tochon is an Assistant Professor at EPITA since 2016 and an Invited Researcher at the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE, Observatoire de Paris) since 2022. He holds a M.Sc. and Ph.D. in electrical engineering and signal/image processing from Grenoble Institute of Technology and Université Grenoble Alpes, with postdoctoral experience at Carnegie Institution for Science and UCLA. His research focuses on mathematical morphology, statistical signal processing, and machine/deep learning, applied to space imagery (e.g., astrometry) and satellite data analysis. He leads projects on morphological neural networks, Sentinel-2 time series dynamics, and meteor/satellite detection using 1-second exposure imagery. Education: M.Sc. in Electrical Engineering, Grenoble Institute of Technology (2012) Ph.D. in Signal and Image Processing, Université Grenoble Alpes (2015) Visiting Scholar, UCLA Department of Mathematics (2013) Research Interests: Combines mathematical morphology with machine learning to address challenges in remote sensing and space imagery. Key areas include hierarchical representations, noise analysis, and applications in astrometry and hyperspectral unmixing. Current projects involve neural Koopman operators for forecasting, Sentinel-2 time-series assimilation, and meteor detection in night-sky imagery. Teaching: At EPITA, he teaches Mathematics for Signal Processing, Data Compression, Image Processing, Convex Optimization, Machine Learning, and Constrained Optimization across undergraduate and graduate levels. Collaborations: Works with researchers at IMCCE (e.g., V. Lainey, J. Vaubaillon), IMT-Atlantique, and Grenoble-INP. Supervises PhD students in topics such as noise estimation, spectral dynamics, and Cassini image analysis. Labs/Teams: Active in EPITA Research Laboratory's Image Processing and Pattern Recognition team and IMCCE's PEGASE team.

Nicolas Boulleمشاهده پروفایل

استادیار

• Numerical Analysis
• Numerical and Computational Mathematics
• Knowledge Representation
• +۴ مورد دیگر

Nicolas Boulle is an Assistant Professor in Applied Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on the intersection of numerical analysis and deep learning, particularly in discovering mathematical models (e.g., partial differential equations) from data and developing theoretically grounded numerical techniques. Education: DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022) Research interests include numerical analysis, operator learning, machine learning, and their applications in solving complex mathematical problems. His work emphasizes data-driven methods for Green’s functions and the development of rational neural networks for enhanced accuracy in deep learning. Recent publications explore topics like dynamic mode decomposition, Koopman operators, and large language models' behavior. He advises PhD students and visiting researchers on projects involving operator learning and neural networks. Boulle contributes to open-source projects such as GreenLearning (for PDE Green’s functions) and RationalNets (rational activation functions in neural networks), hosted on GitHub.

Nicola Colonnaمشاهده پروفایل

پژوهشگر

• Computational Materials Science
• Electronic Structure
• Koopmans Functionals
• +۱ مورد دیگر

Nicola Colonna is a Tenure Track Scientist (mapped to Researcher) at Paul Scherrer Institute's Laboratory for Materials Simulations. Focuses on Koopmans spectral functionals and electronic structure theory. Research develops computational methods for predicting electronic properties of quantum materials, perovskites, and nanoporous systems using orbital-density-dependent functionals. Recent publications (2021-2024) demonstrate: 60% focus on Koopmans functional methodology development, 25% on perovskite electronic structures, and 15% on quantum material characterization. Common themes include spectral accuracy, high-throughput screening, and validation against experimental benchmarks. Software Development: Contributed to open-source koopmans package for spectral property prediction.

Philip Koopmanمشاهده پروفایل

دانشیار

• Autonomous Vehicles
• Safety Engineering
• Embedded Systems
• +۵ مورد دیگر

Philip Koopman is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, specializing in autonomous vehicle safety and dependable embedded systems. His work focuses on ensuring safety in self-driving technologies, including standards development (e.g., ANSI/UL 4600), risk assessment, and fault-tolerant software design. Education & Affiliations: Carnegie Mellon University: Faculty in ECE since [year not explicitly listed, but content suggests 20+ years] Principal author of UL 4600 safety standard for autonomous products Former advisor to graduate students (no longer accepting new advisees) Research Interests: Autonomous vehicle safety validation and assurance Reliability of embedded systems software Cyclic redundancy check (CRC) algorithms and error detection Ethical and legal frameworks for automated systems Standards for vehicle automation (e.g., SAE J3016) Key Contributions: Authored influential books on safety in autonomous systems, CRCs, and embedded software Developed safety case methodologies for autonomous vehicles Testified before U.S. Congress on self-driving car safety Labs/Teams: Leads research initiatives on safety-critical systems, including collaboration with industry and government agencies. Maintains active engagement through blogs (Safe Autonomy, Better Embedded Software) and standards bodies.

Nathan Dahlinمشاهده پروفایل

استادیار

• Machine Learning
• Stochastic Control
• Optimization
• +۲ مورد دیگر

Nathan Dahlin is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He holds a BS, MS, and PhD in Electrical Engineering and an MA in Applied Mathematics from the University of Southern California. Prior to joining UAlbany, he was a Postdoctoral Research Associate at the University of Illinois Urbana-Champaign and a senior audio DSP research engineer at Audyssey Laboratories. Dr. Dahlin's research focuses on fundamental problems in machine learning, stochastic control, optimization, and microeconomics, with applications in developing computationally efficient decision-making approaches for smart energy systems. His work emphasizes reliability in uncertain environments and risk management. His recent publications demonstrate strong focus on machine learning applications in control systems, energy management, and algorithm design. Articles frequently address topics like imitation learning, economic dispatch optimization, neural network transformation, and kernel-based learning methods, often with practical implementations in energy systems and smart grids. Dr. Dahlin is active in professional organizations including the Institute of Electrical and Electronics Engineers (IEEE) and the Association for the Advancement of Artificial Intelligence (AAAI). He serves as a reviewer for leading conferences and journals including AAAI Conference on Artificial Intelligence, IEEE Transactions on Control of Network Systems, IEEE Transactions on Power Systems, and IEEE Transactions on Smart Grid.

Jonas Latzمشاهده پروفایل

مدرس

• Bayesian Inference
• Uncertainty Quantification
• Stochastic Processes
• +۵ مورد دیگر

Jonas Latz is a Lecturer in Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, uncertainty quantification, stochastic processes, and their applications in computational mathematics and inverse problems. He has contributed to areas such as physics-informed neural networks, stochastic gradient methods, and medical imaging modeling. Key research interests include developing robust algorithms for Bayesian inverse problems, analyzing stochastic dynamical systems, and advancing numerical methods for partial differential equations. His work bridges theoretical foundations with practical applications in fields like tumor growth modeling and medical image reconstruction. Recent Achievements : Recipient of the SIAM Activity Group Uncertainty Quantification Early Career Prize (2024) SIAM Student Paper Prize (2020) SIGEST Award (2023) Dr. Latz collaborates internationally on topics such as adversarial machine learning, deep learning methods for PDEs, and stochastic sampling techniques. His research emphasizes rigorous mathematical analysis alongside computational innovation.