Find Academic Supervisors

• Formal Verification
• Parallel and Distributed Systems
• GPU Programming
• +4 more

GANESH GOPALAKRISHNAN is a Professor of Computer Science at the University of Utah's School of Computing. His work focuses on formal verification of parallel/distributed systems, GPU programming, and numerical error analysis. He has contributed to tools like ISP for MPI verification, ARCHER for OpenMP race detection, and FLiT for floating-point consistency testing. His research spans theoretical foundations (e.g., concurrency models) and practical applications (e.g., GPU error analysis). Recent work includes advancing formal methods for mixed-precision computing and resilience in exascale systems. Notable projects include rigorous error estimation for floating-point operations and compiler-assisted verification techniques. Research Interests: Formal Verification of Parallel Systems | GPU & HPC Correctness | Floating-Point Numerical Analysis | Concurrency Bugs | Tools for Distributed Systems. Current work emphasizes hybrid approaches combining formal methods with dynamic analysis to address emerging challenges in heterogeneous computing architectures. Articles Trends: Recent publications (2020–2025) emphasize GPU verification (data races, error analysis), mixed-precision computing (matrix operations, tensor cores), and resilience in HPC systems. Tools like FPDetect and BinFPE highlight practical contributions to error detection in production runs. Workshops (DOE/NSF) indicate leadership in defining correctness strategies for exascale computing. Labs/Teams: Leads research groups focused on formal methods for parallel computing and numerical system reliability. Collaborations include Argonne National Lab, NVIDIA, and LLNL on verification tools and HPC correctness frameworks.

Friedrich FraundorferView profile

Professor

• 3D Computer Vision
• Robot Vision
• Multi View Geometry
• +4 more

Friedrich Fraundorfer is a Professor at Graz University of Technology, specializing in 3D Computer Vision and Autonomous Systems at the Institute of Computer Graphics and Vision (ICG). He has held academic positions at institutions including ETH Zurich, University of North Carolina at Chapel Hill, and Technische Universität München, where he served as Deputy Director of the Chair of Remote Sensing Technology. Research : Focuses on Micro Aerial Vehicle (MAV) autonomy, Visual-Inertial Fusion, and Multi-View Geometry. Projects : Led EU-funded SFly (autonomous MAVs for search-and-rescue), SNF MAV (camera-only 3D mapping), and VCharge (vision-based self-driving cars). Teaching : Offers courses like 'Camera Drones' and 'Mathematical Principles in Vision.' His Pixhawk project created open-source MAV platforms adopted globally. Key Collaborations : With NVIDIA, Volkswagen AG, University of Zurich, and German Space and Aerospace Center (DLR). His students (e.g., Dominik Hirner, Rafael Weilharter) have published on lightweight CNNs for stereo vision and self-supervised 3D reconstruction.

• Biomedical Engineering
• Medical Physics
• Therapeutic Ultrasound
• +4 more

Douglas Christensen is a Professor in both the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering at the University of Utah. His academic career spans over five decades, beginning with a Ph.D. in Electrical Engineering from the University of Utah in 1967. He has authored or co-authored multiple books, including Ultrasonic Bioinstrumentation and Basic Introduction to Bioelectromagnetics . Education: BS, Electrical Engineering, Brigham Young University (1962) MS, Electrical Engineering, Stanford University (1963) PhD, Electrical Engineering, University of Utah (1967) Postdoctoral Fellowship, University of Washington (1972-1974) Research Interests: Therapeutic ultrasound Optical biosensors Acoustic radiation force Phase aberration correction 3D MR thermometry Medical imaging systems Ultrasound wave propagation Scientific Awards: Fellow, American Institute for Medical and Biological Engineering (2001) Distinguished Teaching Award, University of Utah (2004) Lifetime Achievement Award, ECE Department (2020) ECE Chair's Award (2022) His recent publications focus on improving transcranial ultrasound simulations through skull geometry modeling, optimizing phase aberration correction for breast MRgFUS, and enhancing MR thermometry accuracy via model predictive filtering. He has secured significant NIH grants for HIFU bioengineering and transcranial MRgFUS innovations. Christensen teaches foundational courses in biomedical engineering and electrical circuits, with active roles in 2023-2024 academic terms.

Filip SzczepankiewiczView profile

Associate Professor

• Medical Imaging
• Radiology
• Diffusion MRI
• +10 more

Filip Szczepankiewicz is an Associate Professor and Associate Senior Lecturer in Medical Radiation Physics at Lund University, Sweden. He serves as Principal Investigator for eSSENCE: The e-Science Collaboration and LUCC: Lund University Cancer Centre, with active research projects spanning advanced MRI techniques for neuroscience, cancer imaging, and microstructure analysis. His research focuses on several key areas: Advanced diffusion MRI techniques for microstructure imaging Brain imaging applications in neuroscience and cognitive disorders Prostate cancer imaging and biomarker development Cardiac diffusion tensor imaging MRI physics and methodology development Dr. Szczepankiewicz's recent publications demonstrate sophisticated multi-dimensional MRI approaches that separate different biophysical processes within tissues. His work bridges physics, engineering, and clinical applications, particularly in neurological disorders and cancer diagnostics, with increasing emphasis on techniques that probe tissue microstructure with unprecedented detail. He leads significant research projects including: eSSENCE@LU 9:2 - Establishing the link between prostate cancer microstructure and MRI (2023-2026) Prostate Cancer Imaging Group: Multidimensional MRI-based biomarkers (2022-2030) eSSENCE@LU 6:4 - Accelerated microstructure imaging (2020-2021) His work contributes to UN Sustainable Development Goals related to good health and well-being, with documented research collaborations across multiple institutions and countries.

Scott A. HughesView profile

Professor

• Astrophysical General Relativity
• Gravitational Wave Physics
• Black Hole Astrophysics
• +7 more

Scott A. Hughes is a Professor in the Department of Physics at the Massachusetts Institute of Technology (MIT), School of Science. He is affiliated with the MIT Kavli Institute for Astrophysics & Space Research and leads the Hughes Group, focusing on astrophysical general relativity. He previously served as the Astrophysics Division Head (2019–2023) and held the Adam J. Burgasser Chair in Astrophysics and the Class of 1956 Career Development Professorship. Education: B.A. in Physics, Cornell University (1993); Ph.D. in Physics, California Institute of Technology (Caltech), advised by Kip Thorne. Postdoctoral Experience: University of Illinois, Caltech, Kavli Institute for Theoretical Physics (UCSB). Joined MIT Faculty: January 2003. His research centers on astrophysical general relativity , with a focus on black holes , gravitational-wave sources , and strong-field gravity . He investigates waveform modeling, testing black hole spacetimes, and cosmological applications of gravitational waves ('standard sirens'). His work integrates high-performance computing and numerical relativity, contributing to LIGO science. He has authored numerous influential publications on extreme mass-ratio inspirals, ringdown spectroscopy, and gravitational wave cosmology. Analysis of his recent publications reveals a strong trend toward gravitational wave astrophysics , combining theoretical modeling with observational implications for LIGO and future space-based detectors like LISA. His work spans black hole dynamics , numerical relativity , cosmological parameter estimation , and tests of general relativity . There is a growing integration of machine learning and data analysis techniques in his recent work. Scientific Awards and Honors: American Physical Society Fellow (2012) John Simon Guggenheim Fellow (2012) Margaret MacVicar Faculty Fellow, MIT (2017–2027) Buechner Outstanding Advisor Award, MIT Physics (2016) MIT School of Science Prize for Excellence in Undergraduate Teaching (2005–2006) National Science Foundation Career Grant (2005) Buechner Teaching Prize, MIT Physics (2005) Class of 1956 Career Development Professor, MIT (2004) Professor Hughes is a dedicated educator and mentor. He has received multiple teaching awards and is recognized as an outstanding advisor. He teaches core courses including graduate 8.962 (General Relativity) , undergraduate 8.033 (Relativity) , and 8.022 (Electricity and Magnetism) . He has developed extensive open lecture notes for these courses. He is also a first-generation college graduate and actively supports first-generation students at MIT. He leads a research group and mentors graduate students, though specific student names are not listed in the provided text. His research has been supported by the NSF and other grants. He is actively involved in the international gravitational wave community, regularly presenting at major conferences and serving on thesis committees abroad. Laboratories and Research Groups: Hughes Group - Astrophysical General Relativity @ MIT (gmunu.mit.edu), affiliated with the MIT Kavli Institute for Astrophysics & Space Research.

• Medical Image Processing
• Computational Anatomy
• Deep Learning
• +5 more

HONTANI Hidekata is a Professor at Nagoya Institute of Technology's Department of Information Engineering, Media Informatics Field, and Graduate School of Engineering, Media Informatics Program. He is also affiliated with the Advanced Medical Physics and IT Research Center and the Center for Research and Development in Higher Engineering-Education. His career includes academic roles at institutions like Yamagata University and The University of Tokyo. Doctorate in Engineering from The University of Tokyo (2000) Professional memberships in IEEE, SICE, IEICE, and IPSJ Research focuses on Medical Image Processing and Computational Anatomy , with recent advancements in deep learning applications for pathology image analysis, TMS electromagnetic modeling, and spatiotemporal cancer dynamics. He pioneered techniques for counterfactual image generation in lymphoma pathology and adaptive sparse regularization for signal processing. Notable trends in his publications (2017-2024) include AI-driven histopathology , generative models for medical imaging, and tumor microenvironment analysis using multi-scale MRI-pathology fusion. His work bridges machine learning , computational anatomy , and clinical applications . Scientific Awards : Multiple Japan Society of Medical Imaging and Information Sciences awards (2017-2024), Cum Laude Poster Award at SPIE Medical Imaging (2018) Grants : Principal Investigator for JSPS KAKENHI projects on lymphoma subtyping (2022-2025), 3D tumor modeling (2018-2021), and computational anatomy (2014-2019) He leads the Advanced Medical Physics and IT Research Center and contributes to academic societies as a committee member in organizations including IEICE and Japan Society of Medical Imaging and Information Sciences.

• Software Engineering
• Software Performance Engineering
• Geographic Information Systems (GIS)
• +4 more

Dr. Eng. Tomasz Babczyński is a researcher at Wrocław University of Science and Technology, affiliated with the Faculty of Computer Science and Telecommunications and the Department of Technical Computer Science. Software Engineering (specializing in Software Performance Engineering) Geographic Information Systems (GIS) software Image processing and pattern recognition (focusing on handwritten document analysis) His research spans interdisciplinary applications of computer science, including: Railway systems dependability analysis (ERTMS/ETCS protocols) Historical landscape modeling using GIS Multiagent system performance evaluation for power systems Advanced techniques for line segmentation in handwritten documents Recent publications (2020-2024) demonstrate expertise in tensor voting algorithms, data heterogeneity challenges, and safety-critical system analysis. His work combines theoretical computer science with practical applications in transportation, energy, and historical information systems. Contact: tomasz.babczynski@pwr.edu.pl

• Quantum Information and Computation
• Quantum Many-Body Systems
• Tensor Networks
• +7 more

Professor Norbert Schuch is a full Professor of Physics and Mathematics at the University of Vienna, where he leads the Research Group "Quantum Information and Quantum Many-Body Physics" at both the Faculty of Physics and Faculty of Mathematics. He joined the University of Vienna in October 2020 after serving as a tenured Research Group Leader at the Max-Planck-Institute of Quantum Optics in Garching, Germany and as a Lecturer at the Technical University Munich. Prior to that, he held a Tenure-Track-Professor position at the Institute for Quantum Information at RWTH Aachen University. Professor Schuch's research focuses at the intersection of Quantum Information and Computation with the Physics of Complex Quantum Many-Body Systems. His work combines mathematical, physical, and computational approaches to understand quantum correlations in many-body systems. Key research areas include tensor networks (such as Matrix Product States and Projected Entangled Pair States), topological order, entanglement theory, quantum algorithms, and quantum complexity theory. His interdisciplinary approach integrates methods from physics, mathematics, and theoretical computer science to address fundamental questions about quantum systems. His recent publications show a continued focus on tensor network theory and applications, with particular emphasis on topological phases, entanglement structure, quantum algorithms, and computational aspects of quantum many-body systems. His work spans mathematical foundations, physical applications, and computational implementations, demonstrating the cross-disciplinary nature of his research program. As an educator, Professor Schuch teaches courses on Quantum Information, Quantum Computing, and Quantum Algorithms, as well as specialized topics like Entanglement in Quantum Many-Body Systems. He actively supervises PhD students, postdocs, and master's students in his research group, which maintains strong connections with the international quantum information community.

Fabrice BaudoinView profile

Professor

• Stochastic analysis
• Differential geometry
• Dirichlet forms
• +6 more

Fabrice Baudoin is a Professor at the Department of Mathematics, Aarhus University . His research focuses on stochastic analysis, differential geometry, and Dirichlet forms, with applications to Lie groups, manifolds, and fractal spaces. Research Interests : Stochastic methods in geometric analysis Heat kernel theory and functional inequalities Sub-Riemannian geometry and curvature bounds Probability distributions for Brownian motion functionals Dirichlet forms on fractal spaces Publications Trends : Recent works address sub-Riemannian comparison theorems, hypoelliptic SPDEs, infinite-dimensional diffusions, and stochastic processes on fractals. His research combines geometric insights with probabilistic techniques. Teaching Activities : He has taught diverse undergraduate and graduate courses, with lecture notes available on his blog https://fabricebaudoin.blog .

• Sports Technology
• Sustainability Science
• Systems Modeling
• +3 more

Fredrik Ståhl serves as a Senior Lecturer at Mid Sweden University within the Faculty of Science, Technology and Media, specifically affiliated with the Department of Engineering, Mathematics and Subject Didactics. He holds a Doctor of Philosophy degree and maintains active research roles through the Sports Tech Research Centre, with physical presence in Östersund (Room Q281A). His educational foundation includes doctoral research completed at Umeå University, culminating in the 2000 dissertation At the edge of space and time - exploring the b-boundary in general relativity . This early work established his expertise in mathematical relativity before his research trajectory evolved toward applied interdisciplinary domains. Ståhl's research demonstrates a profound transition from theoretical physics to practical applications. Initial investigations centered on spacetime singularities and boundary constructions in general relativity, while recent work focuses on sports technology optimization and sustainability systems. His current projects—including Augmented Sports and the Mid Sweden Craft Brewing Lab —leverage mathematical modeling for athletic performance enhancement and regional sustainability frameworks, reflecting an integrative approach bridging engineering, environmental science, and human movement analysis. Analysis of his publication history reveals three distinct research phases: foundational work in mathematical relativity (1997-2002), transitional biomechanics studies (2008), and current sustainability/sports technology applications (2013-2015). This evolution showcases increasing interdisciplinary collaboration, particularly with sustainability researchers like Skytt and Nielsen, while maintaining rigorous mathematical methodology throughout his career. As an active member of the Sports Tech Research Centre, Ståhl contributes to projects examining human performance through computational modeling. His recent conference participations at the International Society for the Systems Sciences and Global Cleaner Production forums indicate strong engagement with global sustainability challenges, particularly through system modeling approaches for Anthropocene governance and long-term decision support tools.

Rafael de Andrade MoralView profile

Professor

• Statistical Modelling
• Ecology
• Wildlife Management
• +3 more

Rafael de Andrade Moral is a Professor of Statistics at Maynooth University's Faculty of Science & Engineering (since 2025), with prior roles as Associate Professor (2023-2025) and Assistant Professor (2018-2023). He holds a PhD in Statistics (University of São Paulo, 2014-2017) and dual bachelor's degrees in Biology and Education. His work bridges Statistical Ecology , Computational Biology , and Data Science , focusing on modeling ecological systems, agricultural pest dynamics, and biodiversity-ecosystem function relationships. Key research themes include Bayesian modeling , multivariate ecological forecasting , and machine learning applications . He founded the Theoretical and Statistical Ecology Research Group and serves on committees like the Young-ISA Chair . His recent articles span topics like insect abundance forecasting , weed-crop competition under climate change , and neuroinformatics-based learning analysis , reflecting interdisciplinary engagement. Scientific accolades include the Young Statistician Showcase Prize (2018), A-mu-sing Competition First Place (2021), and Maths Week Award (2022). He has advised three PhD students and contributed to over 50 peer-reviewed publications. Active in teaching innovation (e.g., Teaching Statistics through Music ), he also provides statistical consultancy to organizations like NIBIO and Jomakol .

• Neuroscience
• Machine Learning
• Control Theory
• +4 more

Dr. LIU Quanying is an Associate Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech), where she has been a faculty member since September 2019. She serves as the Principal Investigator of the Neural Computing and Control Laboratory (NCC lab) and is a doctoral supervisor. Prior to joining SUSTech, she earned her PhD in Biomedical Engineering from ETH Zurich and conducted postdoctoral research at Caltech. Education: PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Research Interests: Dr. Liu’s research integrates neuroscience, machine learning, and control theory. Her work focuses on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for neuroscience, and optimization techniques for neuromodulation (tES, TMS). She has developed high-density EEG source localization algorithms and data-driven brain network modeling frameworks, aiming to enhance precision in neural stimulation and control. Scientific Awards: The New Brain 30 (2023) AAIC Travel Award (2019) Estes Stars Award (2018) 深圳市孔雀人才计划C类 Laboratory and Team: As the PI of the NCC lab, Dr. Liu leads a team focused on machine learning algorithms, neurocomputational modeling, and neurofeedback control. The lab actively recruits graduate students, postdocs, and visiting researchers, emphasizing interdisciplinary collaboration in neuroscience and AI.

Alexander BarvinokView profile

Professor

• Computational Complexity
• Algorithms
• Algebra
• +6 more

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

Matthias HieberView profile

Professor

• Partial Differential Equations
• Fluid Dynamics
• Evolution Equations
• +3 more

Prof. Dr. Matthias Hieber is a Professor of Applied Analysis at the Department of Mathematics, Technical University of Darmstadt, and serves as Vice Director at the Mathematical Research Institute of Oberwolfach (MFO). His primary research focuses on partial differential equations, fluid dynamics, evolution equations, and harmonic analysis, with significant contributions to geophysical fluid models and liquid crystal theory. Hieber's research centers on the mathematical analysis of nonlinear PDEs arising in fluid mechanics, particularly the primitive equations for geophysical flows, Ericksen-Leslie equations for liquid crystals, and stochastic fluid models. His work establishes fundamental properties including well-posedness, regularity, and long-time behavior in diverse functional settings such as $L^p$ spaces and spaces of bounded functions. Key innovations include analyses of turbulent pressure effects, compressible liquid crystal dynamics, and hydrostatic Stokes operators. Recent publications reveal a concentrated focus on geophysical fluid dynamics and liquid crystal models, with increasing emphasis on stochastic formulations and non-isothermal effects. His work consistently bridges abstract harmonic analysis with concrete physical applications, demonstrating exceptional technical mastery in maximal regularity theory and operator calculus. Hieber's scientific recognition includes: Jean-Morlet Chair at CIRM, 2022 He leads the DFG Research Group FOR 5528 on Mathematical Investigations of Geophysical Fluid Models and has supervised Humboldt Fellows like Dr. Arnab Roy on fluid-structure interaction. His projects include the Geophysical Flows retreat in Manigod, France (2025), and the Leipzig Summer School (2024). At TU Darmstadt, Hieber heads the Applied Analysis research group, which organizes international workshops including the Oberwolfach conference on Mathematical Advances in Geophysical Fluid Dynamics (2022) and the CIRM conference on Nonlinear PDEs in Fluid Dynamics (2022).

• Computer Science
• Signal and Image Processing
• Remote Sensing
• +5 more

Christian Germain is a Professor of Computer Science at Bordeaux Sciences Agro, an engineering school specializing in agronomy. He focuses on information technologies and their applications to agriculture and environmental science, conducting research in image analysis at the IMS laboratory. His work spans remote sensing, embedded agricultural imaging, and digital tool development for vineyards. Key Roles: Co-holder of the AgroTIC business chair (29 corporate sponsors), Scientific Director of DigiLab (open platform for wine-growing experiments). Research Themes: Remote sensing, agricultural imaging systems, covariance pooling in machine learning, and texture analysis for material science. His recent publications highlight collaborations with industry and academic partners, emphasizing applications in vineyard health monitoring, carbon composite modeling, and vine disease detection. Germain’s team utilizes CNNs, Gaussian mixture models, and SAR imaging techniques to advance agricultural and materials engineering. He has contributed to international conferences and journals, integrating computational methods with real-world agricultural challenges, including proximal sensing for crop management and 3D microstructure simulation.