Find Academic Supervisors

• Nonlinear Algebra
• Metric Algebraic Geometry
• Numerical Algebraic Geometry
• +3 more

Paul Breiding is a Professor for Mathematical Methods in Data Science at the University of Osnabrück, within the Faculty of Mathematics/Computer Science/Physics. He is part of the Applied Algebra and Data Analysis working group and the Research Unit Data Science. His research focuses on nonlinear algebra, metric algebraic geometry, and their applications in numerical methods and data science. He is a Fellow of the Junge Akademie Mainz and co-authored the book 'Metric Algebraic Geometry' with Kathlen Kohn and Bernd Sturmfels. His work includes developing the software HomotopyContinuation.jl (v.2.11), which is widely used for numerical algebraic geometry. His research interests span algebraic geometry, tensor decompositions, and computational methods. Recent publications investigate geometric properties of algebraic varieties, condition numbers in tensor approximations, and probabilistic aspects of algebraic structures. Key contributions include studies on the reach of algebraic manifolds, typical ranks of random tensors, and sensitivity analysis in numerical algorithms. His work bridges theoretical algebraic geometry with practical computational tools for data science applications. Software: HomotopyContinuation.jl (v.2.11) Labs/Teams: Applied Algebra and Data Analysis, Research Unit Data Science

Gabriele SANTINView profile

Researcher

• Numerical Analysis
• Kernel Methods
• Approximation Theory
• +5 more

Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.

Wei QianView profile

Associate Professor

• High-dimensional statistics
• Sequential decision making
• Model selection
• +10 more

Wei Qian is an Associate Professor of Statistics at the Department of Applied Economics and Statistics, University of Delaware, affiliated with the College of Agriculture & Natural Resources. He is also an affiliated faculty member of the Data Science Institute at the University of Delaware. Education: Ph.D. in Statistics, minor in Mathematics, University of Minnesota, Twin Cities, 2014 M.S. in Chemistry, Stanford University, 2008 B.S. in Chemistry, Peking University, Beijing, 2006 His research focuses on high-dimensional statistics, sequential decision making, model selection, dimension reduction, nonparametric and semiparametric estimation, statistical computing, actuarial statistics, and deep learning. His work addresses complex data analysis challenges across domains like insurance, epidemiology, and network modeling. Recent publications emphasize machine learning applications in Hypergraph Neural Networks Multi-armed Bandit Problems Gender Equity in Hiring Covid-19 Epidemiological Modeling Interbank Network Risk Analysis High-dimensional Censored Data Scientific Awards: JPMC Fellow, Institute for Financial Services Analytics, University of Delaware (2017 -- present) Grants: NSF DMS funding support

• Combinatorics
• Discrete Geometry
• Computer Science
• +14 more

Daniel J. Kleitman is a Professor at the Department of Mathematics, Massachusetts Institute of Technology . His work spans multiple disciplines, including combinatorics, discrete geometry, computer science, and operations research. Over a career exceeding five decades, he has published 190 indexed articles since 1964, with significant citations (2,431 in total) from 2,755 unique authors. Primary Affiliation : MIT Department of Mathematics Collaborations : Extensive work with Noga Alon (13 collaborations), Zoltán Füredi (10), Curtis Greene (8), and others. Research Focus: Kleitman's work centers on combinatorics and discrete mathematics, with applications in computer science, optimization, and graph theory. His recent publications highlight algorithm design for vehicle routing, coding theory, and structural analysis of planar graphs. Key subfields include Ramsey theory, matching theory, error-correcting codes, and discrete geometry. Scientific Contributions and Collaborations: With over 150 coauthors, Kleitman has contributed to foundational and applied problems in mathematics and computer science. His work has been published in leading journals such as Discrete Math. , Journal of Combinatorial Theory , and SIAM Journal on Applied Mathematics . Notable collaborations include research with Jacob Fox on Rado's boundedness conjecture and with Angela Bassa et al. on planar graph partitioning.

• Bayesian computation
• Monte Carlo methods
• Sequential Monte Carlo
• +4 more

Nicolas Chopin is a Professor of Data Sciences at ENSAE, Institut Polytechnique de Paris. He joined ENSAE in 2006 after serving as a lecturer at the University of Bristol (2003-2006). He holds a PhD from Université Paris VI (2003) and an HDR (habilitation) earned in 2010. His research centers on Bayesian computation methodologies, including: Sequential Monte Carlo (particle filters) Markov chain Monte Carlo Variational inference Probabilistic Machine Learning He develops computational frameworks for complex statistical inference problems. Analysis of his recent publications (2022-2025) reveals strong emphasis on: Monte Carlo innovations (e.g., waste-free SMC, quasi-Monte Carlo), scalable Bayesian modeling, debiasing techniques for sequential inference, and applications in optimization/bandit problems. Theoretical rigor combined with computational efficiency is a consistent theme. Awards: Savage Award for Best Doctoral Dissertation in Bayesian Statistics (2002)

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

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

Lydia ZakynthinouView profile

Assistant Professor

• Data Privacy
• Differential Privacy
• Machine Learning Theory
• +4 more

Lydia Zakynthinou is an Assistant Professor in the Department of Computer Science at Johns Hopkins University and a member of the Johns Hopkins Data Science and AI Institute. Her research centers on theoretical foundations of trustworthy machine learning, focusing on privacy-preserving methods with formal guarantees and exploring connections between privacy, robustness, and generalization. Education: PhD in Computer Science, Northeastern University (advised by Jonathan Ullman and Huy Lê Nguyễn) MSc in Logic, Algorithms, and Theory of Computation, National Kapodistrian University of Athens Diploma in Electrical and Computer Engineering, National Technical University of Athens Her research spans Data Privacy (particularly differential privacy), Machine Learning Theory , and Responsible Computing . She develops privacy-preserving methods with rigorous theoretical analysis while maintaining practical performance, investigating fundamental limitations and surprising connections to robustness and memorization in learning algorithms. Her work bridges theoretical guarantees with real-world applicability for data practitioners. Recent publications (2018-2025) demonstrate consistent focus on differential privacy in high-dimensional statistics, private mean estimation, and robustness-privacy tradeoffs. She has published in top venues including NeurIPS, ICML, and COLT, with multiple spotlight presentations recognizing significant contributions to private machine learning theory. Scientific Awards: Meta Research PhD Fellowship (2020) Northeastern University Khoury College PhD Research Award (2022) Foundations of Data Science Institute postdoctoral fellowship Northeastern University Dissertation Fellowship (2023) Dr. Zakynthinou actively contributes to the academic community through program committee service for IEEE S&P, ICML, NeurIPS (Technical and Ethics Reviewer), TPDP, AAAI, FAccT, and COLT. She organizes the Boston-area Differential Privacy Seminar and NEU Theory Seminar, and co-founded the Khoury PhD Women Group. Her research has been supported by prestigious fellowships including Meta Research and FODSI. As a member of the Johns Hopkins Data Science and AI Institute and Learning Theory Alliance workshop committee, she holds weekly office hours and organizes community events including fireside chats with leading researchers and mentorship roundtables for early-career scholars.

• Manufacturing Engineering
• Machining
• Production Engineering
• +3 more

Milenko Sekulić is a Full Professor at the Chair of Machining within the Department of Production Engineering, Faculty of Technical Sciences, University of Novi Sad. He has held this position since 2017 and currently serves as Head of the Chair of Machining (since 2018), following previous roles as Associate Professor (2012-2017) and Assistant Professor (2008-2012) at the same institution. His academic foundation includes a Bachelor's degree (1993), Magister degree (1998), and PhD (2007), all in Mechanical Engineering from the Faculty of Technical Sciences. His educational journey focused on cutting force analysis in milling, development of cutting force calculation models, and determination of drilling forces. Professor Sekulić's research centers on advanced manufacturing engineering, specializing in machining process optimization, thermal analysis, and cutting force prediction. His work integrates fuzzy logic, evolutionary algorithms, and response surface methodology to solve complex problems in electrical discharge machining, abrasive water jet systems, and high-pressure jet assisted turning. Key contributions include energy efficiency models for EDM, surface roughness prediction frameworks, and thermal state optimization in creep-feed grinding. Analysis of his 15 most recent publications reveals a strong trend toward sustainable and intelligent manufacturing solutions. His work consistently addresses material-specific challenges (particularly nickel-based alloys), develops predictive models for machining outcomes, and pioneers hybrid techniques like laser-assisted cutting. The research demonstrates increasing sophistication in combining computational intelligence with traditional manufacturing physics. No scientific awards or fellowships are documented in the available information. Professor Sekulić actively mentors students through thesis supervision and teaching, building on his own academic progression within the institution. His leadership as Head of Chair involves guiding research direction and laboratory activities for the machining group. International collaborations with Slovenian institutions (evident in co-authored publications) highlight his cross-border research engagement. He leads the Chair of Machining research team, which maintains specialized laboratories for conventional and non-conventional machining processes including EDM, water jet systems, and high-pressure jet assisted equipment. Current work focuses on AI-driven process optimization and sustainable manufacturing techniques.

Iftach HaitnerView profile

Professor

• Cryptography
• Computational Complexity
• Secure Computation
• +3 more

Iftach Haitner is a Professor at Tel Aviv University's School of Computer Science, currently on leave while serving as Principal Researcher at the Stellar Development Foundation. His primary academic affiliation remains with Tel Aviv University where he maintains an active research group and supervises multiple PhD students. His educational background includes a PhD (2008) and Master's degree from the Weizmann Institute of Science under Omer Reingold and Oded Goldreich respectively, and undergraduate studies in Mathematics and Computer Science at Tel Aviv University. His research focuses on Cryptography and Computational Complexity , with significant contributions to coin-flipping protocols, one-way functions, differential privacy, and secure computation. Analysis of his 15 most recent publications reveals consistent focus on foundational cryptographic problems. His work demonstrates strong emphasis on computational entropy concepts (inaccessible entropy, next-block pseudoentropy), protocol security against adaptive adversaries, and tight complexity bounds for cryptographic primitives. The publications span top venues including STOC, FOCS, Crypto, and Eurocrypt, showing sustained high-impact research output. The Kadar Family Award for Outstanding Research (2018) Tel Aviv University Rector's Awards for Excellence in Teaching (2017, 2018) SIAM Outstanding Paper Prize (2011) Intel Israel award for outstanding PhD students (2008) Multiple best paper awards (CRYPTO 2006, ICALP 2006) Haitner has advised numerous PhD students including Noam Mazor, Jad Silbak, and Eliad Tsfadia. His research has been supported by multiple Israel Science Foundation grants (2011-2023), an ERC Starting Grant (2015-2020), and Blavatnik ICRC grants. He also maintains industry connections through roles at Coinbase (2022-2024) and previous consulting positions at Unbound Security and Team8. His professional service includes editorial work for SIAM Journal on Computing and program committee memberships for major cryptography conferences including Crypto, Eurocrypt, and TCC. He co-organizes the Greater Tel Aviv Area Cryptography Seminar and other specialized workshops.

• 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.

Tom GriffithsView profile

Professor

• Cognitive Science
• Machine Learning
• Bayesian Statistics
• +5 more

Tom Griffiths is a Professor of Psychology and Computer Science at Princeton University, where he directs the Computational Cognitive Science Lab and the Princeton Laboratory for Artificial Intelligence. He mentors graduate students across Psychology, Computer Science, and Neuroscience departments, fostering interdisciplinary research at the intersection of human cognition and artificial intelligence. His research develops mathematical models to understand human cognition, emphasizing how human intelligence uniquely operates under constraints of time, computational resources, and communication bandwidth. Using Bayesian statistics, he explains learning from small data; resource rational analysis reveals efficient cognitive strategies; and studies of cultural evolution and language show how humans combine knowledge across minds. This work bridges cognitive science and AI, aiming to build more human-like machines and improve human decision-making through cognitive prostheses. Griffiths leads two prominent research groups: the Computational Cognitive Science Lab investigates computational principles of human thought through theoretical modeling and behavioral experiments, while the Princeton Laboratory for Artificial Intelligence supports innovative AI research. His lab's recent work analyzes large language models using Bayesian frameworks and develops distributed computing models for human collaboration. He actively advises graduate students and has contributed significantly through publications and the best-selling book Algorithms to Live By . His research on cognitive prostheses and metareasoning provides practical tools for enhancing human decision-making in everyday contexts, with applications in behavioral economics and human-AI interaction.

• Numerical Mathematics
• Data Assimilation
• Hamiltonian Dynamics
• +5 more

Sebastian Reich is a Professor of Numerical Analysis at the University of Potsdam and holds an honorary Visiting Professorship at Imperial College London . He leads the Chair of Numerical Mathematics and serves as Editor-in-Chief of the SIAM/ASA Journal on Uncertainty Quantification since 2021. Research Interests Numerical methods for Hamiltonian systems Data assimilation in geoscience Stochastic particle filters Bayesian inference algorithms Molecular dynamics simulation Multi-scale modeling Collaborative Projects : Principal Investigator and former Speaker (2017-2024) of SFB 1294 Data Assimilation , a DFG-funded Collaborative Research Center Active participant in SFB 1114 Scaling Cascades in Complex Systems at Freie Universität Berlin Books Authored : Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge UP, 2015) Simulating Hamiltonian Mechanics (Cambridge UP, 2005) Technical Contributions : Development of symplectic integration methods Innovations in ensemble Kalman filtering Regularization approaches for geophysical models Stochastic algorithms for molecular simulations

Jennifer MuellerView profile

Professor

• Inverse Problems
• Medical Imaging
• Electrical Impedance Tomography
• +3 more

Jennifer Mueller is a Professor in the Department of Mathematics and holds a joint appointment in the School of Biomedical Engineering and Department of Electrical and Computer Engineering at Colorado State University. She serves as Graduate Director and Associate Chair in Mathematics, and was honored as Professor Laureate by the College of Natural Sciences. As founder of the EIT Lab at CSU (funded by NIH and departmental resources), she develops clinical applications of electrical impedance tomography. Ph.D. in Mathematics and Statistics, University of Nebraska-Lincoln (1997) NSF Postdoctoral Fellow at Rensselaer Polytechnic Institute (1997-2000) Her research focuses on inverse problems in biomedical imaging, particularly electrical impedance tomography (EIT) and ultrasound computed tomography (USCT). Key projects include nonlinear regularization of D-bar methods, real-time EIT implementation, and hardware design for clinical lung imaging. Her work bridges mathematical theory and clinical translation through collaborations with University of São Paulo and NIH. Recent publications emphasize dynamic priors in D-bar reconstructions, complex admittivity imaging, and clinical EIT applications for pulmonary disorders. She has pioneered anatomical atlas integration and Schur complement properties to enhance image resolution. Scientific Awards NSF Postdoctoral Fellowship (1997-2000) Mueller has advised 6 PhD students and 3 MS students, with current advisees working on EIT algorithms, hardware development, and clinical applications. She co-organized the inaugural Mummy Range Workshop (2002), and her book Linear and Nonlinear Inverse Problems with Practical Applications (SIAM, 2012) provides computational frameworks for emerging researchers. The EIT Lab under her leadership focuses on direct reconstruction algorithms, pulmonary imaging, and biophysics of impedance measurements.

• Machine Learning Theory
• Game Theory
• Information Theory
• +9 more

Wouter M. Koolen is a Professor of Mathematical Machine Learning at the University of Twente and a Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. Appointed to his professorship on June 1, 2022, he delivers his expertise across both institutions with offices in Enschede and Amsterdam. He is actively engaged in academic leadership through his organization of the Machine Learning Theory Research Semester Programme at CWI in Spring 2023 and serves as an ELLIS Scholar since December 2020. Dr. Koolen's research spans machine learning theory with particular focus on pure exploration in multi-armed bandit models , game tree search algorithms , and provably accelerated learning in both statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His work bridges theoretical foundations with practical applications, especially in safe statistical testing using e-values. He maintains active collaborations through INRIA-CWI associate teams 6PAC with Inria Lille and 4TUNE with Inria Paris and Grenoble. His recent publications reveal a strong emphasis on developing theoretically sound methods for statistical inference that maintain validity under optional stopping and continuation, representing a significant shift from traditional p-value based approaches. This work has important implications for fields requiring rigorous statistical guarantees in adaptive experimental settings. NWO VENI grant recipient QUT Vice-Chancellor's postdoctoral research fellowship awardee ELLIS Scholar (elected December 2, 2020) Member of ACM Future of Computing Academy Professor Koolen has supervised numerous PhD students to completion, including Hongwei Wen, Clément Lezane, and Tyron Lardy in 2025, and formerly Rianne de Heide who won the VVSOR Willem R. van Zwet award. He has served on program committees for major conferences including COLT, ICML, and ALT, and actively organizes workshops on cutting-edge topics in machine learning theory. His research group at CWI hosts regular reading groups and seminar series, fostering a vibrant theoretical machine learning community in the Netherlands.

• Minimal Surfaces
• Geometric Analysis
• Conformal Geometry
• +3 more

Samuel Bronstein is a PostDoctoral Research Fellow at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig, Germany. Previously, he completed his PhD at the Département de Mathématiques et Applications (DMA) of École Normale Supérieure (ENS) in Paris under Nicolas Tholozan and remains a member of the GDR Platon research group in Geometry and Groups. Education: PhD in Mathematics, École Normale Supérieure (completed August 2024) His research centers on geometric structures with expertise in Minimal Surfaces, Geometric Analysis, Conformal Geometry, Higgs Bundles, Higher Kleinian Groups, and Negative Curvature. Work integrates representation theory, dynamical systems, and Teichmüller theory to solve problems in geometric analysis. Analysis of his 2023-2025 publications reveals dominant themes: almost-Fuchsian structures in symmetric spaces, asymptotic Plateau problems, and Anosov representations, with cross-disciplinary contributions to mathematical biology via epidemic modeling. He actively shapes the academic landscape through workshop organization (e.g., 2025 Universal Teichmüller Theory Workshop) and participation in major conferences including IHP Program 'Highrags' (2025) and '20 Years of Anosov Representations' (2024).