Find Academic Supervisors

Tom HutchcroftView profile

Professor

• Probability Theory
• Percolation Theory
• Phase Transitions
• +4 more

Tom Hutchcroft is a Professor of Mathematics at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics, and Astronomy. He specializes in probability theory, particularly percolation theory, focusing on phase transitions in non-Euclidean geometries and fractal structures. In 2024, he was awarded the prestigious Packard Fellowship for Science and Engineering, providing $875,000 over five years to support his research. His work includes groundbreaking contributions such as proving the existence of two phase transitions in negatively curved spaces and solving Schramm's locality conjecture with graduate student Philip Easo. Education Bachelor's degree in Mathematics, University of Cambridge (2013) PhD in Mathematics, University of British Columbia (2017) Postdoctoral Fellowships at the University of Cambridge (2017–2021) Research Interests Hutchcroft explores percolation dynamics in complex systems, including phase transitions, long-range percolation, and critical phenomena. His work bridges pure mathematics and theoretical physics, addressing foundational problems like the behavior of three-dimensional percolation at criticality. He investigates how mathematical frameworks can unify seemingly unrelated physical systems. Awards 2024 Packard Fellowship for Science and Engineering Advising & Grants Hutchcroft advises graduate student Philip Easo and leverages the Packard Fellowship to pursue high-risk, high-reward projects. His research is supported by grants enabling exploration of nonamenable graphs and long-range percolation models. Labs/Teams He collaborates with Caltech's Mathematics Department and participates in interdisciplinary initiatives within the Division of Physics, Mathematics, and Astronomy, contributing to research centers like the Institute for Quantum Information and Matter (IQIM).

Chinmoy BhattacharjeeView profile

Professor

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +4 more

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Yifan JingView profile

Assistant Professor

• Additive and Combinatorial Number Theory
• Combinatorics and Graph Theory
• Harmonic Analysis
• +8 more

Yifan Jing is an Assistant Professor in the Department of Mathematics at The Ohio State University (OSU). He previously held postdoctoral positions at the University of Oxford’s Mathematical Institute and Wolfson College, mentored by Ben Green. His academic journey includes a PhD from the University of Illinois Urbana-Champaign (2021), supervised by József Balogh and Xiaochun Li, an M.Sc. from Simon Fraser University (2018) under Bojan Mohar, and a B.Sc. from the University of Science and Technology of China (2016), advised by Jack Koolen. His research focuses on Arithmetic Combinatorics, Analytic and Combinatorial Group Theory, Lie groups, Abstract Harmonic Analysis, Representation Theory, Additive Number Theory, Model Theory applications, Discrete Probability, and Theoretical Computer Science. Notable contributions include work on measure growth in Lie groups, inverse theorems for geometric inequalities, and structural graph theory. He received the 2023 Kirkman Medal for his research contributions. He currently organizes OSU’s Combinatorics Seminar and teaches Math 4507: Geometry. His advising includes PhD students Yewen Sun, Chavdar Lalov, and Yuchen Meng, alongside mentoring multiple undergraduate researchers at Oxford. Collaborators include leading mathematicians such as Chieu-Minh Tran, Ruixiang Zhang, and Bojan Mohar. His work bridges analysis, algebra, and logic to address problems across combinatorics, number theory, and geometry.

Anurag SahayView profile

Visiting Assistant Professor

• Number Theory
• Mathematical Analysis
• Combinatorics
• +3 more

Anurag Sahay is a Golomb Visiting Assistant Professor of Mathematics at Purdue University, affiliated with the Department of Mathematics within the College of Science. His contact information includes the phone number 765-494-0481 and office MATH 402. He holds the academic rank of Visiting Assistant Professor and is not part-time. His research interests span a broad range of mathematical disciplines, with a focus on analytic number theory, mathematical analysis, and combinatorics. Sahay explores topics such as zeta functions (Riemann and Hurwitz), function fields, quadratic residues, hypergraph Turán problems, and geometric configurations. His work combines theoretical insights with computational methods, addressing questions related to moments of zeta functions, paucity problems in number theory, and distinct distances in combinatorial geometry. Recent publications (2023–2025) highlight his contributions to understanding zeta function moments, shifted convolution problems, and VC dimension applications. These articles reflect a consistent theme of advancing knowledge in analytic number theory and its intersections with algebra, combinatorics, and geometry. No scientific awards or grants are explicitly mentioned in the provided texts. Sahay has no listed advisees or master’s/PhD students at this time. While no specific laboratories or research teams are detailed, his affiliations with Purdue University’s Department of Mathematics suggest involvement in collaborative research environments typical of such institutions.

Wolfgang MulzerView profile

Professor

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Zvi GalilView profile

Professor

• Algorithms
• Complexity Theory
• Cryptography
• +3 more

Zvi Galil is a distinguished academic and former Dean of Computing at Georgia Institute of Technology (2010-2019). He holds the title of Storey Chair and serves as Executive Advisor for Online Programs. His academic journey includes leadership roles at Columbia University (Fu Foundation School of Engineering Dean, 1995-2007) and Tel Aviv University (President, 2007-2009). He earned degrees in Applied Mathematics from Tel Aviv University and a PhD in Computer Science from Cornell University. Galil’s research focuses on algorithms, complexity theory, cryptography, and stringology. He has authored over 200 papers and edited 5 books, with contributions to graph algorithms, parallel computing, and data structures. He is a Fellow of the ACM and American Academy of Arts and Sciences, and a member of the National Academy of Engineering. His work has influenced fields like online education through initiatives like OMSCS (Georgia Tech’s Online Master of Science in Computer Science). Educations: BS and MS (summa cum laude), Applied Mathematics, Tel Aviv University PhD, Computer Science, Cornell University His research trends span dynamic graph algorithms, real-time string processing, and scalable graph isomorphism techniques. He has also contributed to foundational areas like suffix trees and text indexing. His awards include the Columbia Great Teacher Award (2009) for pedagogical excellence. Awards: ACM Fellow Member, American Academy of Arts and Sciences Member, National Academy of Engineering Columbia Society of Graduates Great Teacher Award Galil advises on online education programs and collaborates with the Algorithms and Randomness Center (ARC) at Georgia Tech. He has held editorial roles at major journals and advised Oxford University Press on computer science publications.

Demetres ChristofidesView profile

Associate Professor

• Combinatorics
• Random Graphs
• Extremal Graph Theory
• +10 more

Dr. Demetres Christofides is an Associate Professor in Mathematics and Course Leader of BSc (Hons) Mathematics & Statistics at the University of Central Lancashire Cyprus (UCLan Cyprus). He holds a PhD in Mathematics from the University of Cambridge (2008), alongside advanced qualifications from Cambridge including a BA (2003), MA (2007), and Certificate of Advanced Study in Mathematics (2004). Affiliations: School of Sciences, Department of Mathematics. Administrative Roles: Former Senate Member (2015–2020), Deputy Head of School (2017–2018), and Academic Standards Lead (2017–2020). Research Interests: Focuses on combinatorics, including random graphs, extremal graph theory, positional games, and algorithmic graph theory. His work spans Hamilton cycles, Cayley graphs, information theory, and structural graph properties. He has contributed to over 10 peer-reviewed publications in journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Funding & Grants: Secured research grants from the Cyprus Research Promotion Foundation and the EPSRC, including projects on extremal combinatorics and graph algorithms. Awards: Smith Prize (University of Cambridge). Outreach: Trains Cypriot Mathematical Olympiad teams, creates problems for international competitions (e.g., IMO Shortlist 2020), and co-authored a training book for high-school mathematics competitions. Teaching: Leads and teaches modules across all undergraduate years, including Real Analysis, Algebra, and Complex Analysis. Previously taught at Charles University, Warwick, and Birmingham.

• Non-uniform Random Number Generation
• Graph Theory
• Nodal Sets
• +2 more

Josef Leydold is Associate Professor of Statistics and Mathematics at Vienna University of Economics and Business. His research develops computational methods for random variate generation and graph theoretical applications. Research Areas: Design of black-box algorithms for non-uniform random numbers; geometric properties of graph Laplacians; spherical harmonics applications; and optimization techniques. Publications: Focus on practical algorithms for statistical computing, including monographs on automatic random variate generation and mathematical foundations for economists. No awards or student supervision details are documented.

• Machine Learning
• Graph Analysis
• Natural Language Processing
• +7 more

Michalis Vazirgiannis is a Professor at the Department of Computer Science, École Polytechnique, France, leading the Data Science and Mining (DaSciM) group. He holds degrees in Physics (Athens University), Informatics (PhD, Athens University), and AI (Heriot-Watt University). His research focuses on machine learning for graphs, NLP, combinatorial optimization, and applications in fraud detection, biomedical tasks, and social networks. He has supervised 29 PhD theses and published over 250 papers. Awards include the ERCIM Fellowship, Marie Curie EU Fellowship, and Tencent's Rhino-Bird Award. Educations Bachelor's in Physics, Athens University Master's in AI, Heriot-Watt University PhD in Informatics, Athens University Research Interests His work spans graph neural networks (GNNs), community detection, text mining, and biomedical applications. He emphasizes explainability in GNNs and has developed models like RW-GNN. Current projects include the ANR-HELAS Chair on heterogeneous data and the X/AXA Data Science Chair. Industrial collaborations include Airbus, Google, and Tencent. Grants & Chairs ANR-HELAS Chair (2020–25): Deep learning for graphs and NLP AXA Data Science Chair (2015–18) ANR-XCOVIF (2020–21): Social impact analysis of COVID-19 via social media Labs & Teams The DaSciM group has 15 completed PhD theses and collaborates with industry on projects like predictive maintenance and recommendation systems. Key projects include graph generative models for biomedical data and privacy-preserving synthetic medical records.

• Operator Algebras
• Random Matrices
• Privacy-Preserving Data Synthesis
• +4 more

March Boedihardjo is an Assistant Professor in the Department of Mathematics at Michigan State University (MSU). His academic journey includes postdoctoral roles at ETH Zurich (2022-2023), the University of California, Irvine (2021-2022), and UCLA (2016-2021). He earned his Ph.D. in Mathematics from Texas A&M University (2012-2016) under advisors Bill Johnson and David Kerr, and holds a B.Sc./M.Phil. from Hong Kong Baptist University (2007-2011). Boedihardjo's research focuses on operator algebras, random matrices, and privacy-preserving data synthesis. His work bridges functional analysis and modern data science, with contributions to covariance loss minimization, max-sliced Wasserstein distances, and privacy-utility tradeoffs in synthetic data. He has presented at venues like Carnegie Mellon's Probability/Math Finance Seminar and the International Conference on Machine Learning (ICML). His teaching includes advanced courses like MTH 890: Readings in Mathematics. His research outputs span foundational mathematics (e.g., C*-algebra representations on ℓp spaces) and applied topics (e.g., differentially private synthetic data generation). While no specific awards are listed, his active conference participation reflects scholarly engagement in operator theory, probability, and data science intersections.

Rostislav GrigorchukView profile

Professor

• Group Theory
• Dynamical Systems
• Low dimensional Topology
• +3 more

Rostislav Grigorchuk is a Distinguished Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Science. His research focuses on Group Theory, Dynamical Systems, Low-dimensional Topology, Discrete Mathematics, Abstract Harmonic Analysis, and Random Walks. He earned his Ph.D. from Moscow Lomonosov State University in 1978. Education: Ph.D., Mathematics, Moscow Lomonosov State University (1978) B.Sc./M.Sc., Mathematics, Moscow Lomonosov State University (1970–1975) Research Interests: Grigorchuk’s work spans group theory and its applications, including branch groups, spectral properties, and dynamical systems. His recent articles explore topics such as subgroup structures, spectral analysis of graphs, and integrable systems. Teaching: He has taught advanced courses like Modern Algebra, Probability, and Linear Algebra, reflecting his expertise in foundational mathematical disciplines. Editorships: He serves on editorial boards for journals including Groups, Geometry, and Dynamics , International Journal of Algebra and Computation , and Journal of Modern Dynamics .

Wesley PegdenView profile

Professor

• Discrete Mathematics
• Probabilistic Combinatorics
• Combinatorial Game Theory
• +7 more

Wesley Pegden is a Professor in the Department of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. He holds a Ph.D. from Rutgers University. His research focuses on Discrete Mathematics, including probabilistic combinatorics, combinatorial game theory, graph theory, and discrete geometry. A central theme of his work is the Abelian sandpile model, where he has explored fractal patterns and connections to Apollonian circle packings. Collaborations with Lionel Levine and Charles Smart have advanced understanding of the sandpile's geometric properties through novel constructions of integer superharmonic functions. His work extends to applications in network science, epidemiological modeling, and statistical sampling biases (e.g., analyzing the Bangladesh mask trial). Notably, he contributed to studies on gerrymandering and congressional districting, including an expert analysis of Pennsylvania's district map. Awards include the Sloan Research Fellowship. Research Highlights: Abelian sandpile fractal geometry, algorithmic local lemma extensions, and random graph dynamics. Key Collaborations: Lionel Levine, Charles Smart, Alan Frieze.

• Applied Mathematics
• Data Science
• Partial Differential Equations
• +7 more

Dejan Slepčev is a Professor and Associate Dean for Faculty and Graduate Affairs at the Mellon College of Science, Department of Mathematical Sciences, Carnegie Mellon University. His research focuses on applied analysis, with applications to data science, collective behavior of particle systems, and energy-driven systems. He holds a Ph.D. from the University of Texas at Austin. His research interests include partial differential equations, calculus of variations, optimal transportation, and their applications to machine learning tasks such as clustering and classification. He develops mathematical frameworks for analyzing variational and PDE-based problems on random data samples, improving algorithms for data science. He also studies systems of interacting particles, particularly those modeling collective behavior in biological and physical systems, and energy-driven systems involving pattern formation and interface evolution. His work bridges theoretical analysis and computational methods, addressing challenges in data analysis, image processing, and the dynamics of complex systems. Key contributions include studies on spectral clustering, graph-based semi-supervised learning, and the continuum limits of discrete variational problems. Education: Ph.D., University of Texas at Austin.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Daniel SussmanView profile

Assistant Professor

• Probability and Statistics
• Network Analysis
• Graph Theory
• +3 more

Dr. Daniel Sussman is an Assistant Professor at Boston University, affiliated with the Probability and Statistics research group. He also serves as the Associate Director of the Program in Statistics. His work focuses on statistical methodologies for network analysis, including graph matching, latent variable models, and causal inference under network interference. Dr. Sussman develops computational tools for analyzing complex networks, such as the iGraphMatch R package for graph matching analysis. His research bridges theoretical foundations with practical applications in neuroscience, epidemiology, and computational social science. Key research interests include network dynamics, stochastic processes on graphs, and statistical estimation in noisy network data. He explores geometric and algebraic properties of random graphs, as well as scalable algorithms for large network datasets. His contributions span theoretical results (e.g., asymptotic consistency of embeddings) to applied methods (e.g., unbiased estimation in exposure models). Dr. Sussman’s publications highlight advancements in graph matching techniques, including matched filter approaches and multi-scale heat diffusion methods. His work emphasizes robustness to noise and scalability for real-world networks. While no explicit awards are listed, his contributions reflect significant impact in statistical network analysis. His professional role as Associate Director involves overseeing statistical training programs and curricula. Though no advising details are provided, his research group likely supports graduate students focused on network science and computational statistics.