Find Academic Supervisors

• Ground Penetrating Radar
• Civil Engineering
• Structural Health Monitoring
• +7 more

Amir Alani is a Professor at the University of West London with extensive expertise in Ground Penetrating Radar (GPR) applications for infrastructure and environmental monitoring. His research spans civil engineering, geophysics, and remote sensing, with particular focus on non-destructive testing methodologies for structural health assessment. His research interests center on Ground Penetrating Radar applications across multiple domains including structural health monitoring of bridges and tunnels, tree root mapping and assessment, airport pavement inspection, and flood forecasting systems. Dr. Alani has pioneered innovative approaches using polarimetric GPR systems for tree trunk inspection and developed enhanced data processing frameworks for mapping tree root systems in urban environments. His work integrates GPR with other technologies like InSAR (Interferometric Synthetic Aperture Radar) for comprehensive infrastructure monitoring. Analysis of Dr. Alani's recent publications reveals a strong trend toward integrating multiple sensing technologies for infrastructure monitoring, with significant work on applying GPR to environmental challenges like tree health assessment and urban flood management. His research demonstrates a consistent focus on practical applications of geophysical methods to solve real-world engineering problems, particularly in urban infrastructure contexts. Dr. Alani has been actively involved in advising PhD students and securing research grants, though specific details of these activities are not fully documented in the available publications. His research group appears to collaborate extensively with international partners across Europe. His laboratory work focuses on GPR technology development and application, with particular emphasis on tree root mapping systems and infrastructure health monitoring. The research team has developed specialized processing frameworks for GPR data analysis, including geostatistical approaches and deep learning techniques for root detection.

Naonori UedaView profile

Research Professor

• Machine Learning
• Artificial Intelligence
• Physics-Informed Deep Learning
• +5 more

Naonori Ueda is a Research Professor and Deputy Director at RIKEN Center for Advanced Intelligence Project. He also serves as a Visiting Fellow at NTT Communication Science Laboratories, Research Supervisor for Mathematical Information Platform at Japan Science and Technology Agency (JST), and Visiting Professor at Kobe University's Graduate School of System Informatics. His distinguished career spans academia, government research institutions, and industry collaboration, with significant contributions to advancing artificial intelligence and machine learning applications across multiple scientific domains. Dr. Ueda's research interests focus on the intersection of machine learning, artificial intelligence, and physical sciences. He specializes in physics-informed deep learning approaches that integrate governing physical equations with neural network architectures. His work spans geophysical data analysis, remote sensing applications, computational seismology, and environmental monitoring systems. He has pioneered methods for crustal deformation modeling, earthquake prediction, tsunami inundation forecasting, and satellite imagery analysis using advanced machine learning techniques. His research demonstrates how AI can solve complex scientific problems by bridging the gap between data-driven approaches and physical domain knowledge. His publication record reveals a strong trend toward applying machine learning to solve real-world geophysical and environmental challenges. His recent work shows increasing sophistication in physics-informed neural networks that incorporate domain-specific knowledge into deep learning architectures. The publications span high-impact journals like Nature Communications, demonstrating the interdisciplinary significance of his work. His research consistently focuses on practical applications of AI for disaster prevention, environmental monitoring, and scientific discovery. Fellow of IEICE (Institute of Electronics Information and Communication Engineers) Member of Japan Prize field review committee Selection Committee Member for Brilliant Female Research Award (The Jun Ashida Award) Member of Kyoto Prize Selection Committee Dr. Ueda has secured substantial research funding through multiple government-sponsored projects including RIKEN Pioneering Project 'Prediction Science,' JST AIP Acceleration Research projects on weather prediction and drug discovery, and AMED-funded medical research initiatives. His leadership extends to serving as Sub-project Director for Japan's Moonshot R&D Project. He actively mentors researchers through his roles at RIKEN, NTT, and various academic institutions, fostering the next generation of AI scientists. As Deputy Director of RIKEN Center for Advanced Intelligence Project, Dr. Ueda leads one of Japan's premier AI research initiatives. He also serves on the Advisory Board of Kobe University's Mathematical and Data Science Center and Kyoto University's Graduate School of Informatics. His leadership extends to coordinating the AI Seminar at Osaka Industrial Association and supervising the Keihanna 'Edison Society' at the International Institute for Advanced Studies, demonstrating his commitment to bridging academic research with industrial applications.

Damek DavisView profile

Visiting Associate Professor

• Optimization
• Machine Learning
• Signal Processing
• +4 more

Damek Davis is a Visiting Associate Professor at the Wharton Department of Statistics and Data Science at the University of Pennsylvania and an Associate Professor of Operations Research at Cornell University (currently on leave). He held NSF Postdoctoral Fellowships and completed his PhD in Mathematics at UCLA under Wotao Yin and Stefano Soatto. His research bridges optimization , machine learning , statistics , and signal processing , with a focus on convergence guarantees for stochastic and nonsmooth optimization methods. He has developed accelerated gradient descent algorithms, characterized SGD behavior in nonconvex settings, and formalized avoidance of strict saddle points in proximal methods. Key publications include exponential accelerations of gradient-based techniques and first guarantees for SGD on weakly convex functions. His work has been recognized by the Sloan Research Fellowship INFORMS Optimization Society Young Researchers Prize NSF CAREER Award SIAM Activity Group on Optimization Best Paper Prize Davis actively advises Penn graduate students and serves as an associate editor at Mathematical Programming and Foundations of Computational Mathematics . He emphasizes clear technical writing and public communication, maintaining a blog and lecture notes on optimization theory.

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +3 more

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).

Marc HOFFMANNView profile

Professor

• Statistics
• Nonparametric Statistics
• Stochastic Processes
• +3 more

Marc HOFFMANN is a Professor at Université Paris Dauphine-PSL and holds the Fundamental Chair at the Institut Universitaire de France since 2024. His academic journey includes roles at institutions such as INRIA (2020-2022), École Polytechnique (2007-2015), and Université Gustave Eiffel (2003-2012). Research Focus: Statistics of random processes, nonparametric statistics, and applications in financial modeling and population biology. Key Contributions: Adaptive estimation, confidence bands, inverse problems, rough volatility modeling, and growth-fragmentation processes. Advising: Supervised 19 PhD students with topics spanning statistical inference, Hawkes processes, and stochastic volatility. Collaborations include CIFRE grants with EDF, SCOR, and Banque de France. Scientific Awards : Fundamental Chair at Institut Universitaire de France (2024).

• Image Processing
• Medical Imaging
• Biological Imaging
• +3 more

Michaël Unser is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering , leading the Biomedical Imaging Laboratory . He serves as Academic Director for Imaging at EPFL and contributes to cross-departmental teaching in Microengineering , Mathematics , and Life Sciences Engineering . His research spans Image Processing , Medical Imaging , Wavelets , and Spline-based Modeling , with a focus on multiresolution analysis and single-molecule localization microscopy . He has mentored over 30 PhD students and supervised numerous research projects. Recent publications highlight advancements in super-resolution microscopy , deep learning integration , and inverse problem solving for biomedical imaging. His work emphasizes mathematical rigor and open-source software development for accessible bioimaging tools. IEEE Technical Achievement Award (2008) IEEE EMBS Career Achievement Award (2020) Three ERC Advanced Grants (FUNSP, GlobalBioIm, FunLearn) As Academic Director for Imaging , he leads EPFL's cross-disciplinary imaging initiatives. His teaching includes Fundamentals of Image Analysis and Signals and Systems courses.

Eileen R. MartinView profile

Associate Professor

• Computational Science
• Geophysics
• Applied Mathematics
• +9 more

Eileen R. Martin is an Associate Professor at Colorado School of Mines, jointly appointed in Geophysics and Applied Math and Statistics with a 60/40 split. She collaborates with two industry-aligned consortia: the Center for Wave Phenomena (CWP) and the Center to Advance the Science of Exploration to Reclamation in Mining (CASERM). PhD, Institute for Computational and Mathematical Engineering, Stanford University (2018) MS, Geophysics, Stanford University BS, Mathematics and Computational Physics, University of Texas at Austin Her research bridges computational science and geophysics with applications to subsurface characterization using advanced sensing technologies. She focuses on distributed acoustic sensing (DAS) , seismic imaging , inverse problems , and machine learning for geoscientific data analysis. Recent work explores permafrost thaw monitoring , glacier hydrology , and mine seismicity through scalable algorithms. Selected 2024-2025 Publications : Modeling permafrost thermodynamics with differentiable computing DAS applications in cryoseismic cataloging and glacier monitoring Urban traffic monitoring via multicomponent seismic data Open-source DASCore Python library development Lossy compression effects on seismic data integrity Scientific Awards : NSF CAREER grant SIAM Geosciences Early Career Prize SEG J. Clarence Karcher Award Presidential Early Career Award for Science and Engineering (PECASE) Academic Leadership : Advising 13 graduate students (MS/PhD) across Geophysics, Applied Math, and Hydrologic Engineering Teaching courses in Parallel Scientific Computing, Digital Signal Processing, and Mathematical Geophysics Co-leading weekly group meetings with industry consortia integration Labs & Collaborations : Center for Wave Phenomena (CWP) at Mines CASERM: Mining Exploration to Reclamation Consortium Stanford Exploration Project (alumni) Lawrence Berkeley National Lab affiliate

• Artificial Intelligence
• Medical Imaging
• Cardiac MRI
• +3 more

Qian Tao is an Assistant Professor in the Department of Imaging Physics at the Faculty of Applied Sciences, Delft University of Technology, where she leads research on trustworthy AI methodologies for critical healthcare applications including medical imaging for patient diagnosis and clinical intervention. Education: BSc in Electrical Engineering (with Distinction), Fudan University, Shanghai MSc in Biomedical Engineering (with Distinction), Fudan University, Shanghai PhD, University of Twente, Netherlands (Thesis: "Face Verification for Mobile Personal Devices") Her research integrates artificial intelligence with medical imaging, focusing on cardiac MRI analysis, motion correction, image reconstruction, and AI-driven clinical applications. She develops robust methodologies for medical image processing that address challenges in noise reduction, parameter extraction, and physiological motion artifacts, with emphasis on clinical reliability and diagnostic accuracy. Recent publications demonstrate a cohesive trajectory applying advanced machine learning to cardiac imaging problems, particularly in MRI reconstruction, motion correction, and medical device characterization. Her work bridges deep learning innovation with practical clinical requirements, emphasizing physics-informed AI approaches for healthcare-critical systems. Scientific Awards: 2023 Amazon Research Award for Φ-Generative Medical Imaging by Physics and AI (PhAI) Dr. Tao's research is supported through competitive awards including the Amazon Research Award, and she actively collaborates with clinical partners to translate AI methodologies into medical practice. While specific grant details are limited in the source material, her work demonstrates strong industry-academia partnerships for healthcare technology development. She leads a dedicated research laboratory within the Department of Imaging Physics focused on trustworthy AI for medical imaging, with current projects spanning cardiac MRI reconstruction, motion correction techniques, and AI applications for medical device characterization and clinical diagnostics.

• Machine Learning
• Artificial Intelligence
• Causal Representation Learning
• +3 more

Dr Yuhang Liu is a Researcher at the Australian Institute for Machine Learning, affiliated with the Faculty of Sciences, Engineering and Technology at the University of Adelaide. His research focuses on bridging causality and machine learning , including topics like causal representation learning and multi-domain/modal learning , as well as integrating Bayesian learning with deep learning through techniques such as Bayesian deep learning . Additionally, he explores inverse problems in computer vision and signal processing.

• Composite laminates
• Composite materials
• Finite element analysis
• +8 more

Marco Gherlone is a Full Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He obtained his M.S. degree in Aerospace Engineering at POLITO in 1999 and his Ph.D. in 2003. He became assistant professor in Aircraft Structures in 2005, associate professor in 2014, and full professor in 2020. From 2018 to 2024, he served as coordinator of the Ph.D. program in Aerospace Engineering at POLITO. He has been a visiting researcher at the National Institute of Aerospace (USA) in 2007, 2011, and 2012. His educational background includes: M.S. in Aerospace Engineering, Politecnico di Torino (1999) Ph.D., Politecnico di Torino (2003) Professor Gherlone's research focuses on composite materials and structural analysis, with particular emphasis on: Higher-order theories for multilayered composite and sandwich structures Structural optimization Impact problems Inverse methods for structural health monitoring Analysis of damaged composite structures and damage detection His recent publications reveal a strong focus on shape sensing, structural health monitoring, and advanced finite element methods for composite structures. He has developed expertise in the Refined Zigzag Theory for analyzing complex composite structures, with applications ranging from aerospace to hydrogen storage systems. Professor Gherlone has received notable recognition including: The Composite Structures Award 2007 conferred by Elsevier, Netherlands He serves on the editorial boards of Sensors (since 2021) and Shock & Vibration (since 2018). As an educator, Professor Gherlone has supervised 4 Ph.D. students and co-supervised 2 others, along with 134 M.S. theses and 203 B.S. theses. He currently supervises 3 Ph.D. students and co-supervises 1. His teaching includes "Aircraft structures" and "Aerospace structures dynamics" for the M.S. degree program, and previously taught "Fundamentals of structural mechanics" for the B.S. degree program. He leads the AESDO (Aircraft and Engine Structural Design and Optimization) research group and has been involved in numerous research projects funded by the Piedmont Region, Italian Ministry of University and Research, European Commission, and industry contracts with aerospace and automotive companies.

• Optimal Control Theory
• Numerical Methods
• Kinetic Models
• +5 more

Dr. Jan Bartsch is a Lecturer at the Institute of Mathematics, University of Würzburg, Germany. He has been working as a Scientific Employee at the Institute of Mathematics since 2024. Prior to this, he was a Postdoc at the University of Konstanz from 2021 to 2024 in the Collaborative Research Center 1432 "Fluctuations and Nonlinearities in Classical and Quantum Matter Beyond Equilibrium." Dr. Bartsch's educational background includes: PhD in Scientific Computing from the University of Würzburg (2018-2021), dissertation title: "Theoretical and numerical investigation of optimal control problems governed by kinetic models" Master's degree in Mathematics from the University of Würzburg (2016-2018), thesis title: "Optimal control problems governed by Liouville models - Mathematical analysis and implementation" Bachelor's degree in Computational Mathematics from the University of Würzburg (2013-2016), thesis title: "Optimal Control of Androgen Suppression of Prostate Cancer" His research focuses on optimal control theory, numerical methods for partial differential equations, and kinetic models. Dr. Bartsch specializes in Monte Carlo methods for solving kinetic models, optimal control of stochastic differential equations, and numerical methods for hyperbolic differential equations. His work bridges theoretical mathematics with practical computational approaches, particularly in the context of control problems governed by complex physical models. Dr. Bartsch's recent publications show a strong focus on applying adjoint-based methods to optimal control problems, particularly involving stochastic processes and kinetic models. His research demonstrates expertise in developing numerical frameworks that combine Monte Carlo approaches with control theory to solve complex mathematical problems arising in physics and engineering applications. Contact Information: Email: jan.bartsch@uni-wuerzburg.de Phone: +49 931 31-80733 Office: Building 40 (Mathematics East), Room 00.012, Emil-Fischer-Straße 40, 97074 Würzburg

Janusz FrączekView profile

Professor

• Computer Methods in Mechanics
• Kinematics and Dynamics of Multibody Systems
• Robotics
• +3 more

Prof. Janusz Frączek is a faculty member at Warsaw University of Technology, holding the academic rank of Professor. His research focuses on computational mechanics, kinematics and dynamics of multibody systems, robotics, and biomechanics. University: Warsaw University of Technology Academic Rank: Professor Contact: janusz.fraczek@pw.edu.pl | New Aviation Building, room 322 Research Interests: Computer methods in mechanics Kinematics and dynamics of multibody systems Robotics Biomechanics Teaching Activities: Dynamics of Multibody Systems Surveying and experimental techniques Theory of Machines and Mechanisms I Article Trends: The 15 most recent publications emphasize computational mechanics, robotics, Hamiltonian frameworks, and optimization. Topics include redundant constraints, parallel computing, nonholonomic systems, and augmented Lagrangian methods.

• Electromagnetic Theory
• Inverse Scattering Problems
• Computational Electromagnetics
• +3 more

Prof. Tanju YELKENCİ is an academic who continues his work in electromagnetic theory and forward and inverse scattering problems within the Faculty of Engineering , Department of Electrical and Electronics Engineering . His research bridges theoretical electromagnetics with practical applications in radiation safety and computational modeling. Education Licence (1982-1986): Yıldız Technical University, Department of Electrical Engineering. Master's (1986-1989): Istanbul Technical University, Electronics and Communications Program. Doctorate (1994-1999): Technische Universitaet Wien (Vienna Technical University), Faculty of Electrotechnics. His research focuses on numerical methods in electromagnetics , including specific absorption rate (SAR) analysis for mobile phone radiation, inverse scattering problems in complex geometries, and electromagnetic safety standards . Recent publications highlight his work on computational models for iron slags and inverse scattering in waveguide structures .

Dietmar WeinmannView profile

Researcher

• Theoretical Solid State Physics
• Quantum Effects in Electronic Properties
• Mesoscopic Physics
• +1 more

Dietmar Weinmann is a Senior Researcher at the CNRS (Centre National de la Recherche Scientifique) affiliated with the IPCMS (Institut de Physique et Chimie des Matériaux de Strasbourg) and the University of Strasbourg. His work focuses on theoretical solid-state physics, particularly quantum effects in electronic properties, mesoscopic physics, and quantum transport phenomena. His research explores non-local heating in quantum thermoelectrics, scanning gate microscopy applications in graphene and semiconductor heterostructures, power dissipation asymmetry in quantum point contacts, and orbital magnetization mechanisms in mesoscopic systems. Key themes include electron correlations, spin-orbit interactions, and disorder effects in nanoscale devices. Scientific awards include the Marie Curie Fellowship during his postdoctoral work at SPEC Saclay. He teaches an elective course on Electronics for Quantum Science and Technology at the University of Strasbourg. As a member of the Mesoscopic Quantum Physics team, his research combines theoretical modeling with experimental collaborations on quantum transport imaging and inverse problem solving via machine learning.

• Optimization
• Cryptography
• Artificial Intelligence
• +5 more

Yu Li is a Lecturer at the University of Picardie Jules Verne (UPJV) and a member of research unit UR 4290 “Optimization and Cryptography, AI – OCIA.” His office is located in room 302, reachable by internal telephone extension 5900. Research Interests Dr. Li’s research spans several inter-related domains: Optimization & Control Theory – developing dynamic optimization algorithms for industrial processes such as continuous casting in steel manufacturing. Cryptography & Security – investigating secure and dependable models for cloud and distributed systems. Artificial Intelligence & Robotics – integrating AI perception and decision-making into cloud-connected robotic platforms, including exoskeletons for rehabilitation and autonomous ground vehicles. Cloud & Fog Computing – designing middleware and domain-specific languages that seamlessly connect robotic devices with cloud and edge resources. Publication Trends Over the past decade, Dr. Li’s publication record reveals a clear evolution from foundational work in software architecture and component-based systems (2010-2016) toward cutting-edge applications in cloud/fog-enabled robotics and AI-driven cyber-physical systems (2017-2023). His studies increasingly emphasize real-world deployment, simulation-driven resource estimation, and human-centric interaction in rehabilitation robotics. Scientific Awards & Recognition No specific awards are listed in the provided materials. Advising & Funding No explicit information about supervised students or funded grants is available in the text supplied. Laboratories & Teams He carries out his research within the UR 4290 research unit “Optimization and Cryptography, AI – OCIA” at UPJV, focusing on collaborative projects that bridge mathematics, computer science, and robotics engineering.