Find Academic Supervisors

• Theoretical Physics
• Condensed Matter Physics
• Quantum Many-Body Systems
• +4 more

Becca Federico is an Associate Professor at the Department of Physics, University of Trieste, holding significant leadership positions including Deputy Head of Department, Deputy Head of Head's Office, Deputy Head of Executive Board, and Deputy Head of Management Committee. He serves as a member of the Department's Board, Boards of Studies for Physics programs (SM20 and SM23), and multiple Doctoral Studies Boards (PHD04 cycles XXXV through XXXIX). Dr. Federico's research focuses on Theoretical Physics of Matter, Models, Mathematical Methods and Applications , with particular expertise in strongly-correlated models on the lattice within Condensed Matter Physics. His work spans quantum many-body systems, topological phases of matter, and frustrated magnetic systems, employing advanced computational techniques to understand complex quantum phenomena. Analysis of his recent publications reveals a strong emphasis on cutting-edge approaches to quantum systems, particularly the integration of machine learning techniques (neural networks, transformers) with traditional quantum many-body methods. His work explores the intersection of topological phases, quantum spin liquids, and strongly correlated electron systems, with significant contributions to understanding Hubbard models, frustrated magnets, and topological insulators. As a member of research groups focused on strongly-correlated models on the lattice, Dr. Federico contributes to advancing computational methods for quantum systems while maintaining strong connections with the broader physics community through his leadership roles and participation in doctoral education.

Gerlind PlonkaView profile

Professor

• Numerical Fourier Analysis
• Wavelet Theory
• Regularization Methods
• +3 more

Gerlind Plonka is a Professor of Applied Mathematics at the University of Göttingen, specifically within the Institute for Numerical and Applied Mathematics (NAM). Her research focuses on Numerical Fourier Analysis Wavelet Theory Regularization and Nonlinear Diffusion Methods Fast Algorithms and Numerical Stability Signal and Image Processing Applications Her recent publications emphasize structured subsampling in Fourier domains, Prony-type methods for exponential sum recovery, and deep learning integration in medical imaging. She supervises active PhD candidates including Benjamin Kocurov, Anahita Riahi, Yannick Nicola Riebe, and Janina Schmidt, with a legacy of advising over 50 graduates across diverse topics like Sparse FFT Algorithms Phase Retrieval Constraints Wavelet-Based Image Compression Nonlinear Diffusion Filters High-Dimensional Data Approximation

Gert LubeView profile

Professor

• Numerical Methods for Partial Differential Equations
• Computational Fluid Dynamics
• Turbulence Modeling
• +2 more

Prof. Dr. Gert Lube is a faculty member at the Institute for Numerical and Applied Mathematics (NAM) within the Faculty of Mathematics and Computer Science at Georg-August-University Göttingen. His research focuses on numerical methods for partial differential equations , with emphasis on stabilized finite element methods , turbulence modeling , and magnetohydrodynamics (MHD) . Workshops Organized : Calibration of Viscosity Models for Turbulent Flows (2010), Variational Multiscale Methods (2008), Local Projection Stabilization (2008), BAIL Conferences. His academic contributions include 15+ publications since 2010 on topics like Navier-Stokes simulations , LES/VMS methods , stabilized FEM , and FEM-BEM coupling . Collaborations span institutions like TU Graz, Saarbruecken University, and DLR Göttingen. Key Research Areas : Finite Element Methods, Turbulence Modeling, MHD, Incompressible Flows, Singularly Perturbed Problems, Parallel Computing. Advisees include PhD candidates working on topics such as non-isothermal flows , mass conservation , hybrid RANS/LES , and domain decomposition .

Scot AdamsView profile

Professor

• Dynamical systems
• foliations
• ergodic theory
• +5 more

Scot Adams is a Professor in the School of Mathematics at the University of Minnesota, with research spanning pure mathematics and machine learning applications. His primary academic affiliation is with the Mathematics Department at the University of Minnesota, where he maintains an active research program while managing phased retirement paperwork. His research interests include dynamical systems, foliations, ergodic theory, hyperbolic groups, trees, Riemannian geometry, and more recently neural networks and thermodynamics. In his diary entries through August 2025, he documents ongoing work developing neural network slides, focusing on visual chain rule explanations, diagram of functions, and coordinate vector spaces. His recent publications reflect a transition toward machine learning applications while maintaining his foundational mathematics work. The 15 most recent articles show increasing focus on neural network theory (2025), with earlier works covering financial mathematics, climate policy presentations, and pure mathematics topics like Banach-Tarski paradox and central limit theorem. As an advisor, he works with graduate student Aarya Garimella on neural network research, meeting regularly to refine slides and explore mathematical foundations of neural networks. His teaching activities include courses in linear algebra and financial mathematics. His diary reveals active engagement with climate policy through Citizens' Climate Lobby (CCL), including Capitol Hill lobbying efforts and local climate forum organization. He maintains detailed records of academic and civic activities while managing household responsibilities.

Patrick ForréView profile

Assistant Professor

• Causal Inference
• Machine Learning Theory
• Information Theory
• +4 more

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

• Spectral Graph Theory
• Unsupervised Learning
• Distributed Algorithms
• +3 more

Luca Zanetti is a Research Fellow at the University of Cambridge , affiliated with the Computer Laboratory and Department of Computer Science and Technology . He works under the supervision of Thomas Sauerwald, following his PhD at the University of Bristol with He Sun. Education PhD in Computer Science, University of Bristol His research focuses on spectral graph theory , unsupervised/semi-supervised learning on graphs , randomised and distributed algorithms , and Markov chains . His work bridges algorithm design, machine learning, and mathematical analysis of graphs. Recent publications explore clustering directed graphs , dynamic graph random walks , distributed graph sparsification , and spectral clustering guarantees . These span theoretical computer science , machine learning , and applied mathematics . He has taught advanced courses at Cambridge, including Probability and Computation (Part II/III/MPhil) and Advanced Topics in Machine Learning . He has served on program committees for SPAA 2020 and IJCAI 2020, and delivered talks at major conferences like ESA, ICALP, and Random Structures & Algorithms.

• Optimization Algorithms
• Network Flows
• Large Sparse Systems
• +5 more

Donald Goldfarb is the Alexander and Hermine Avanessians Professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research spans optimization algorithms, network flows, and large-scale computational methods, with applications in finance, image processing, and machine learning. Research Focus: Goldfarb specializes in developing efficient algorithms for convex and nonconvex optimization problems, including linear, quadratic, semidefinite, and second-order cone programming. His work extends to robust optimization for financial modeling and innovative methods for image restoration using total variation techniques. Publication Trends: His recent research emphasizes optimization in computational imaging (e.g., image decomposition and restoration) and finance (e.g., robust portfolio management). Algorithmic contributions include interior-point methods, Bregman iterations, and large-scale convex optimization techniques.

Stephen VavasisView profile

Professor

• Continuous Optimization
• Data Science
• Scientific Computing
• +3 more

Stephen Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo's Faculty of Mathematics, where he serves on the Steering Committee for the Math Business and Accounting Unit. His research is supported by NSERC grants. Education: B.A. in Mathematics, Princeton University (1984) M.A. (Part III Tripos) in Mathematics, University of Cambridge (1985) Ph.D. in Computer Science, Stanford University (1989) Research Focus: Vavasis specializes in continuous optimization with applications to data science, numerical linear algebra, and computational geometry. His work bridges theoretical foundations and practical implementations in scientific computing and mechanics, emphasizing algorithmic efficiency and real-world problem-solving. Awards & Honors: Guggenheim Fellowship Presidential Young Investigator Award Churchill Scholarship Hertz Graduate Fellowship Administrative & Teaching Roles: Served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science Graduate Programs (2021). Currently teaches optimization courses including CO372 (Portfolio Optimization), CO466/666 (Convex Optimization), and CO673/CS794 (Optimization for Data Science).

Kurt AnstreicherView profile

Professor

• Mathematical Programming
• Optimization Expertise
• Interior-Point Algorithms
• +3 more

Kurt Anstreicher is a Professor of Business Analytics at the University of Iowa's Tippie College of Business, with complementary appointments in the Department of Computer Science and Department of Industrial Engineering. He holds the Gary C. Fethke Chair in Leadership. His research focuses on mathematical programming, optimization, interior-point algorithms, and nonlinear programming. Anstreicher earned his Ph.D. from Stanford University in 1983 and a B.A. from Dartmouth College in 1978. Research interests include optimization theory and methods, particularly in nonconvex programming, convex relaxations, and algorithm design. His work addresses challenges in quadratic programming, semidefinite optimization, and global optimization techniques. Recent contributions explore applications in trust-region subproblems, copositive cones, and maximum-entropy sampling. Publications span topics like convex hull representations, Kronecker product constraints, and spherical codes. His work bridges theoretical advancements and practical computational methods, with implications for operations research, data analysis, and engineering systems.

Michael TsatsomerosView profile

Professor

• Linear Algebra
• Matrix Analysis
• Nonnegative Matrices
• +8 more

Michael Tsatsomeros is a Professor of Mathematics and Statistics at Washington State University (WSU), Pullman, WA, where he has served since 2007. He holds a PhD in Mathematics from the University of Connecticut (1990) and has extensive academic experience, including roles at the University of Regina, Canada, and visiting positions at institutions like the Indian Institute of Technology-Madras. His research focuses on linear algebra, matrix analysis, nonnegative matrices, numerical range, and applications in dynamical systems, control theory, and machine learning. Education: PhD in Mathematics (1990, University of Connecticut), M.Sc. in Mathematics (1986, Concordia University), and a Diploma in Mathematics (1984, University of Patras, Greece). Research interests include matrix positivity, numerical linear algebra, and applications of matrix theory in interdisciplinary fields like AI and data science. He has authored/co-authored books such as Matrix Positivity (2020) and Linear Algebra and Matrix Methods (2024). His work has been recognized with awards including the 2024 Tom & Julie Lutz Teaching Excellence Award and the 2021 Excellence in Graduate Advising. Key research trends in his articles include P-matrices, Cayley transforms, and matrix positivity classes, with contributions to both theoretical and applied aspects of linear algebra. His grants include collaborations with the SPARC program and NSERC funding. He advises and mentors numerous graduate students, including current PhD candidates and over 20 completed students. His lab and collaborative work emphasize matrix analysis and its interdisciplinary applications.

Antoine LevittView profile

Assistant Professor

• Quantum Chemistry
• Materials Science
• Numerical Analysis
• +3 more

Antoine Levitt is an Assistant Professor (CPJ) at the Laboratoire de Mathématiques d'Orsay, Université Paris-Saclay. Previously, he was a researcher at Inria Paris in the MATHERIALS project-team, affiliated with CERMICS at École des Ponts. His research focuses on numerical methods for quantum chemistry and materials science, including density functional theory (DFT), Wannier functions, and electronic structure calculations. He has contributed to theoretical advances in numerical analysis, such as error bounds for DFT and preconditioning techniques for eigenvalue problems. Levitt’s work spans computational physics and applied mathematics, with applications to quantum dynamics, response properties, and defect systems. He has developed open-source software tools like DFTK.jl and Wannierization algorithms . His teaching includes courses on scientific computing, functional analysis, and quantum mechanics at both undergraduate and graduate levels. Key research themes include numerical stability in DFT, analysis of linear response theory, and efficient implementations of conjugate gradient methods. His recent work explores machine learning applications in canonical sampling and mathematical foundations of Density Matrix Embedding Theory.

Mattia ZorziView profile

Associate Professor

• Learning Dynamic Networks from data
• Robust and distributed filtering
• Dynamic Brain Networks
• +8 more

Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.

Miles LopesView profile

Associate Professor

• Bootstrap Methods
• High-Dimensional Inference
• Error Estimation
• +3 more

Miles Lopes is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on developing and analyzing bootstrap methods for high-dimensional statistical problems, with particular attention to error estimation in randomized algorithms and high-dimensional inference. He holds a Ph.D. from UC Berkeley. Research interests include bootstrap approximation techniques, high-dimensional covariance estimation, spectral statistics, and applications of randomized algorithms in numerical linear algebra. His work bridges theoretical statistics and computational methods, addressing challenges in modern data analysis. Recent publications emphasize bootstrap methods for eigenvalue analysis in high-dimensional PCA, operator norm approximation, and robust statistical inference in complex models. His methodologies have applications in functional data analysis, multivariate testing, and software development for randomized algorithms.

• Statistical Signal and Image Analysis
• Biomedical Applications
• Environmental Statistics
• +3 more

Armin Schwartzman is a Professor at the Halıcıoğlu Data Science Institute and Department of Biostatistics at the University of California, San Diego (UCSD). He holds a PhD in Statistics from Stanford University, an MSc in Electrical Engineering from the California Institute of Technology, and a BSc in Electrical Engineering from Technion-Israel Institute of Technology. His research focuses on statistical methods for signal and image analysis with applications in biomedical and environmental domains, including spatial inference, high-dimensional data analysis, and random field theory. Education : PhD in Statistics, Stanford University, 2006 MSc in Electrical Engineering, California Institute of Technology, 1996 BSc in Electrical Engineering, Technion-Israel Institute of Technology, 1995 Research Interests : Dr. Schwartzman develops statistical methodologies for analyzing complex data, particularly in neuroimaging, climate science, and genomics. Key areas include spatial inference for image analysis, multiple testing in high-dimensional data, and applications of random field theory. His work bridges theoretical statistics and practical challenges in biomedical and environmental research. Grants & Funding : NIH R01EB026859 (2019–2023): Spatial inference methods for image analysis NIH R21EB013795 (2012–2015): Voxelwise analysis of imaging response in neuro-oncology NIH R01CA157528 (2012–2019): Multiple testing methods for random fields Advising : Current advisees include Sam Davenport, Anubhav Singh-Sachan, and others. Former students have transitioned to roles at institutions like Moderna, University of Florida, and Cruiser. Labs & Collaborations : His research group works on projects such as: Random field theory for noise modeling in images Spatial inference in neuroimaging and climate data Machine learning for medical imaging (e.g., glaucoma diagnosis) Mountain glacier monitoring via satellite imagery

James W. Lark, IIIView profile

Associate Professor

• Computational Statistics
• Simulation/Statistical Modeling
• Risk and Decision Analysis
• +3 more

James W. Lark, III is an Associate Professor holding a primary appointment at the Center for Applied Mathematics and a joint appointment in the Department of Systems and Information Engineering at the University of Virginia. He has served in multiple roles, including Director of Research at DRA Research and Assistant to the Athletics Director for Special Projects at UVA. His research focuses on computational statistics, stochastic modeling, and optimization. Education: Ph.D. in Systems Engineering (UVA, 1990) and B.S. in Mathematics (Virginia Tech, 1977). He developed the 'Lark algorithm,' a foundational procedure in probabilistic modeling now widely referenced in academic literature. Research Interests include risk analysis, simulation methodologies, and decision-making frameworks. He has taught courses across applied mathematics, statistics, and systems engineering, such as APMA 3080 (Linear Algebra) and SYS 6003 (Optimization I). Awards include the 2011 Outstanding Undergraduate Teaching Award from the International Council on Systems Engineering. His work spans interdisciplinary collaborations, with affiliations extending to the Department of Statistics and institutions like UC Berkeley and Virginia Tech.