Find Academic Supervisors

• Nonlinear Partial Differential Equations
• Geometric Analysis
• Riemannian Manifold Learning
• +7 more

Vahideh Vahidifar is a Research Fellow in the Department of Mathematics at the University of Sussex's School of Mathematical and Physical Sciences, holding a Global Talent Endorsement from EPSRC/UKRI. Her academic journey includes a PhD from Iran University of Science and Technology (2018-2022) and a Research Associate position at Iran's National Elites Foundation (2020-2022). Her research spans the intersection of pure mathematics and applied computational fields, with core expertise in Nonlinear PDEs , Geometric Analysis , and Geometric Deep Learning . Key interests include Riemannian Manifold Learning, Graph Neural Networks, Calculus of Variations, and applications in Material Sciences. Her work connects abstract mathematical frameworks with practical robotics and AI systems. Analysis of her 12 recent publications (2021-2025) reveals a strong focus on gradient estimates for nonlinear parabolic/elliptic equations on metric measure spaces, with significant contributions to Ricci flow theory and geometric analysis. Parallel work applies mathematical principles to surgical robotics, particularly tremor detection and Leap Motion-controlled systems. Global Talent Endorsement by EPSRC/UKRI (2022) Vahidifar maintains active international collaborations across the UK, Canada, and USA. Her robotics research demonstrates practical translation of mathematical frameworks into medical device control systems, particularly in surgical robotics where she has developed novel approaches using Leap Motion sensors and Kalman filtering. Current work bridges geometric analysis with emerging AI architectures like Graph Neural Networks. She leads research in geometric foundations of machine learning while maintaining strong ties to mechanical engineering applications, particularly in smart materials and continuum mechanics. Her laboratory work integrates theoretical mathematics with physical robotics systems for surgical applications.

Matthias KarlbauerView profile

Researcher

• Machine Learning
• Deep Learning
• Physics-Aware Neural Networks
• +12 more

Matthias Karlbauer is a Postdoctoral Researcher in the Cognitive Modeling group at the Wilhelm Schickard Institute for Computer Science, University of Tübingen. He is currently working in the Land-Atmosphere Feedback Initiative (LAFI), focusing on physics-aware machine learning for climate and environmental modeling. His work bridges cognitive science, artificial intelligence, and geophysical systems. PhD in Cognitive Modeling, University of Tübingen (2019–2024) Master of Cognitive Science, University of Tübingen (2015–2018) Bachelor of Cognitive Science, University of Tübingen (2012–2015) Scholar, International Max Planck Research School for Intelligent Systems (IMPRS-IS) His research centers on physics-aware neural networks , spatiotemporal data prediction , and deep learning for environmental systems . He develops models like DISTANA and finite volume neural networks to integrate physical laws into neural architectures, enabling robust forecasting of temperature, geopotential, and fluid dynamics. His interests also extend to generative models, recurrent networks, and graph neural networks applied to climate and sustainability challenges. The recent publications show a strong trend toward integrating partial differential equations with neural networks, denoising spatiotemporal signals , and modeling physical processes using hybrid AI. His work emphasizes interpretability, physical consistency, and real-world applicability in climate science and cognitive modeling. Matthias has actively supervised multiple Bachelor’s and Master’s students on projects related to neural network applications in physics and climate data. He has contributed to teaching as a tutor and lecturer in courses such as Generative and Recurrent Neural Networks , Advanced Artificial Neural Networks , and Graph Neural Networks . While no formal grants are mentioned, his IMPRS-IS affiliation suggests institutional funding support. He is part of the Cognitive Modeling research group at the University of Tübingen, collaborating on projects involving neural modeling of cognitive and physical processes, with a focus on sustainability and climate protection.

Silvia CalderazzoView profile

Researcher

• Bayesian Statistics
• Clinical Trial Design
• Markov Models
• +5 more

Dr. Silvia Calderazzo is a researcher specializing in Bayesian statistics and its applications in clinical and biomedical research. Her work centers on statistical methodology for clinical trial design, dose-response modeling, and inference in complex biological systems. Research Interests: Her primary areas include Bayesian clinical trial design, Markov models, and statistical modeling of dose-response relationships. She applies advanced statistical techniques to problems in toxicology, oncology, and public health, with a strong methodological focus. Publication Trends: Her recent publications (2018–2019) show a consistent emphasis on Bayesian methods, particularly in quantifying prior information, incorporating external data in trials, and modeling biological and toxicological responses. These works appear in top journals such as JAMA , Biometrics , and Archives of Toxicology , reflecting both methodological rigor and clinical relevance. Education: PhD in Statistics, University of Warwick, UK (2017) Master degree in Statistics, University of Padua, Italy (2012) Bachelor degree in Statistics, population and society, University of Padua, Italy (2010) Scientific Awards: Advising and Grants: No information is available regarding students advised or research grants received. Labs and Teams: While specific lab affiliations are not mentioned, her collaborative publications suggest involvement in interdisciplinary research teams focused on biostatistics and translational medicine.

• Strength Engineering Methods
• Fatigue Analysis
• Reliability and Robustness in Product Design
• +3 more

Professor Mårten Olsson holds the Sverker Sjöström Professorship in Reliable Structures at the Royal Institute of Technology (KTH) within the Department of Materials and Structural Mechanics. He has led the department since 2006 and previously held roles such as Director of PhD Education and Head of Solid State Engineering programs. His research focuses on strength engineering methods to enhance product reliability and robustness through advanced fatigue analysis, multiaxial loading models, and probabilistic failure prediction. Olsson has authored over 60 peer-reviewed articles and supervised 14 completed PhD students, contributing to fields like additive manufacturing, engine block dynamics, and composite material behavior. His work integrates computational modeling with experimental validation to address industry challenges in structural reliability and optimization. Research Interests: Olsson’s research spans fatigue analysis, multiaxial loading effects, reliability-based design optimization, and material degradation mechanisms. Notable areas include probabilistic modeling of standing contact fatigue, fracture mechanics in composites, and vibration analysis in engine systems. Key Contributions: His publications highlight advancements in second-order reliability methods, fatigue probability models for complex geometries, and the integration of deformation effects into structural dynamics. Olsson’s methodologies have been applied to aerospace components (e.g., gas turbine blades) and automotive systems (e.g., engine gaskets). Academic Leadership: Beyond research, Olsson has orchestrated educational programs at KTH, including the Master of Science in Engineering Physics, and pioneered competency specialization tracks in Solid State Engineering. His teaching spans undergraduate through doctoral levels, emphasizing practical industrial applications.

Ahmet KırışView profile

Professor

• Fractional Calculus
• Vibration Analysis
• Computational Mathematics
• +3 more

Ahmet Kırış is a Professor in the Department of Mathematics Engineering at Istanbul Technical University, Turkey, with active research output through 2025. His work bridges applied mathematics and engineering, focusing on fractional calculus applications in vibration analysis, image processing, and thermoelasticity. He maintains an h-index of 8 (Scopus) with 26 publications and leads a current research project on image denoising. His research emphasizes fractional differential equations and computational modeling for complex physical systems. Key contributions include vibration analysis of rectangular plates using Chebyshev finite difference methods, fractional image denoising techniques, and electrohydrodynamic flow modeling. He frequently develops numerical strategies like wavelet collocation and fractional total variation to solve nonlinear boundary value problems in multi-dimensional domains. Analysis of his 2024-2025 publications reveals a cohesive trajectory in extending fractional calculus to real-world engineering challenges. Recent work demonstrates innovation in space-time fractional operators for image denoising, coupled thermoelastic plate dynamics, and adiabatic reactor modeling—showcasing how non-local mathematical frameworks solve problems where classical methods fail. Scientific recognition includes: Three instances of TOP 25 Hottest Articles award (2005) TUMTMK 13th National Congress Best Presentation Award (2003) TUMTMK 14th National Congress Best Work Award (2005) TUMTMK 15th National Congress Best Doctoral Second Prize (2007) Professor Kırış has supervised 4 students and currently directs the SRP-funded project "TVFF yöntemi ile gürültülü görüntülerin temizlenmesi" (Cleaning Noisy Images with TVFF Method) from May 2024-February 2025. His collaborative network spans international co-authors from Poland and India, reflecting strong cross-institutional partnerships in computational mathematics.

Sajidah AhmedView profile

Researcher

• Plasma Physics
• Nuclear Fusion
• Turbulence
• +2 more

Sajidah Ahmed is a plasma physicist affiliated with the University of York and actively contributes to nuclear fusion research. Her work focuses on turbulent phenomena in magnetically confined fusion plasmas, particularly the dynamics of scrape-off layer (SOL) fluctuations and their impact on plasma-wall interactions. Doctoral research (2023) on Intermittent fluctuations in the boundary of magnetically confined fusion plasmas . Published studies in leading fusion journals on topics such as filament propagation, MHD instabilities, and pulse superposition modeling. Research Highlights: Her research addresses critical challenges for commercial fusion reactors, including: Characterizing blob-like turbulent structures in SOL regions. Reconstructing intermittent time series using deconvolution methods. Investigating density limits and plasma confinement in devices like Alcator C-Mod and MAST Upgrade.

• Computational Photonics
• Quantum Cascade Devices
• Microwave Engineering
• +3 more

Michael Haider is an Associate Professor at the Technical University of Munich within the TUM School of Computation, Information and Technology , specifically affiliated with the Chair of Computational Photonics (Prof. Christian Jirauschek). His work bridges quantum device modeling, stochastic electromagnetic field analysis, and advanced optoelectronic simulations. Research interests include: Quantum Cascade Lasers and Detectors Josephson Traveling-Wave Parametric Amplifiers Stochastic and Cyclostationary Electromagnetic Field Propagation Terahertz Technology and Frequency Comb Generation Computational Photonics and Microwave Modeling Principal Component Analysis for Electromagnetic Systems His recent publications focus on quantum amplification mechanisms, THz laser dynamics, and stochastic field modeling using Maxwell-Bloch frameworks. Collaborations with Prof. Jirauschek, Prof. Russer, and researchers at ETH Zürich and SPIE conferences highlight his interdisciplinary approach.

• Control Systems
• Automation and Control Engineering
• Process Control
• +7 more

Ing. Pavel Navrátil, Ph.D. is an Assistant Professor at the Faculty of Applied Informatics , Tomas Bata University in Zlín, affiliated with the Institute of Automation and Control Engineering . His work focuses on control systems, optimization, and simulation of industrial processes. Education: Ph.D. in Technical Cybernetics (2004), Ing. in Automation and Control Technology (2000) Research Interests: Pavel specializes in Multivariable Control Systems , Time Delay Systems , and Energy Optimization . His work applies mathematical modeling and simulation to solve complex problems in industrial automation, district heating, and automotive control systems. He also contributes to Computer-Aided Control Education through innovative low-cost laboratory devices. Publications Trends: His articles emphasize Simulation (6 papers), Optimization (5 papers), and Control Systems (12 papers). Key subfields include Time Delay Stability Analysis , Heat Source Modeling , and Educational Technology Development . Labs & Teams: He works within the Institute of Automation and Control Engineering , collaborating with researchers like Libor Pekař and Jaroslav Balátě on control theory and industrial applications.

Geordie RichardsView profile

Assistant Professor

• Nonlinear Partial Differential Equations
• Probability Theory
• Dynamical Systems
• +6 more

Geordie Richards is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph. He holds a PhD in Mathematics from the University of Toronto (2012) and has held academic positions at the University of Minnesota, University of Rochester, Utah State University, and the University of Toronto Mississauga. PhD in Mathematics, University of Toronto (2012) NSERC Graduate Scholarship (2006) Richards' research focuses on the analysis of deterministic and stochastic nonlinear partial differential equations (PDEs) arising in physics and engineering, particularly those modeling fluid dynamics and dispersive wave phenomena. His work integrates techniques from probability theory and dynamical systems to address questions of well-posedness, ergodicity, and uncertainty quantification in PDEs. Key themes include random data Cauchy theory, ergodicity of stochastic PDEs, singular stochastic dispersive equations, and applications to engineering uncertainty analysis. Recent publications highlight his contributions to stochastic hydrodynamic stability, surrogate modeling for nuclear energy systems, and ergodic theory for dispersive equations. His work bridges rigorous PDE analysis with practical applications in turbulence and nuclear reactor design. Mechanical & Aerospace Engineering Teacher of the Year, Utah State University (2019) NSERC Graduate Scholarship (2006) Richards has secured significant research funding including the NRC Faculty Development Grant (2019-2022) as Co-PI and NSF Conference Grant (2017). He teaches with active learning strategies, covering courses from first-year Calculus to advanced research topics.

Fahrettin HorasanView profile

Associate Professor

• Machine Learning
• Data Security
• Recommender Systems
• +3 more

Dr. Fahrettin Horasan is an Associate Professor in the Department of Computer Engineering at the Faculty of Engineering and Natural Sciences . His research focuses on applied computational methods in data security, machine learning, and information retrieval systems. Specialized in medical image watermarking and encryption using matrix decomposition techniques (SVD, ULV) Developed novel collaborative filtering recommender systems with hybrid approaches and matrix approximation Contributions in sentiment analysis for healthcare and e-commerce domains His recent publications highlight interdisciplinary work combining cryptology, biomedical imaging, and large-scale data processing. Notable methods include chaotic system-based encryption, latent semantic indexing, and gradient boosting for darknet traffic analysis. While specific awards, educational background, and student advisement details are not publicly detailed in the provided texts, his technical output reflects active engagement in computational science advancements.

William M. McEneaneyView profile

Professor

• Nonlinear Control
• Numerical Methods for Hamilton-Jacobi Equations
• Max-Plus and Idempotent Analysis
• +9 more

William M. McEneaney is a Professor in the Department of Mechanical and Aerospace Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He maintains an active research program while teaching advanced courses including MAE289C (Spring 2025), MAE142 (Fall 2024), MAE180 (Fall 2024), and MAE288A (Spring 2024). His office is located in 1809 EBU I, and he has been recognized with prestigious fellowships from both SIAM and IEEE. Professor McEneaney's research spans several interconnected areas at the intersection of control theory, applied mathematics, and physics. His primary interests include nonlinear control theory, numerical methods for Hamilton-Jacobi equations, and Max-Plus/Idempotent Analysis. His work extends to astrodynamics and the n-body problem, where he applies principles of stationary action to solve complex orbital mechanics problems. He has made significant contributions to understanding the relationships between stochastic and deterministic control systems, with applications ranging from risk-sensitive filtering to quantum systems. His recent publications demonstrate a strong focus on staticization techniques, which provide computational reductions for systems with low-dimensional nonlinearities. His work bridges theoretical developments in Hamilton-Jacobi PDEs with practical applications in control systems, wave equations, and quantum mechanics. A notable trend is his exploration of connections between optimal control theory and fundamental physics principles, particularly in developing representations for Schrödinger equations using stationary action principles. Fellow of SIAM (Society for Industrial and Applied Mathematics) Fellow of IEEE (Institute of Electrical and Electronics Engineers) Professor McEneaney has supervised numerous graduate students and collaborated extensively with researchers including P.M. Dower, R. Zhao, and Y. Zheng. His research has been supported by various funding sources that enable his work on computational methods for control problems and fundamental solutions. His book "Max-Plus Methods for Nonlinear Control and Estimation" (Birkhauser, 2006) is a significant contribution to the field. He has also co-edited several influential volumes including "Numerical Methods for Optimal Control Problems" (Springer, 2018) and "Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind" (CRC Press, 2007). His research group focuses on developing novel computational approaches for solving complex control problems, particularly those involving Hamilton-Jacobi equations. They maintain strong connections with the broader applied mathematics community through participation in workshops like the 2017 Numerical Methods in Optimal Control workshop. The group's work often intersects with physics applications, particularly in quantum systems and orbital mechanics, where they develop efficient numerical methods for solving two-point boundary value problems.

Jerome JouffroyView profile

Professor

• Control Systems Engineering
• Robotics
• Autonomous Vehicles
• +3 more

Professor Jerome Jouffroy is affiliated with the University of Southern Denmark under the Faculty of Engineering and Science , specializing in Mechanical and Electrical Engineering . His work bridges theoretical control systems research with practical applications in robotics, marine engineering, and mechatronics. Research Focus: Control systems design, autonomous vehicles, marine stabilization, and 3D-printed mechatronics. Collaborations: Active in international networks, particularly in drone cooperative control and marine systems. Projects: Supervised research on drone load handling and energy-efficient UAVs. His publications highlight advancements in quadrotor control , modulating function methods , and marine vessel stabilization . Recent work explores tilt-rotor energy efficiency and cooperative drone systems. Despite extensive media engagement (e.g., defense industry AI applications, offshore wind drone servicing), no formal scientific awards are listed. Teaching includes Control Systems and interdisciplinary team projects.

Wilhelm StannatView profile

Professor

• Stochastic Processes
• Neuroscience
• Stochastic Partial Differential Equations
• +7 more

Prof. Dr. Wilhelm Stannat is a W3 Professor of Mathematics at the Technical University of Berlin (since 2011) and affiliated with the Bernstein Center Berlin. He previously held positions at TU Darmstadt, FernUniversität Hagen, and University of Bielefeld. Research focuses on stochastic processes in neuroscience, stochastic partial differential equations, and stochastic filter theory. Current projects include CRC/Transregio 388 "Rough Analysis, Stochastic Dynamics and Related Fields" (since 2024), SFB 1294 Data Assimilation (subprojects A01/A02), and MATH+ "Data-Driven Stochastic Modelling of Semiconductor Lasers" (until 2024). Editorial roles: Associate Editor for Annali dell'Universita di Ferrara (2010–), Applied Mathematics & Optimization (2023–), and Mathematical Neuroscience and Applications (2021–). He supervises PhD students in stochastic neural networks, optimal control, and data assimilation. Recent courses include Analysis II, Stochastic Partial Differential Equations, and Stochastic Processes in Neuroscience.

• Stochastic Modeling
• Statistical Physics
• Non-Equilibrium Statistical Physics
• +4 more

Cristian Spitoni is an Assistant Professor in the Mathematical Modeling group at the Mathematical Institute within the Faculty of Science at Utrecht University. His office is located in the Hans Freudenthal Building at Budapestlaan 6, Room 510, 3584 CD Utrecht. He maintains an active research profile spanning mathematical physics, statistics, and interdisciplinary applications in medical informatics and neuromorphic computing. His primary research interests include Stochastic Modeling, Statistical Physics, Non-Equilibrium Statistical Physics, and Survival Analysis. Dr. Spitoni's work demonstrates a remarkable interdisciplinary range, bridging theoretical mathematics with practical applications in healthcare and computing. His research trajectory shows a fascinating evolution from fundamental statistical physics problems to medical applications and more recently to neuromorphic computing. Analysis of his recent publications (2023-2025) reveals three major research thrusts: 1) Theoretical work on probabilistic cellular automata and metastability in statistical physics; 2) Medical statistics applications focusing on ICU infections, sepsis, and survival analysis; and 3) Cutting-edge research in neuromorphic computing, particularly on memristors, fluidic circuits, and brain-inspired computing architectures. His work increasingly shows interdisciplinary convergence, with mathematical techniques from statistical physics being applied to both medical informatics and neuromorphic engineering problems. Dr. Spitoni has established productive collaborations with researchers across multiple disciplines, including medical researchers at Utrecht University Medical Center and physicists working on novel computing architectures. His research has been published in high-impact journals spanning physics, mathematics, medical informatics, and computer science, demonstrating the breadth and significance of his contributions. His teaching responsibilities include courses such as Interacting Particle Systems in the Lattice and Continuum, Introduction to Complex Systems, and Mathematical Statistics, reflecting his expertise in both theoretical and applied mathematical modeling.

• Signal Processing
• Statistical Signal Processing
• System Identification
• +5 more

Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.