Find Academic Supervisors

• Functional Analysis (applied)
• Operator/Spectral Theory
• Compressed Sensing
• +12 more

Professor Anders Christian Hansen is a leading mathematician at the University of Cambridge and University of Oslo , specializing in Functional Analysis , Artificial Intelligence , and Compressed Sensing . As head of the Applied Functional and Harmonic Analysis group and a Royal Society University Research Fellow , his work bridges foundational mathematics with cutting-edge data science. Current affiliations: University of Cambridge (since 2013), University of Oslo (Professor II since 2012) Previous roles: Von Karman Instructor (Caltech), Marie Curie Fellow (Vienna), Junior Research Fellow (Cambridge) Research Interests : Functional Analysis (applied), Operator/Spectral Theory, Compressed Sensing Artificial Intelligence, Computational Barriers, Foundations of Mathematics Medical Imaging, Inverse Problems, Numerical Analysis Article Trends : His recent work explores the limits of AI through the Solvability Complexity Index (SCI), revealing inherent instabilities in deep learning despite theoretical guarantees. Collaborations with B. Adcock and M. Colbrook address structured sampling and numerical algorithms for spectral problems in infinite dimensions. Scientific Awards : PROSE Award Finalist (2022) for Compressive Imaging Whitehead Prize (2019), IMA Prize (2018) Leverhulme Prize (2017) for contributions to applied analysis Advising & Grants : Supervised 17 PhD students and 8 postdocs. Active in grants related to medical imaging , AI safety , and computational mathematics .

Ander BiguriView profile

Researcher

• Tomographic Reconstruction
• Medical Imaging
• Machine Learning
• +4 more

Dr. Ander Biguri is a Researcher at the Cambridge Image Analysis (CIA) group within the Faculty of Mathematics at the University of Cambridge. His primary research focuses on tomographic reconstruction techniques, particularly in cone-beam computed tomography (CBCT), and the integration of machine learning into medical imaging workflows. He is the main developer and maintainer of the TIGRE toolbox, an open-source software for iterative CT reconstruction. Key research areas include source-detector trajectory optimization for improved imaging quality, low-dose CT reconstruction, metal artifact reduction, and dynamic PET imaging. His work emphasizes practical applications in clinical and industrial tomography, with contributions to both algorithm development and hardware optimization. Biguri collaborates on projects such as AdvanCT (EURAMET) and has published extensively on topics like motion correction in PET/CT, GPU-based iterative reconstruction, and the design of imaging systems for radiation therapy.

Gonzalo MateosView profile

Associate Professor

• Statistical Signal Processing
• Machine Learning
• Network Science
• +4 more

Gonzalo Mateos is an Associate Professor in the Department of Electrical and Computer Engineering and holds a secondary appointment in the Department of Computer Science at the University of Rochester. He is also the Asaro Biggar Family Fellow in Data Science and a member of the Goergen Institute for Data Science and Artificial Intelligence. Additionally, he serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Signal and Information Processing over Networks , and is part of the IEEE SigPort Editorial Board. Dr. Mateos earned his B.Sc. in Electrical Engineering from Universidad de la Republica, Uruguay in 2005. He then pursued graduate studies at the University of Minnesota, Twin Cities, where he received his M.Sc. in 2009 and Ph.D. in 2011. Prior to joining the University of Rochester in 2014, he worked as a Systems Engineer at Asea Brown Boveri (ABB) in Uruguay from 2004 to 2006 and served as a visiting scholar at Carnegie Mellon University's Computer Science Department during the 2013 academic year. His research interests focus on statistical learning from Big Data , network science , decentralized optimization , and graph signal processing , with applications in dynamic network health monitoring , social networks , power grid analysis , and large-scale data analytics . His work emphasizes blind deconvolution, graph topology inference, fairness-aware methodologies, and explainable AI for medical and engineering systems. Dr. Mateos has been honored with the NSF CAREER Award (2018), the IEEE Young Author Best Paper Award (2017), and multiple conference best paper awards including ICASSP 2018, SSP Workshop 2016, and SPAWC 2012. His doctoral research was recognized with the University of Minnesota's Best Dissertation Award (2013, Honorable Mention) in Physical Sciences and Engineering. In advising, he mentors graduate students in his research areas, though specific names are not listed here. His grants include the NSF CAREER Award, and he collaborates on projects like fairness-aware graph filter design and dynamic network monitoring. His research teams and labs operate within the Goergen Institute, focusing on interdisciplinary data science and AI applications. He actively contributes to the Hajim School of Engineering and the broader University of Rochester community, fostering innovation at the intersection of electrical engineering and computer science. His industry experience and academic roles reflect a commitment to both theoretical and practical advancements in networked systems and machine learning.

• Quantum Entanglement
• Photonics
• Quantum Information Science
• +3 more

Natalia Herrera Valencia serves as a Research Associate at the Institute of Photonics and Quantum Sciences within Heriot-Watt University's School of Engineering & Physical Sciences. Her work centers on experimental quantum photonics, specifically manipulating high-dimensional quantum states for advanced information processing. Her research spans quantum entanglement engineering , photonic quantum computing , and noise-resilient quantum communication . Key specialties include structured light control, optical fiber networks for entanglement distribution, and tomographic reconstruction of complex quantum states. She investigates fundamental limits of quantum systems under losses and decoherence. Recent publications (2024-2025) reveal a concentrated effort in scalable quantum photonic architectures , with emphasis on multiplexed networks, inverse-designed optical circuits, and parallelized Bell tests. These works collectively advance high-dimensional quantum information protocols using integrated photonics platforms. Dr. Herrera Valencia actively collaborates with the quantum photonics group at Heriot-Watt, particularly within Mehul Malik's research team. Her experimental work leverages the university's state-of-the-art quantum optics facilities for generating and characterizing entangled photon states in complex media.

Davide RattacasoView profile

Research Fellow

• Quantum Information
• Quantum Many-Body Systems
• Adiabatic Quantum Computation
• +3 more

Davide Rattacaso is a Research Fellow at the University of Padua's Department of Physics and Astronomy, funded by the European Project EuRyQa. His research focuses on quantum information, quantum many-body systems, and developing hardware-aware quantum compilers using tensor network techniques. Current projects include adiabatic quantum computation, inverse problems in quantum systems, resource theory, and information geometry. His work integrates advanced computational methods with theoretical frameworks to address challenges in quantum hardware optimization and quantum dynamics. Key themes across his research include understanding non-equilibrium quantum phenomena and leveraging geometric approaches to analyze quantum complexity and chaos. His contributions span foundational studies on Hamiltonian reconstruction, stabilizer entropy dynamics, and delocalized quantum state evolutions. The research is supported by grants emphasizing practical applications of quantum algorithms and architectures.

Marek BehrView profile

Professor

• Computational Mechanics
• Fluid Dynamics
• Biomedical Engineering
• +3 more

Marek Behr is a Professor (C4/W3) and Chairholder at the Chair for Computational Analysis of Technical Systems within the Faculty of Mechanical Engineering at RWTH Aachen University since 2004. He also holds an Adjunct Professorship at Rice University’s Department of Chemical and Biomolecular Engineering since 2005. His academic leadership roles include serving as Founding Director of the Center for Simulation and Data Science (CSD) since 2018, President of the German Association for Computational Mechanics (GACM) since 2021, and Executive Council Member of the International Association for Computational Mechanics (IACM) since 2020. He coordinates the International Research Training Group 2379 Modern Inverse Problems with the University of Texas at Austin. Behr’s research focuses on computational fluid dynamics (CFD) , biomedical engineering , and finite element methods , with applications in cardiovascular modeling, multiphysics systems, and manufacturing processes. His work bridges engineering and medicine, addressing challenges like in-stent restenosis, hemolysis prediction, and advanced material processing. He has an h-index of 45 (Google Scholar) and over 3,600 citations. Teaching responsibilities include courses such as Simulation Methods in Mechanical Engineering , Parallel Computing in Computational Mechanics , and Finite Elements in Fluids . His research group actively collaborates with institutions like the Jülich-Aachen Research Alliance (JARA) and the Oden Institute . Recent publications emphasize computational models for cardiovascular devices, machine learning in structural health monitoring, and advanced manufacturing simulations, reflecting his interdisciplinary approach to solving complex engineering problems.

• Physical Simulations
• Geometry Processing
• Fluid Dynamics
• +7 more

Dr. Marcel Padilla is a Postdoctoral Researcher at the Interactive Geometry Lab within the Department of Computer Science at ETH Zürich, supported by the Feodor-Lynen Fellowship from the Alexander von Humboldt Foundation. Previously, he completed his PhD at TU Berlin under Professors Ulrich Pinkall and Peter Schröder, focusing on solar corona modeling and geometry processing. Education: PhD in Computer Science, TU Berlin (2023) MSc in Mathematics, TU Berlin (2018) BSc in Mathematics, TU Berlin (2016) Research Interests: Marcel’s work spans physical simulations, geometry processing, fluid dynamics, and plasma modeling. He explores applications in computer graphics, such as modeling solar corona dynamics and vortex filament behavior. His methods often leverage discrete exterior calculus and variational principles to achieve computational efficiency and accuracy. Key Contributions: Recent work includes Exact 3D Green’s Function Integrations on Triangles (2025), Going with the Flow (2024), and Filament Based Plasma (2022). These studies address challenges in fluid-structure interaction, plasma visualization, and efficient numerical integration. Awards: Feodor-Lynen Fellowship (Alexander von Humboldt Foundation) Teaching & Outreach: Marcel has taught courses on Discrete Differential Geometry at ETH Zürich and Geometry Processing at TU Berlin. He also develops practical resources for Houdini programming and scientific presentation design. Labs & Collaborations: His research is supported by institutions like the DFG Collaborative Research Center TRR 109 and the Einstein Foundation Berlin. He actively contributes to open-source projects, including implementations of his publications on GitHub.

• Fuzzy systems
• Computational mathematics
• Numerical optimization
• +5 more

Professor Gleb Beliakov is a faculty member at Deakin University's School of Information Technology within the Faculty of Science Engineering and Built Environment. He has held roles including Associate Head of School (Research) from 2009–2012 and currently leads the Computational Intelligence and Data Analytics Research cluster and the Data to Intelligence research centre. His expertise spans fuzzy systems, computational mathematics, numerical optimization, and high-performance computation. Education: PhD and MSc from the Russian Peoples Friendship University. Research Interests: Focuses on fuzzy systems, aggregation functions, parallel programming, and their applications in decision-making and data analysis. Grants: Includes projects on Trusted Autonomy (CRC), Satellite Communication Systems (Defence), and Explainable AI (ARC). His research emphasizes fuzzy measures, Choquet integrals, and their use in addressing complex problems like resource optimization and multimedia piracy. He has supervised numerous PhD and master's students, contributing to advancements in preference mining, image processing, and watermarking techniques.

• Seismic Data Processing
• Signal Processing
• Microseismic Monitoring
• +2 more

Jubran Akram is an Adjunct Assistant Professor in the Department of Earth, Energy, and Environment within the Faculty of Science at the University of Calgary. His research focuses on advanced geophysical signal processing techniques. Primary research areas include: Microseismic data processing and event detection Wavefield separation and noise suppression methods Machine learning applications in seismic analysis Velocity modeling and hypocenter localization His publications (2018-2024) demonstrate consistent focus on developing computational methods for seismic data enhancement, including machine learning approaches for phase picking, noise reduction, velocity calibration, and event location.

Weiran SunView profile

Professor

• Applied Mathematics
• Partial Differential Equations (PDE)
• Kinetic Theory
• +4 more

Weiran Sun is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on partial differential equations (PDEs), kinetic theory, and applied analysis, with recent interests expanding into AI applications in mathematics. He holds a Ph.D. in Applied Mathematics and Scientific Computation from the University of Maryland (2008). Research interests include: Analysis and numerical methods for kinetic equations Inverse problems related to kinetic theory Mathematical collaboration with computer scientists (e.g., DeLTA Lab at SFU for LLMs in applied math) Recent work highlights: Development of PDE-Controller using LLMs for formalization/reasoning of PDEs Studies on crossover transport phenomena and diffusion limits Contributions to numerical analysis of nonlocal equations (e.g., Lévy-Fokker-Planck) Upcoming activities include organizing the 5th SIAM Pacific Northwest Section Meeting (2025) and participating in kinetic theory programs at SLMath (Berkeley). His research spans interdisciplinary collaborations, blending traditional applied mathematics with emerging AI tools.

• Approximation Theory
• Mathematical Physics
• Radial Basis Functions
• +5 more

Francis J. Narcowich is a Professor of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from Princeton University (1972) and has been a faculty member since his appointment. His research focuses on approximation theory, mathematical physics, and numerical methods, with notable contributions to radial basis functions, wavelets, and spherical approximation. Education: Ph.D. in Mathematics, Princeton University, 1972 M.A. in Mathematics, Princeton University, 1970 B.S. in Mathematics, DePaul University, 1968 Teaching: Current courses (Spring 2025): Math 414 (Fourier Series and Wavelets), Math 601 (Methods of Applied Mathematics) Past courses include Analysis for Applications, Complex Variables, and Special Functions Research Interests: Dr. Narcowich's work bridges approximation theory and applied mathematics, emphasizing kernel-based methods, scattered data approximation, and numerical solutions of PDEs on spheres. His contributions include advancements in radial basis functions, localized bases, and spectral theory for operators. Publications: Over 100 peer-reviewed articles, including seminal works on spherical interpolation, meshless methods, and inverse theorems in approximation theory. Notable books include *A First Course in Wavelets and Fourier Analysis* (co-authored with Albert Boggess). Grants and Service: Extensive funding history in applied mathematics and computational science. Active in academic service, including curriculum development and graduate student mentorship.

Matt SuttonView profile

Lecturer

• Bayesian Methodology
• Monte Carlo Methods
• High-Dimensional Statistics
• +3 more

Dr. Matt Sutton is a Lecturer in Statistical Inference for Complex Models at the School of Mathematical Sciences. He earned his PhD in 2019, focusing on developing statistical methods for high-dimensional data in clinical health and biological contexts. Previously, he worked as a postdoc at Lancaster University under the Bayes4Health grant. His research emphasizes Monte Carlo methods, Bayesian methodology, and high-dimensional statistics, with a current focus on continuous-time Monte Carlo techniques to accelerate Bayesian inference. Sutton actively contributes to the Models and Algorithms research program at the Centre for Data Science. His research interests include computational statistics, specifically advancements in PDMP samplers, control variates, and scalable Bayesian methods. Notable work spans applications in genomics, geophysics, and healthcare data analysis. Sutton’s methodologies aim to enhance computational efficiency in complex statistical inference tasks. His articles reflect a trend toward optimizing sampling algorithms and addressing challenges in high-dimensional Bayesian problems. Key areas include PDMP-based sampling, debiasing techniques, and federated learning applications. Sutton has not yet reported formal scientific awards or listed advisees in the provided text. His involvement in collaborative initiatives like the Centre for Data Science underscores his commitment to interdisciplinary research.

Alen AlexanderianView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +3 more

Alen Alexanderian is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), serving since 2022. Previously, he was an Assistant Professor at NC State (2015-2022), a Research Associate and Postdoctoral Fellow at The University of Texas at Austin’s ICES (2012-2015), and a Postdoctoral Fellow at Johns Hopkins University (2010-2012). He earned his PhD in Applied Mathematics from the University of Maryland, Baltimore County (2010), with prior degrees including an MS (2007) and BA in Mathematics (2005) from Hood College. His research focuses on numerical analysis, scientific computing, and uncertainty quantification. Key areas include numerical methods for inverse problems, optimization under uncertainty, Bayesian inversion, and sensitivity analysis. He develops algorithms for optimal experimental design and applies these to large-scale inverse problems in engineering and science. His recent work emphasizes hyper-differential sensitivity analysis, robust optimal design under model uncertainty, and applications in PDE-constrained optimization. His studies often bridge theoretical developments with practical implementations in fields like porous media flow, biotransport, and epidemiological models. Notable contributions include frameworks for Bayesian optimal sensor placement, variance-based sensitivity analysis techniques, and scalable computational methods for high-dimensional inverse problems. His research integrates mathematical rigor with computational efficiency, addressing challenges in data assimilation, model reduction, and risk-aware decision-making.

Alessio AlexiadisView profile

Associate Professor

• Physics-Informed Machine Learning
• Particle Methods (MD, DEM, SPH)
• Computational Fluid Dynamics
• +7 more

Dr. Alessio Alexiadis is a Reader in Chemical Engineering at the University of Birmingham, specializing in mathematical modeling and physics-informed machine learning. He holds a PhD from Politecnico di Torino (2001) and has held academic positions across 11 institutions in 8 countries. His research focuses on particle methods (MD/DEM/SPH), multiphase flows, and interdisciplinary applications in materials science, biomedical engineering, and environmental modeling. Education: PhD in Chemical Engineering, Politecnico di Torino, 2001 BSc in Chemical Engineering, 1998 Research Interests: Development of Discrete Multiphysics frameworks for complex systems AI-driven simulations and physics-informed machine learning Modeling fluid-structure interactions, colloids, and biophysical systems Applications in energy storage, climate modeling, and biomedical devices Grants & Funding: EPSRC CDT in Formulation Engineering (PI, £multi-million) Leverhulme Trust (PI, 2019–2023) US Office of Naval Research (PI, 2018–2022) Awards & Editorial Roles: Marie Curie Fellowship (2008) Editorial Board member: PLOS ONE, Scientific Reports (Nature), and ChemEngineering Labs & Collaborations: Modelling Concepts and Tools (MCT) group at Birmingham International collaborations spanning 4 continents

• Geological Modeling
• 3D Geophysical Modeling
• Structural Geology
• +3 more

Lachlan Grose is a Research Fellow at Monash University's School of Earth Atmosphere and Environment, specializing in advanced geological and geophysical modeling. His work focuses on integrating machine learning, 3D structural analysis, and geophysical inversion techniques to address challenges in subsurface resource management and tectonic understanding. Key projects include the Three-dimensional Bayesian Modelling of Geological and Geophysical data (ARC-funded) and development of the LoopStructural software, a time-aware 3D geological modeling platform. Grose also contributes to industry collaborations like the AMIRA P1249 project on ore body knowledge modeling. Research interests span structural geology, igneous intrusion modeling, FAIR data principles, and geophysical inversion methods. He has led efforts to improve usability of Loop3D and pioneered machine learning applications for geological structure imaging. Leading developer of LoopStructural (open-source 3D modeling framework) Primary investigator on usability improvements for geoscientific workflows Recipient of ARC and industry grants totaling over AUD 3M Supervises PhD candidates focusing on interdisciplinary geoscience problems Recent publications emphasize transdimensional inversion methods, FAIR data integration, and application of machine learning to geophysical datasets. His work bridges academic research with industrial subsurface resource challenges.