جست‌وجوی استاد و پژوهشگر

Georg Friedrich Harrerمشاهده پروفایل

استادیار

• Plasma Physics
• Fusion Energy
• Plasma Edge Physics
• +۲ مورد دیگر

Dr. Georg Friedrich Harrer serves as a Research Assistant Professor in the Department of Physics at Hampton University's School of Science. He holds a Ph.D. (2020), M.Sc. (2016), and B.Sc. (2013) from TU Wien, Vienna, Austria, establishing a robust foundation in plasma physics and fusion energy research. His educational credentials include: Doctor of Philosophy (Ph.D.) in Physics, TU Wien, 2020 Master of Science (M.Sc.) in Physics, TU Wien, 2016 Bachelor of Science (B.Sc.) in Physics, TU Wien, 2013 Dr. Harrer's research program centers on advancing fusion energy through investigations of plasma stability, confinement regimes, and diagnostic methodologies. His expertise spans Plasma Edge Physics, Magnetohydrodynamic Stability, and Plasma Diagnostics, with significant contributions to understanding edge-localized modes (ELMs), scrape-off layer dynamics, and plasma-wall interactions in tokamak devices. He maintains active collaborations with major international fusion facilities including ASDEX Upgrade, TCV, MAST-U, and JET. Analysis of his 2022-2024 publications reveals a concentrated focus on developing exhaust scenarios for fusion reactors, optimizing high-confinement regimes without large ELMs, and translating experimental findings from European facilities toward ITER and DEMO reactor designs. His work bridges computational modeling with experimental validation to address critical challenges in plasma stability and heat management. No scientific awards or student advising activities were documented in the source material. Prior to joining Hampton University, Dr. Harrer completed a postdoctoral fellowship at TU Wien and the Max Planck Institute for Plasma Physics / EUROfusion, where he additionally served as Scientific Coordinator for EUROfusion. His pedagogical approach integrates theoretical frameworks with hands-on experimentation and project-based learning to prepare students for careers in advanced physics research and energy technology development.

Michael Cottman Morganمشاهده پروفایل

استاد

• Synoptic Meteorology
• Large Scale Dynamics
• Mid-Latitude Weather Systems
• +۴ مورد دیگر

Michael Cottman Morgan is a Professor in the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin-Madison, affiliated with the College of Letters and Science. He holds a PhD from the Massachusetts Institute of Technology and has served as Chair of the department’s undergraduate program for multiple terms (2005–2007, 2008–2010, 2014–2022). His research focuses on synoptic meteorology, large-scale atmospheric dynamics, and the predictability of mid-latitude and tropical weather systems. Education: PhD, Massachusetts Institute of Technology Dr. Morgan’s research applies adjoint-derived forecast sensitivity fields to analyze weather systems, with a particular emphasis on snowstorms, frontogenesis, and optimal perturbation evolution. His work bridges theoretical frameworks (e.g., Eady model, potential vorticity inversion) and practical weather prediction tools. Recent publications highlight synoptic-dynamic interpretations of sensitivity gradients and predictability challenges in weather modeling. He has contributed extensively to academic service, including leadership roles in the American Meteorological Society (AMS) and the World Meteorological Organization. In 2015, he was honored as an AMS Fellow. His sabbatical experiences include serving as an AMS/UCAR Congressional Science Fellow (2007–2008) and National Science Foundation Division Director for Atmospheric and Geospace Sciences (2010–2014). Scientific Awards: Fellow, American Meteorological Society (2015) Dr. Morgan has supervised multiple PhD students, including Dr. Eric Rappin, Dr. Hyun Mee Kim, and Dr. Chih-Chieh "Jack" Chen. His research group investigates topics such as rotating tank experiments, isentropic trajectories, and non-linear balance PV inversion, with applications to the National Collegiate Weather Forecasting Contest.

Abdellah Ichibaمشاهده پروفایل

دانشیار

• Urban Hydrology
• Flood Management
• Radar Hydrology
• +۴ مورد دیگر

Abdellah Ichiba serves as a Research Associate Professor at the HM&Co laboratory of École des Ponts ParisTech, where he directs hydrological modeling research and leads development of the Multi-Hydro platform. His expertise spans urban hydrology, radar-based flood forecasting, and hydrological modeling, with specialized focus on real-time flood management using radar data integration in urban environments. His academic foundation includes: PhD in Hydrology from Paris-Est University Master degree from École des Ponts ParisTech Engineer degree in Hydraulics from École Hassania des Travaux Publics Dr. Ichiba pioneers radar-enhanced urban flood management , investigating multifractal rainfall patterns, fractal catchment characteristics, and scale effects in hydrological modeling. His work bridges radar meteorology and urban hydrology to improve real-time flood prediction, emphasizing high-resolution data integration and non-linear system responses in complex urban drainage networks. Analysis of his 2015-2017 publications reveals consistent innovation in radar hydrology applications and urban model validation . Key contributions include multifractal radar product comparisons, fractal urban catchment representations, scale-effect quantification, and Multi-Hydro validation using dual-band radar data. These studies collectively advance urban flood resilience through rigorous data-model integration frameworks. His research portfolio features leadership in major collaborative initiatives: INTERREG RainGain (2011-2015): Radar-based urban flood management KIC-Climat Blue Green Dream (2012-2015): Climate adaptation infrastructure FP7 SMARTesT (2010-2014): Smart urban water solutions ANR TRAFIPOLLU (2013-2016): Urban traffic-pollution dynamics Dr. Ichiba drives research valorisation through official Multi-Hydro model deposits (v2.1 with French Programs Protection Agency) and v3.0 release at the 2017 EGU General Assembly. He actively engages the scientific community via conference organization including RainGain Cop21 (2015) and EGU sessions on Multi-Hydro (2017), fostering collaboration between researchers, water managers, and policymakers.

Alicia Gonzalez-Buelgaمشاهده پروفایل

مدرس ارشد

• Dynamics and Control
• Energy Harvesting
• Non-Linear Dynamics
• +۴ مورد دیگر

Dr. Alicia Gonzalez-Buelga is a Senior Lecturer at the University of Bristol, affiliated with the School of Electrical, Electronic and Mechanical Engineering. She is an active member of the Dynamics and Control research group and contributes to multiple initiatives within the Cabot Institute for the Environment, including City Futures Research, Natural Hazards and Disasters Research, and Environmental Change Research. Her work bridges engineering dynamics with environmental challenges, focusing on practical solutions for seismic protection and sustainable infrastructure. Education: Bachelor of Engineering (BEng) from University of Oviedo, Spain Doctor of Philosophy (PhD) from University of Oviedo, Spain Dr. Gonzalez-Buelga's research spans two primary domains: structural dynamics and engineering education. In structural dynamics, she specializes in vibration control systems, energy harvesting devices, and seismic isolation strategies, with particular focus on developing low-cost solutions applicable in developing countries. Her work on rocking columns, tuned mass dampers, and inerter-based systems demonstrates innovative approaches to structural resilience. In engineering education, she investigates student experiences, particularly in online and blended learning environments, and develops outreach programs to enhance engagement with mechanical engineering. Her recent publications reveal a trend toward integrating practical engineering solutions with educational innovation. The research shows increasing attention to post-pandemic educational approaches while maintaining strong focus on vibration control and seismic protection technologies. Her work bridges theoretical dynamics with real-world applications, particularly in developing affordable seismic isolation systems and advancing engineering pedagogy through partnership and co-creation methodologies. Dr. Gonzalez-Buelga actively contributes to professional service, including peer review activities for journals such as Vibration. Her research collaborations span multiple disciplines, connecting engineering dynamics with environmental challenges through the Cabot Institute. She is involved with the Dynamics and Control research group at Bristol and contributes to several Cabot Institute initiatives focused on urban resilience and environmental change. Her work connects structural engineering solutions with broader environmental sustainability goals, particularly in the context of natural disaster mitigation and sustainable infrastructure development.

Hulusi Kafalıgönülمشاهده پروفایل

استاد مدعو

• Systems Neuroscience
• Visual Perception
• Multisensory Integration
• +۴ مورد دیگر

Hulusi Kafalıgönül is an Adjunct Professor at Bilkent University, holding dual affiliations with the National Magnetic Resonance Research Center (UMRAM) and the Aysel Sabuncu Brain Research Center. He directs the Cortical Dynamics and Perception Lab and serves as faculty in the Interdisciplinary Neuroscience Program. His academic credentials include: Ph.D. in Electrical & Computer Engineering, University of Houston (2007) Certificate in Cognitive Science, Center for Neuro-Engineering & Cognitive Science, University of Houston (2007) B.S. in Electrical & Electronics Engineering, Boğaziçi University, Istanbul (2003) Dr. Kafalıgönül's research integrates experimental and theoretical approaches to decode neural mechanisms of visual perception. His lab employs psychophysics, brain imaging, and computational modeling using neural networks and non-linear dynamics to investigate sensory processing hierarchies. He posits that visual system analysis provides fundamental insights into how neural activity generates perception and behavior, leveraging that one-third of the human cortex processes visual information. His scientific recognition includes: Innovative Research Award from Kavli Institute for Brain and Mind, UC San Diego He oversees active mentorship of 4 Ph.D. students and 2 Master's students while having graduated approximately 10 researchers. His work receives substantial funding from TÜBİTAK, EU Marie Curie Actions, and the British Academy, enabling cross-disciplinary investigations into sensory systems. The Cortical Dynamics and Perception Lab operates within the Aysel Sabuncu Brain Research Center ecosystem, maintaining formal affiliations with the Neuroscience and Neurotechnology Center of Excellence (NÖROM), UMRAM, and the National Nanotechnology Research Center (UNAM), facilitating multimodal neuroscience research.

Mattia Mattioniمشاهده پروفایل

دانشیار

• Control of Networked Systems
• Hybrid Dynamical Systems
• Nonlinear Control
• +۳ مورد دیگر

Mattia Mattioni is Associate Professor at the DIAG department of Sapienza University of Rome, where he carries out research and teaching in control engineering. Since 2022 he serves as Associate Editor for the IEEE Control Systems Society Conference Editorial Board and since 2020 as Corresponding Member of the IFAC Technical Committee on Non-linear Control Systems. Education: Ph.D. in Systems and Control Theory (2018) – joint program, Sapienza Università di Roma & Université Paris-Sud Double M.Sc. "Master de Recherche en Automatique, Traitement du Signal et des Images" (2015) – Université Paris-Sud M.Sc. (Laurea Magistrale) in Control Engineering (2014) – Sapienza Università di Roma, magna cum laude B.Sc. (Laurea) in Control Engineering (2012) – Sapienza Università di Roma, magna cum laude Research interests revolve around hybrid systems , networked control , nonlinear dynamics under sampling and time-delays , with emphasis on multi-agent consensus , passivity-based design and string stability of vehicle platoons . His work blends theoretical developments (Lyapunov and geometric techniques) with practical digital implementation issues, producing tools applicable to robotics, automotive formations and general sampled-data engineering systems. Recent publications (2024-2025) concentrate on digital passivity-based control , asynchronous sampled-data consensus , string-stable platooning and multi-consensus in hybrid networks , showing a clear trajectory toward robust, distributed and energy-aware control solutions for cyber-physical systems. Scientific awards: Premio SIDRA "Miglior Tesi di Dottorato 2019" – best Italian Ph.D. thesis in Automatic Control Finalist, Prix des meilleures thèses du GDR MACS & Club EEA 2019 – best French-university Ph.D. thesis in Automatic Control Editorial & committee service: Associate Editor, IEEE CSS Conference Editorial Board (2022-present) Corresponding Member, IFAC TC 2.5 Non-linear Control Systems (2020-present) No graduate students or grant details are disclosed in the supplied material; his office is located in room A205, DIAG, Via Ariosto 25, Rome.

Veronica Piccialliمشاهده پروفایل

استاد

• Nonlinear Optimization
• Semidefinite Programming
• Game Theory
• +۶ مورد دیگر

Veronica Piccialli is a Full Professor at the Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG) at Sapienza University of Rome. She teaches Geometry I for Management Engineering and Optimization Methods for Machine Learning for Data Science. Previously, she was Associate Professor at the University of Rome Tor Vergata (2020-2021) and Researcher there from 2008 to 2020. She serves as Associate Editor for INFORMS Journal on Computing (since 2019) and EURO Journal on Computational Optimization (since 2021). Dr. Piccialli earned her degree in Computer Engineering (summa cum laude) and PhD in Operations Research from Sapienza University of Rome in 2000 and 2004 respectively. In 2006, she completed a postdoc at the Combinatorics & Optimization department of the University of Waterloo, Canada. She obtained Italian national scientific qualifications as Associate Professor in 2013 and as Full Professor in 2017. Her research focuses on the intersection of optimization and machine learning, with particular expertise in nonlinear optimization, semidefinite programming, and mixed integer nonlinear programming. She applies these methods to diverse areas including Brain Computer Interfaces, electric consumption disaggregation, and process engineering for membrane systems. Her work demonstrates how advanced optimization techniques can enhance machine learning algorithms and solve complex engineering problems. Her recent publications show a strong trend toward integrating optimization with machine learning, particularly in clustering algorithms, support vector machines, and neural networks. She has developed exact algorithms for semi-supervised learning problems and applied optimization techniques to real-world challenges in logistics, energy systems, and process engineering. Her interdisciplinary approach bridges theoretical advances in optimization with practical applications across multiple domains. Dr. Piccialli has authored or co-authored over 40 articles in prestigious journals including Mathematical Programming, SIAM Journal on Optimization, IEEE Transactions on Neural Networks and Learning Systems, and Computational Optimization and Applications. She has also contributed 3 refereed book chapters to international publications. As an educator, she supervises student research and teaches advanced courses in optimization methods and geometry. Her teaching materials demonstrate a commitment to connecting theoretical concepts with practical applications, particularly in data science and machine learning contexts. Her current research involves collaborations with Université de Lorraine on membrane systems for gas filtration and with RFI (Rete Ferroviaria Italiana) on optimizing last-mile facilities for freight trains, demonstrating the real-world impact of her optimization expertise.

• Radiation Dosimetry
• Medical Physics
• FLASH Radiotherapy
• +۷ مورد دیگر

Maurizio Marrale serves as Associate Professor in the Department of Physics and Chemistry at the University of Palermo (Unipa), holding position PHYS-06/A. His office is located at the Department of Physics and Chemistry "Emilio Segre" (DIFC), Viale delle Scienze, Building 18, with regular office hours on Thursdays from 3:00 PM to 5:00 PM. His research spans radiation dosimetry, medical physics, and advanced imaging techniques. Key focus areas include FLASH radiotherapy verification using alanine/EPR dosimetry and silicon carbide detectors, transcranial magnetic resonance-guided focused ultrasound for neurological disorders (particularly essential tremor), and MRI-based dosimetry with Fricke gels. His work bridges physics principles with clinical applications in radiation oncology and diagnostic imaging. Analysis of his 15 most recent publications reveals strong emphasis on Ultra-high dose rate (UHDR) beam characterization for FLASH radiotherapy Development of novel dosimetry systems (SiC detectors, ionoacoustic sensors) Advanced neuroimaging techniques for thalamic targeting Diffusion correction methodologies in 3D dosimeters Clinical MRI applications for neurological disorders His research demonstrates consistent integration of computational modeling (Geant4, deep learning) with experimental validation. As principal supervisor, he has guided over 35 master's theses at Unipa between 2009-2024, primarily in Physics and Medical Radiology Techniques programs. His supervisory work focuses on radiation dosimetry applications, MRI physics, and radiotherapy technology validation.

Oliver Rheinbachمشاهده پروفایل

استاد

• High-Performance Computing
• Numerical Mathematics
• Domain Decomposition Methods
• +۸ مورد دیگر

Oliver Rheinbach is a Professor of High-Performance Computing in Continuum Mechanics at the Institute of Numerical Mathematics and Optimization, Faculty of Mathematics and Computer Science, TU Bergakademie Freiberg. He also serves as the Pro-Dean of the faculty and the Scientific Director of the University Computing Center (URZ). His academic affiliations reflect a deep integration of computational mathematics and high-performance computing in engineering and biomedical applications. Research Interests: His primary fields include High-Performance Computing, Numerical Mathematics, Domain Decomposition Methods, Finite Element Methods, and Fluid-Structure Interaction. His work bridges theoretical numerical analysis with practical applications in biomechanics, materials science, and exascale computing. He actively explores the co-design of algorithms and solvers for next-generation supercomputers. Research Trends from Publications: The 15 most recent articles reveal a consistent focus on scalable domain decomposition methods (e.g., BDDC, FETI-DP), particularly for nonlinear and time-dependent problems in solid and fluid mechanics. There is a strong emphasis on parallel algorithms for exascale systems, with applications in hemodynamics, glacier modeling, and cardiac simulation. Recent work integrates machine learning into inverse problems, indicating a forward-looking research direction. Scientific Awards and Recognition: h-index of 28 (Google Scholar), 16 (zbMATH) Active participation in DFG and BMBF-funded priority programs Leadership roles in major academic and computing infrastructures Advising and Grants: While no formal list of students is provided, his leadership in research projects such as SPP2311, SPP2256, and SCALEXA suggests extensive mentorship and collaboration. He has secured substantial grant funding from the DFG (e.g., EXASTEEL, Domain-Decomposition-Based FSI) and BMBF (SCALEXA, OERSax), reflecting national recognition of his research impact. Labs and Teams: As Scientific Director of the URZ, he leads the university's high-performance computing infrastructure. He is deeply involved in the Faculty’s Compute Cluster and collaborates with interdisciplinary teams in computational biomechanics and materials science. His work in the SPP2256 and SPP2311 consortia involves national and international research networks focused on variational modeling and cardiovascular simulation.

Raj Rao Nadakuditiمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Random Matrix Theory
• Machine Learning
• +۷ مورد دیگر

Raj Rao Nadakuditi is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, College of Engineering. His research bridges statistical signal processing, random matrix theory, and machine learning with applications in sensing, imaging, and communications. Research Interests: Statistical Signal Processing and Learning Random Matrix Theory and Applications Signal Processing for Sensor Networks Light Transport Through Opaque Media Eigen-wavefront Imaging for Brain Circuit Mapping High-Dimensional Data Analysis His recent work focuses on overcoming fundamental limits in imaging through highly scattering media, such as brain tissue, by leveraging eigen-wavefronts and algorithmic innovations. This has direct implications for the BRAIN Initiative and optical neuroimaging. Publications Trends: His most recent articles reflect a strong emphasis on the application of random matrix theory to signal detection, covariance estimation, and imaging through random media. Key themes include eigenvalue analysis, high-dimensional statistics, spectral methods, and wavefront shaping for deep-tissue imaging. Scientific Awards: DARPA Young Faculty Award (2014) ONR Young Investigator Program Award AFOSR Young Investigator Program Award Advising and Grants: Prof. Nadakuditi has advised students in advanced signal processing and machine learning. His research is generously supported by the National Science Foundation, DARPA, Office of Naval Research, Air Force Office of Scientific Research, and Army Research Office’s MURI program. He has led projects on fundamental limits of imaging and robust signal processing in low-SNR environments. Labs and Teams: He collaborates with Prof. Eric Michielssen on computational electromagnetics and is part of the broader EECS research community at the University of Michigan focused on autonomy, AI, and precision health. His group works on theoretical and applied aspects of sensing and imaging systems.

Nathaniel Johnstonمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Quantum Entanglement
• Matrix Analysis
• +۱۶ مورد دیگر

Nathaniel Johnston is a researcher specializing in quantum information theory, combinatorics, and computational mathematics. He is the creator of QETLAB , a MATLAB toolbox for quantum entanglement, and the founder of ConwayLife.com , a major resource on cellular automata including the comprehensive LifeWiki. He also maintains StatDistributions.com and PlatPrices.com. His research spans: Quantum entanglement and quantum error correction Superpermutations and combinatorics on words Conway’s Game of Life and cellular automata Matrix norms and linear preserver problems Applications of linear algebra to puzzles like Lights Out Geometric optimization and calculus problems He produces educational math videos using Manim, covering advanced topics in an accessible format. His scholarly output includes significant contributions to open problems in mathematics and software tools widely used in the research community. He actively engages with users via GitHub and social media (Bluesky). Notable Projects: QETLAB : A MATLAB toolbox for quantum entanglement, actively developed and cited in research. ConwayLife.com / LifeWiki : The largest encyclopedia of Conway’s Game of Life patterns. StatDistributions.com : A tool for calculating p-values and test statistics. PlatPrices.com : A non-academic site tracking PlayStation game prices and trophies. Scientific Contributions: Disproved the minimal superpermutation conjecture. Developed algorithms for computing hard-to-compute matrix norms. Created accessible explanations of complex topics via video and interactive tools. While his current institutional affiliation is not stated, his body of work, software development, and scholarly communication confirm his status as an active academic researcher in applied and computational mathematics.

Hermann F. Faselمشاهده پروفایل

استاد

• Aerospace Engineering
• Fluid Dynamics
• Hypersonic Flow
• +۷ مورد دیگر

Hermann F. Fasel is a Professor in the Department of Aerospace and Mechanical Engineering at the University of Arizona and a member of the Graduate Faculty. His research is centered on high-speed aerodynamics, boundary-layer transition, and computational fluid dynamics, utilizing advanced simulation techniques such as direct numerical simulation (DNS) and linear stability analysis. Research Interests: Fasel's work spans fundamental and applied aspects of fluid dynamics, particularly in hypersonic and transonic regimes. His expertise includes boundary-layer instability, shock-wave interactions, active flow control, and transition prediction using machine learning. He investigates complex flow phenomena over various geometries such as flat plates, cones, swept wings, and hollow cylinders. The recent publications highlight a consistent focus on high-speed boundary-layer transition, with increasing integration of data-driven methods like the Mori-Zwanzig formalism for modal decomposition. His research bridges theoretical analysis, numerical simulation, and experimental validation, often in collaboration with teams from other institutions. Scientific Awards: AIAA Fellow (2021) AIAA Fluid Dynamics Award (2019) Ludwig Prandtl Ring (2018) Advising and Grants: Fasel has advised numerous graduate students and researchers, including C. Hader, A. Haas, and S. Hosseinverdi. His research is supported by major funding agencies such as the Office of Naval Research (ONR), Air Force Office of Scientific Research (AFOSR), and Army Research Office (ARO), as evidenced by his regular participation in their program reviews and workshops. Labs and Teams: He leads a research group focused on high-fidelity simulations of compressible flows, collaborating with experimentalists and theorists. His team develops and applies advanced CFD solvers and participates in national and international forums such as AIAA, IUTAM, and ERCOFTAC.

• Artificial Intelligence
• Optimization
• Machine Learning
• +۴ مورد دیگر

Sebastian Pokutta is the David M. McKenney Family Associate Professor at Georgia Tech's Stewart School of Industrial & Systems Engineering. He serves as director of the Interactive Optimization and Learning Lab and associate director of Georgia Tech's Center for Machine Learning. His research spans Artificial Intelligence , Optimization , and Machine Learning with applications in quantum computing, social science modeling, and sustainability. Mathematics M.Sc. and PhD from University of Duisburg-Essen (2003-2005) Postdoc at MIT Operations Research Center Former roles at IBM ILOG, TU Darmstadt, MIT, and University of Erlangen-Nürnberg Recent research focuses on Frank-Wolfe algorithms for optimization, quantum entanglement thresholds , and human-AI co-creativity . His work has been published in Transactions on Mathematical Software , Mathematics of Operations Research , and ICML proceedings . Scientific awards include: 2025 Science Prize from the Association for Pediatric Orthopedics (VKO) 2025 Land-Doing MIP Computational Competition winner He co-leads the Cluster of Excellence MATH+ and directs research at the Interactive Optimization and Learning Lab, focusing on real-world deployments like biomass estimation from satellite data and mathematical discovery with AI . His recent blog posts discuss federated learning robustness and Frank-Wolfe.jl algorithmic improvements.

• Stochastic Control
• Computational Finance
• Machine Learning
• +۷ مورد دیگر

Christoph Reisinger is a Full Professor of Applied Mathematics at the Mathematical Institute, University of Oxford, a position he has held since 2017 (previously Associate Professor from 2006-2017). He is a Fellow at St Catherine's College (since 2006) and an Associate Member of the Oxford-Man Institute of Quantitative Finance (since 2008). He serves as Director of Graduate Studies (Teaching) at the Mathematical Institute (2023-2026) and was Co-Director of the Centre for Doctoral Training on The Mathematics of Random Systems (2022/23). He is also a member of the Mathematical and Computational Finance Group (since 2006) and the Data Science Group (since 2018). Professor Reisinger's research lies at the intersection of stochastic modeling, computational mathematics, and machine learning. His work spans stochastic control, numerical analysis of nonlinear PDEs, mathematical foundations of reinforcement learning and deep learning, and computational finance. Current research focuses on approximation and reinforcement learning of stochastic control problems, mathematical analysis of neural networks, mean-field limits of particle systems, numerical approximation of high-dimensional SDEs and PDEs, and mathematical modeling of financial markets. His publication record shows a strong trend toward integrating machine learning with traditional computational finance methods. Recent work explores policy gradient methods for stochastic control, mean-field game theory applications to financial markets, and novel numerical schemes for McKean-Vlasov equations. His research bridges theoretical mathematics with practical applications in quantitative finance, particularly in option pricing, risk management, and market microstructure modeling. Professor Reisinger has supervised numerous doctoral students, many of whom have gone on to prestigious positions in academia and industry. His current PhD students include Boris Baros, William Gibson, Matthieu Meunier, Zihan Guo, Maria Olympia Tsianni, Filippo De Angelis, and Michael Giegrich. His past students include several who have received awards such as the Graduate Paper Competition First Prize and G-Research DPhil Prizes. He serves as Editor-in-Chief of the Journal of Computational Finance (since 2018) and holds associate editor positions at Applied Mathematics and Optimization (2022-2024), Applied Mathematical Finance (since 2018), and International Journal of Computer Mathematics (since 2012).

Daniel Kuhnمشاهده پروفایل

استاد

• Data-driven Optimization
• Stochastic Programming
• Robust Optimization
• +۳ مورد دیگر

Daniel Kuhn is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he holds the Chair of Risk Analytics and Optimization in the College of Management of Technology. His research focuses on developing computational methods for data-driven decision-making under uncertainty, with applications in engineered systems, machine learning, and finance. Previously, he held positions at Imperial College London and Stanford University. Education includes: PhD in Economics, University of St. Gallen MSc in Theoretical Physics, ETH Zurich Research interests span data-driven optimization , stochastic programming , and robust decision-making frameworks . His work develops computationally tractable methods for uncertainty quantification in complex systems, bridging operations research with statistical learning. Current investigations focus on distributionally robust optimization using Wasserstein metrics and applications in energy markets and fair machine learning. Publication analysis reveals three primary trends: 1) Fundamental advances in distributionally robust optimization theory, 2) Machine learning applications with uncertainty guarantees, and 3) Energy system optimization under regulatory constraints. His methodological work consistently emphasizes computational tractability and practical applicability. As lab director of the Risk Analytics and Optimization group, he leads research on: Stochastic control systems Data-driven decision frameworks Robust machine learning Current PhD students include researchers working on federated learning fairness, optimal power flow, and reinforcement learning theory. Past graduates have made significant contributions to Wasserstein distributionally robust optimization and vehicle-to-grid frequency regulation.