Find Academic Supervisors

• Commutative Algebra
• Algebraic Geometry
• Tensor Algebra
• +5 more

Alessandro Oneto is an Associate Professor at the Department of Mathematics, Università di Trento, Italy. His academic career includes postdoctoral positions at institutions like Inria Sophia Antipolis (France), Universitat Politècnica de Catalunya (Spain), and OVGU Magdeburg (Germany). He holds a PhD in Mathematics from Stockholm University (2016) and master's/bachelor's degrees from Università di Genova (Italy). Research interests focus on Commutative Algebra and Algebraic Geometry, particularly tensor decompositions and algebraic statistical models. He co-organizes events such as the TensorDec Lab and contributes to the INABAG network. Recent work includes studies on decomposition loci of tensors, Hadamard products of curves, and Segre-Veronese varieties' non-defectivity. Awards: Humboldt Fellowship, PRIN2022 grant, INdAM-GNSAGA membership. Labs/Teams: TensorDec Lab (since 2020), MultilinearVerse project, INABAG. Grants: Principal Investigator for PRIN2022 Applied Algebraic Geometry of Tensors.

Philipp BringmannView profile

Researcher

• Numerical solution of PDEs
• Least-squares finite element method
• Adaptive mesh refinement
• +4 more

Philipp Bringmann is a Researcher at the Institute of Analysis and Scientific Computing (E 101) at TU Wien, Austria. His work focuses on numerical methods for partial differential equations (PDEs), with specializations in adaptive finite element methods (FEM), least-squares formulations, and discontinuous Petrov-Galerkin techniques. He holds a PhD in Mathematics from Humboldt-Universität zu Berlin (2020) and has held postdoctoral positions at both TU Wien and Humboldt-Universität. Research Interests : Numerical solution of PDEs Least-squares finite element methods Adaptive mesh refinement Iterative linearization techniques Applications in computational mechanics Teaching : Current: Least-Squares Finite Element Methods (Winter 2024/25) Past: Numerical methods for PDEs (Winter 2023/24) Software Contributions : octAFEM3D: 3D adaptive finite element software MooAFEM: MATLAB-based adaptive FEM toolkit Recent Talks : Presented work on adaptive FEM with inexact solvers at the Chemnitz Finite Element Symposium 2024 .

Michael InnerbergerView profile

Researcher

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Michael Innerberger is a researcher at TU Wien's Faculty of Mathematics and Geoinformation , affiliated with the Research Group Numerics of PDEs . He holds a Dr.techn. (PhD) and Dipl.-Ing. in engineering. His work focuses on adaptive finite element methods (FEM) , numerical analysis , and multigrid solvers for partial differential equations (PDEs), with an emphasis on achieving optimal computational complexity and robust error estimation. Recent contributions include the development of hp-robust multigrid solvers and the MooAFEM MATLAB framework for higher-order adaptive FEM. His research addresses both symmetric and nonsymmetric PDEs, including semilinear and nonlinear cases. Education: Dipl.-Ing. (TU Wien), Dr.techn. (2022, TU Wien) Key Areas: Adaptive mesh refinement, goal-oriented error control, iterative linearization, and numerical software development. Publications highlight advancements in rate-optimal and cost-optimal adaptive algorithms for elliptic PDEs, demonstrating rigorous complexity analysis and practical implementation strategies. Collaborations include Dirk Praetorius, Roland Becker, and Jens Markus Melenk. Labs/Teams: Core member of the Numerics of PDEs research group, contributing to theoretical and computational advancements in scientific computing.

Xavier AntoineView profile

Professor

• Mathematical analysis of wave scattering problems
• Numerical analysis of PDEs
• Scientific computing
• +2 more

Xavier Antoine is a Full Professor in applied and computational mathematics at the Institut Elie Cartan de Lorraine (IECL) , Université de Lorraine, and a member of the Inria team SPHINX . He leads the associated team BEC2HPC focused on Bose-Einstein Condensates simulation. Research Focus : Mathematical and numerical analysis of wave scattering problems (Helmholtz, Maxwell, Klein-Gordon), quantum mechanics (Schrödinger/Gross-Pitaevskii), and fractional/nonlocal PDEs. Methodologies : Absorbing boundary conditions, perfectly matched layers, high-order approximation schemes, domain decomposition methods, and structure-preserving algorithms. His recent work includes advancements in fractional gradient flows for nonlinear Schrödinger equations and domain decomposition solvers for acoustic problems. He collaborates on multidisciplinary projects involving computational physics and engineering. Current Roles : Attaché pour la Science et la Technologie at the French Embassy in Beijing (since 2022), and former Head of IECL (2015-2020). He also served as a Scientific Advisor at the French National Agency for Research (ANR).

Pierre LissyView profile

Assistant Professor

• Controllability of Partial Differential Equations
• Control Theory
• Mathematical Analysis
• +3 more

Pierre Lissy is a Junior Professor at the CERMICS Laboratory, École des Ponts ParisTech, since September 2023. His research focuses on the controllability of linear and nonlinear systems of partial differential equations (PDEs), particularly heat, wave, and Schrödinger equations, with applications in mathematical analysis and numerical methods. His research spans control theory, mathematical analysis, and numerical approximation. Recent work includes studies on null controllability of parabolic equations, traffic flow reconstruction, and stabilization of degenerate PDEs. His publications emphasize theoretical grounding and computational approaches, with keywords like Control Theory Partial Differential Equations Numerical Analysis Mathematical Physics Stochastic Processes He supervises students such as Nail Baloul, Lucas Davron, Roman Vanlaere, Jingrui Niu, and Clément Moreau. He teaches courses in functional analysis, partial differential equations, and numerical methods for evolution equations.

Leonard BerradaView profile

Researcher

• Optimization
• Deep Learning
• Verification
• +5 more

Leonard Berrada is a Research Scientist at DeepMind, focusing on robust and verified AI. He previously completed his DPhil/PhD at the University of Oxford under Andrew Zisserman and M. Pawan Kumar. Education: University of Oxford (DPhil/PhD, supervised by Andrew Zisserman and M. Pawan Kumar) Research Interests: His work spans optimization, deep learning, verification, and privacy-preserving machine learning. Recent publications highlight expertise in federated learning, biomedical image analysis, inverse problems, and tumor growth modeling. Publication Trends: His recent work (2022–2025) focuses on federated learning, biomedical image analysis, and inverse problems in personalized tumor modeling. Earlier contributions (2020–2018) address neural network training, verification, and loss function design. Keywords include Computer Science , Optimization , and Biomedical Engineering . Scientific Awards: Outstanding Reviewer Award at CVPR 2018 Top 10% Reviewer at NeurIPS 2020 Notable Contributions: Funded by Yougov and EPSRC during his PhD, he pioneered methods in neural network verification and federated learning. His thesis was accepted without correction by Andrea Vedaldi and Julien Mairal (2020). He also contributed to Gilbert Strang's book on linear algebra and learning from data.

David BermanView profile

Professor

• M-theory
• String Theory
• Non-commutative Geometry
• +3 more

Professor David Berman of Queen Mary University of London is a leading researcher in M-theory , String Theory , and Quantum Gravity . Affiliated with the School of Physical and Chemical Sciences and the Centre for Fundamental Physics , his work bridges Theoretical Physics and Machine Learning through interdisciplinary research. Education: University of Manchester, Durham University (PhD), Cambridge (Advanced Research Fellow) His research focuses on branes , non-commutative geometry , and duality symmetries , including groundbreaking work on exceptional geometry in M-theory. Recent projects explore the intersection of machine learning and physics-inspired algorithms . Analysis of his 15 most recent publications reveals trends in AI interpretability , quantum geometry , and exceptional field theory . Key collaborations include institutions like CERN , Harvard , and Perimeter Institute . Grants include major funding from the Science & Technology Research Council and EU Horizon 2020 for projects like Amplitudes, Strings and Duality and X-SUGRA . Public engagement highlights: BBC Radio 4 appearances, Hay Festival talks, and artistic collaborations with Turner Prize winners. He serves as Director of Outreach for the School of Physics and Astronomy. Active in DERI (Digital Environment Research Institute) and Centre for Fundamental Physics , he leads teams in exploring quantum gravity , duality manifest formalisms , and AI applications in theoretical physics.

• Combinatorics
• Random Graphs
• Discrete Mathematics
• +5 more

Juan José Rue Perna is an Associate Professor in the Department of Mathematics at Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Mathematics and Statistics (FME) and the Institute of Mathematics of UPC-BarcelonaTech. He also serves as Director of CFIS (Centre de Formació Interdisciplinària Superior) and maintains strong connections with the Centre de Recerca Matemàtica (CRM). His research focuses on combinatorial mathematics, particularly in the areas of random graphs, discrete mathematics, enumerative combinatorics, and additive combinatorics. Rue Perna has made significant contributions to the understanding of planar maps, extremal combinatorics, and probabilistic methods in discrete structures. His work often bridges theoretical mathematics with practical applications in computer science and network analysis. Rue Perna's recent publications demonstrate consistent research output across multiple high-impact journals in combinatorics and discrete mathematics. His work spans theoretical investigations of graph structures, enumeration problems, and applications to number theory. The research trends show a strong emphasis on probabilistic methods, asymptotic analysis, and connections between algebraic structures and combinatorial phenomena. Premi Albert Dou de la SCM Rue Perna has successfully advised multiple PhD students to completion, including Clément Requilé, Christoph Spiegel, Vasiliki Velona, and Maximilian Wötzel. His research has been supported by numerous competitive grants including projects from the Spanish Ministry of Science, European Research Council (ERC), and the Berlin Mathematical School. He has organized several significant academic events including EUROCOMB 2021 in Barcelona and various workshops on combinatorics and discrete mathematics. He is an active member of the GAPCOMB research group (Geometric, Algebraic and Probabilistic Combinatorics) at UPC, collaborating extensively with researchers across Europe. Rue Perna also contributes to the mathematical community through editorial work for journals including the Journal of Number Theory, European Journal of Combinatorics, and Integers: electronic journal of combinatorial number theory.

Xavier ClaeysView profile

Professor

• Wave Propagation
• Mathematical Modeling
• Numerical Analysis
• +5 more

Xavier Claeys is a Professor at ENSTA Paris, affiliated with the Applied Mathematics Unit (UMA) and the Wave Propagation, Mathematical Study and Simulation (POEMS) research team. His work focuses on the mathematical modeling and numerical analysis of wave propagation phenomena with applications in electromagnetics and acoustics. He maintains an active research program with numerous publications spanning from 2007 to the present, demonstrating sustained scholarly contributions to his field. Dr. Claeys earned his PhD from Université Versailles Saint-Quentin-en-Yvelines in 2008 with a thesis on 'Asymptotics and numerical analysis for wave diffraction by thin wires.' He completed his Habilitation from Université Pierre-et-Marie Curie in 2016 with research on 'Boundary integral equations of time harmonic wave scattering at complex structures.' His academic trajectory shows a consistent focus on developing mathematical frameworks for wave propagation problems. His primary research interests center on the modeling and numerical analysis of linear wave propagation phenomena, particularly in the context of frequency domain electromagnetics and acoustics. His specific technical interests include domain decomposition methods, boundary element methods, scientific computing, multi-scale problems, and singularities in elliptic problems. Dr. Claeys has developed specialized software tools including bemtool (a boundary element library) and ddmtool (a domain decomposition library) to support his research, demonstrating his commitment to translating theoretical advances into practical computational tools. Analysis of his publication record reveals a consistent evolution toward increasingly sophisticated domain decomposition approaches, particularly optimized Schwarz methods and multi-trace boundary integral formulations. His work demonstrates a strong theoretical foundation combined with practical implementation considerations, with applications spanning electromagnetic scattering, acoustic wave propagation, and metamaterial analysis. Recent publications show increasing focus on addressing computational challenges related to complex geometries and physical boundaries. Dr. Claeys has led significant research projects including 'NonlocalDD' funded by the French National Research Agency (ANR), which investigated boundary integral equations in conjunction with domain decomposition. He was previously involved in the ANR-funded 'METAMATH' project focused on the modeling and numerical analysis of metamaterials. His research contributes to ENSTA Paris's broader research focus on cutting-edge areas with applications in sustainable energy, transportation, and defense. His work aligns particularly with the 'Waves and Vibrations' research axis at ENSTA Paris, which encompasses activities related to wave propagation phenomena with applications including seismic effects on infrastructure, non-destructive testing, and acoustic management in transportation and energy sectors. Dr. Claeys' theoretical contributions provide foundational support for these applied research directions through advanced mathematical modeling and numerical methods development.

Song HanView profile

Associate Professor

• Efficient Deep Learning
• Neural Network Compression
• Hardware-Aware AI
• +7 more

Song Han is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). His research focuses on efficient deep learning computing, bridging algorithm and hardware design to enable scalable AI systems. PhD in Electrical Engineering from Stanford University Research Interests Efficient Deep Learning Neural Network Compression Hardware-Aware Transformers Sparse Attention Mechanisms Quantization Techniques Edge and IoT Computing Recent Publication Trends highlight advances in LLM optimization, diffusion model quantization, and quantum-classical co-design. His work emphasizes reducing computational costs while maintaining model fidelity. Scientific Awards Best Paper, ICLR and FPGA Symposium NSF CAREER Award MIT Technology Review 35 Innovators Under 35 Collaborations include the MIT-IBM Watson AI Lab, focusing on AI hardware and system co-design. Many of his techniques are integrated into commercial AI chips.

Michael GentnerView profile

Researcher

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +2 more

Dr. Michael Gentner is a researcher at the Institute of Optimization and Operations Research, Ulm University, where he teaches courses including Angewandte Diskrete Mathematik (Combinatorics) and Advanced Discrete Algorithms. His institutional contact details include office Helmholtzstr. 18, Room 1.47, with Thursday office hours (14:00-16:00) and phone number +49 731 50-23637. His primary research focuses on structural graph theory and combinatorics, with expertise in domination theory, independence number, zero forcing sets, and extremal graph problems. Gentner investigates properties of graphs through degree sequences, clustering coefficients, and dynamic monopolies, particularly in specialized graph classes like forests and graphs without short cycles. Analysis of his 2015-2018 publications reveals consistent emphasis on establishing bounds and extremal values for graph invariants. His work bridges theoretical combinatorics with applications in network science, frequently collaborating with Dieter Rautenbach and other researchers in the field. Scientific Awards No scientific awards or honors were mentioned in the provided information. Advising and Grants No details regarding student supervision, PhD advisees, or research grants were found in the source material. Labs and Teams Gentner operates within Ulm University's Institute of Optimization and Operations Research, though specific laboratory structures or research team compositions are not detailed in the available text.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

Stephan MescherView profile

Researcher

• Algebraic Topology
• Differential Geometry
• Robot Motion Planning
• +5 more

Stephan Mescher is a Researcher at the Institute of Mathematics within the Faculty of Natural Sciences II at Martin Luther University Halle-Wittenberg. Based at the Georg Cantor House on Theodor-Lieser-Strasse in Halle (Saale), he conducts advanced research at the intersection of algebraic topology and differential geometry with applications in robotics. His research focuses on: Topological complexity and spherical complexities in motion planning Geodesic analysis in Riemannian and Finsler manifolds Morse theory and A-infinity algebra structures Closed geodesics and critical point theory Geometric properties of configuration spaces for robots Recent publications reveal a cohesive trajectory toward sequential topological complexity and fibered decompositions of cut loci, demonstrating how abstract topological invariants solve concrete problems in robot navigation. His work bridges pure mathematics with engineering applications through rigorous analysis of manifold structures. No scientific awards were documented in available sources. Information regarding student supervision, research grants, or collaborative teams was not provided in the source materials.

Kevin CarlbergView profile

Associate Professor

• Contextual AI
• Computational Physics
• Computational Mathematics
• +3 more

Kevin Carlberg is an Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering at the University of Washington. He currently holds leadership roles in AI Research Science at Meta Reality Labs and has prior experience at Sandia National Laboratories. His research focuses on contextual AI, computational physics, and model reduction techniques. He holds a PhD in Aeronautics and Astronautics from Stanford University (2011), an MS from Stanford (2006), and a BS from Washington University in St. Louis (2005). Carlberg leads interdisciplinary teams to develop AI-driven methodologies for extreme-scale simulations and wearable technologies. His work emphasizes reducing computational complexity for high-consequence applications, such as national security and VR/MR devices. Key areas include nonlinear model reduction, uncertainty quantification, and data-driven time-parallelism. He has pioneered techniques like the network uncertainty quantification (NetUQ) method and deep neural network-based error modeling. His research has been published in top journals like Computer Methods in Applied Mechanics and Engineering and Journal of Computational Physics , focusing on ROMs, machine learning integration, and high-performance computing. His contributions address challenges in Kolmogorov widths and real-time simulation requirements for large-scale systems.

• Database Management
• Data Privacy
• Data Science
• +4 more

Dr. Tianbing Xia is a Lecturer at the School of Computing and Information Technology, University of Wollongong (UOW), where he has been employed since 2021. He holds a PhD in Computer Science from UOW (1997–2000) and has over two decades of experience in tertiary education, research, and the IT industry. Prior to his PhD, he worked in the IT department of China Construction Bank HUBEI Province, contributing to software networking systems that earned a national-level award. His teaching spans a wide range of undergraduate and postgraduate courses, including CSIT115, CSCI 882, and others. Research interests include database management, data privacy, combinatorial code design, and computer security. He has actively supervised four Higher Degree Research students, focusing on topics like data processing optimization and cybersecurity. Dr. Xia’s research contributions span over 50 publications, with notable works in query optimization, parallel data transfer scheduling, and combinatorial matrix constructions. He has received funding for projects like Real-Time Visualisation of Intrusion Detection and Fuzzy-based Cryptography Applications . Notable recognition includes the national Technology and Advanced Development Grand Prize for a software networking system. He is currently available to supervise Masters and PhD students in data science and cybersecurity domains.