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Andrew ChildsView profile

Professor

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +2 more

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Richard NicklView profile

Professor

• Mathematical Statistics
• High-dimensional Inference
• Bayesian Nonparametrics
• +3 more

Richard Nickl is a Professor of Mathematical Statistics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Statistical Laboratory. His research focuses on high-dimensional inference, Bayesian nonparametrics, statistics for partial differential equations, and inverse problems. He has held significant grants, including an ERC Advanced Grant (2024–2029) and an EPSRC Programme Grant (2022–2027). His work bridges statistics, probability, and analysis, with contributions to theoretical foundations and computational methods in non-linear inverse problems. Key research interests include Bayesian posterior consistency, statistical inference for diffusions, and polynomial-time algorithms for high-dimensional posteriors. Notable publications include foundational monographs such as Mathematical foundations of infinite-dimensional statistical models (2016), which earned a PROSE Award, and recent advancements in Bayesian nonparametric inference for McKean-Vlasov models (2025). His group organizes workshops, such as the 2024 Statistical Aspects of Non-Linear Inverse Problems conference. Awards: 2017 PROSE Award in Mathematics. Grants: ERC Advanced Grant, EPSRC Programme Grant. Lab/Team: Research Group in Mathematical Statistics at DPMMS, focusing on inverse problems and Bayesian methodology.

June HuhView profile

Professor

• Algebraic Geometry
• Combinatorics
• Matroid Theory
• +5 more

June Huh is a Mathematics Professor at Princeton University's Department of Mathematics. His research focuses on the interplay between algebraic geometry, combinatorics, and matroid theory, with notable contributions to Hodge theory, tropical geometry, and log-concavity phenomena. He is actively involved in collaborative projects such as the FRG initiative on matroids, graphs, and algebraic geometry. Key research interests include matroid polytopes, Chow rings, Lagrangian geometry, and combinatorial applications of Hodge-Riemann relations. His work bridges discrete and continuous mathematics, with implications for enumerative geometry and geometric combinatorics. Recent publications explore topics like volume polynomials, Bergman fans, and singular Hodge theory in combinatorial geometries. He has received funding for interdisciplinary research through grants like the FRG Collaborative Research program. His contributions highlight innovative methods in geometric and algebraic combinatorics.

• Algebraic Combinatorics
• Representation Theory
• Statistical Mechanics
• +3 more

Greta Panova is a Gabilan Distinguished Professor of Science and Engineering and a Professor of Mathematics at the University of Southern California (USC). Her research focuses on Algebraic Combinatorics, with connections to representation theory, statistical mechanics, probability, and computational complexity theory. She also engages in molecular biology modeling. Panova holds editorial roles at journals including the Electronic Journal of Combinatorics, Arnold Mathematical Journal, and Communications of the American Mathematical Society. She is a writer/editor for the Putnam Mathematical Competition (2023-2025) and is currently supported by NSF grants in the CCF division. Her research interests span Algebraic Combinatorics, Representation Theory, Statistical Mechanics, Probability, and Computational Complexity Theory. Specific areas include Kronecker and Littlewood-Richardson coefficients, asymptotic behavior of combinatorial structures, and the interplay between algebraic structures and computational complexity. She also explores applications in molecular biology, particularly protein dynamics in DNA lesions. NSF grants in CCF division (current) Editorial roles at Electronic Journal of Combinatorics, Arnold Mathematical Journal, and others Contributor to the Putnam Mathematical Competition Panova's research is supported by NSF grants, focusing on computational complexity and algebraic combinatorics. She has advised students in areas related to her research, though specific names aren’t listed here. Grants have funded explorations into geometric complexity theory, asymptotic combinatorics, and molecular biology modeling. Her work involves collaborations across disciplines, including statistical mechanics and integrability, as highlighted in her white paper contributions.

Mike TehranchiView profile

Lecturer

• Mathematical Finance
• Stochastic Analysis
• Probability Theory
• +3 more

Dr. Mike Tehranchi is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) . His research focuses on mathematical finance, stochastic processes, and probability theory. He holds a Lecturer position and is actively involved in academic research, with notable contributions to financial models, term structure analysis, and stochastic calculus. His work bridges theoretical probability and applied finance, addressing topics such as interest rate modeling, implied volatility, and optimal investment strategies. Tehranchi’s research often intersects with optimization, statistical methods, and interdisciplinary applications in astrophysics and fluid dynamics. He maintains an active publication record and contributes to the academic community through his role in the Statistical Laboratory. Key research trends in his articles include the analysis of financial derivatives, stochastic processes in market dynamics, and the application of advanced mathematical techniques to real-world financial problems. His work emphasizes rigorous theoretical foundations while addressing practical challenges in quantitative finance. Dr. Tehranchi has no listed students or academic awards in the provided texts. He can be reached via email and is based in Room D1.04 at the Statistical Laboratory.

Gregory SmithView profile

Professor

• Algebraic Geometry
• Commutative Algebra
• Symbolic Computation
• +1 more

Gregory G. Smith is a Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on algebraic geometry, commutative algebra, and symbolic computation, with a particular interest in the interplay between positivity, convexity, and combinatorial structures. He holds a B.ScH from Queen's University, an MA from Brandeis University, and a PhD from the University of California, Berkeley. His research contributions include work on Hilbert schemes, toric varieties, and computational algebra, with publications in top-tier journals such as the Journal of the American Mathematical Society and Compositio Mathematica . He has received prestigious awards, including the Coxeter-James Prize (2012) and the André-Aisenstadt Prize (2007). Smith is also an editor of the Journal of Software for Algebra and Geometry . He has advised multiple graduate students, including Sasha Zotine (PhD 2024) and Benjamin Hersey (PhD 2021). His teaching spans undergraduate and graduate courses in algebra, geometry, and combinatorics, emphasizing rigorous mathematical reasoning and computational tools.

• Real Algebraic Geometry
• Combinatorics
• Convex Optimization
• +3 more

Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington. Her research focuses on real algebraic geometry, combinatorics, and convex optimization, with applications to hyperbolic polynomials, determinantal representations, and convex algebraic geometry. She collaborates extensively on projects involving numerical ranges, quasicrystals, and geometric optimization problems. Research Interests: Real algebraic geometry and its connections to combinatorics and optimization Hyperbolic and log-concave polynomials Convex geometry and spectrahedra Applications in matrix analysis and statistical mechanics Her work spans theoretical advances in algebraic geometry and computational methods, including contributions to the study of principal minors, tropical geometry, and phase retrieval problems. Recent publications highlight her focus on Fourier quasicrystals, higher-rank numerical ranges, and combinatorial structures in matroids. Publications: Over 30 peer-reviewed articles, including influential works on quartic curves, determinantal representations, and log-concave polynomials. Grants & Collaborations: Active in interdisciplinary research, with projects supported by NSF and collaborations in algebraic combinatorics and geometric optimization.

• Combinatorics
• Matroid theory
• Tropical geometry
• +1 more

Dr. Felipe Rincón is a Senior Lecturer in Algebra at Queen Mary University of London, affiliated with the School of Mathematical Sciences and the Centre for Combinatorics, Algebra and Number Theory. He works at the intersection of combinatorics, tropical geometry, and algebraic geometry, focusing on matroid theory and its connections to tropical geometry. Research Interests Combinatorics Matroid theory Tropical geometry Algebraic geometry His research explores how combinatorial structures like matroids influence geometric and algebraic objects, particularly through tropicalization. Key topics include tropical ideals, moduli spaces, and applications to compressed sensing. Recent Publication Trends Dr. Rincón’s work bridges abstract combinatorics (e.g., matroid subdivisions, positroids) with applied fields (e.g., signal processing). He investigates tropicalization of algebraic varieties, CSM cycles, and the balance properties of tropical ideals. Advising and Grants He advises PhD students Benjamain Dobres, Samuel-Louis Gardiner, and Xiaoan Yang, and leads the EPSRC-funded project Matroids in tropical geometry (2021-2023, £210,270). Labs and Teams Dr. Rincón is a core member of Queen Mary’s Combinatorial Algebraic Geometry Research Group and the Centre for Combinatorics, Algebra and Number Theory, collaborating on international programs like the Barcelona CRM Intensive Research Program (2026).

Holden LeeView profile

Assistant Professor

• Machine Learning
• Probability
• Theoretical Computer Science
• +3 more

Holden Lee is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, where he joined in 2022. His research focuses on the theoretical foundations of machine learning, probability, and their intersections with theoretical computer science. He explores probabilistic methods in modern machine learning, including deep learning-based generative models and convergence guarantees for sampling algorithms like Markov Chain Monte Carlo. Prior to JHU, he was a postdoc at Duke University and a Simons Fellow at UC Berkeley. He holds a PhD in Mathematics from Princeton University and degrees from MIT and the University of Cambridge. Education: PhD in Mathematics, Princeton University, 2019 MASt in Pure Mathematics, University of Cambridge, 2014 BSc in Mathematics, MIT, 2013 Research Interests: Machine Learning Theory Probabilistic Sampling Methods Generative Models Statistical Learning Theory His work emphasizes theoretical rigor, particularly in understanding the success and limitations of deep learning algorithms and designing efficient sampling techniques beyond classical log-concave settings. Articles Trends: Lee’s recent publications (2022–2024) focus on advancing sampling algorithms for complex distributions, analyzing generative models, and improving convergence guarantees for methods like MCMC and score-based diffusion. His work bridges theory and practice, with applications in multimodal data, text generation, and dynamical systems. Awards: Simons Fellow at UC Berkeley (2021) Advising & Grants: Lee has contributed to projects at NeurIPS and collaborates on research in AI efficiency and theoretical guarantees. He teaches courses on probability and applied mathematics at JHU and Duke. Labs/Teams: His research group focuses on theoretical machine learning and probabilistic methods, with ongoing projects on scalable sampling algorithms and generative model analysis.

• Real Algebraic Geometry
• Combinatorics
• Convex Optimization
• +10 more

Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington, College of Arts and Sciences. Her research lies at the intersection of real algebraic geometry, combinatorics, and convex optimization, with a focus on polynomials, determinants, and matroids. Ph.D., Mathematics, UC Berkeley, 2011 B.A., Mathematics and Neuroscience, Oberlin College, 2007 Her research interests include real algebraic geometry, combinatorics, convex optimization, tropical geometry, and spectrahedra. She studies the algebraic and combinatorial structures underlying optimization problems and geometric objects, particularly through the lens of hyperbolic and log-concave polynomials. Her recent publications span topics such as tropicalization of principal minors, determinantal representations, Fourier quasicrystals, and log-concave polynomials. These works demonstrate strong interdisciplinary connections across algebraic geometry, combinatorics, optimization, and mathematical physics. Sloan Research Fellowship (2020) Best Paper Award, STOC (2019) von Neumann Fellowship, IAS (2020–2021) Bernard Friedman Prize, UC Berkeley (2011) Rebecca Cary Orr Prize, Oberlin College (2007) She has advised several Ph.D. students including Tracy Chin, Jonathan Niño-Cortes, Joseph Rogge, Faye Pasley Simon, Michael Ruddy, Georgy Scholten, and Abeer Al Ahmadieh. She has received significant NSF funding, including a CAREER award (2020–2025) on determinantal, hyperbolic, and log-concave polynomials. She has taught courses in tropical geometry, convex algebraic geometry, and optimization at both the University of Washington and North Carolina State University.

Jim HaglundView profile

Professor

• Algebraic Combinatorics
• Enumerative Combinatorics
• Symmetric Functions
• +2 more

Jim Haglund is Professor of Mathematics at the University of Pennsylvania, specializing in algebraic and enumerative combinatorics. His research explores symmetric functions, Macdonald polynomials, combinatorial statistics, rook theory, and polynomial root behavior. He directs the CAGE seminar and IPAC seminar series, fostering collaboration in combinatorics. Dr. Haglund's work connects combinatorics with representation theory and special functions, particularly through Macdonald polynomial operators and the Delta Conjecture. His research employs both theoretical frameworks and computational experimentation. Analysis of recent publications reveals consistent focus on combinatorial structures underlying symmetric functions, with innovations in delta operators, chromatic quasisymmetric functions, and rook theory generalizations. His work frequently bridges combinatorics with algebraic geometry and representation theory. Awards & Recognition: Fellow of the American Mathematical Society Editorial boards: Journal of Combinatorics and Involve Advising & Collaboration: Mentored 16 PhD students and numerous postdoctoral researchers. Leads combinatorial research group exploring connections between Macdonald theory, diagonal harmonics, and algebraic geometry.

• Combinatorics
• Algebraic Combinatorics
• Log-Concave Polynomials
• +13 more

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research lies at the intersection of combinatorics, optimization, and theoretical computer science, with a focus on log-concave and Lorentzian polynomials and their applications in discrete and continuous settings. Assistant Professor, University of Waterloo (2022–present) Dirichlet Postdoctoral Fellow, TU Berlin (2020–2022) Postdoctoral Fellow, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoctoral Fellow, KTH, Stockholm (Fall 2019) James H. Simons Fellow, Simons Institute, UC Berkeley (Spring 2019) His research explores the deep connections between algebraic structures and combinatorial phenomena, particularly through polynomial capacity and Lorentzian polynomials. He applies these tools to problems in optimization, sampling, and representation theory. His work often involves developing new algebraic and analytic techniques to tackle longstanding conjectures and algorithmic challenges. The recent publications highlight a consistent focus on Lorentzian polynomials, capacity bounds, and their applications in combinatorics, optimization, and theoretical computer science. Key themes include matroid theory, log-concavity, sampling algorithms, volume approximation, and connections to Lie theory and representation theory. The research spans both theoretical developments and algorithmic applications, often in collaboration with leading researchers in the field. Dirichlet Postdoctoral Fellowship, TU Berlin Postdoc Fellowship in Algebraic and Enumerative Combinatorics, Institut Mittag-Leffler James H. Simons Fellowship, Simons Institute, UC Berkeley Jonathan Leake has advised or collaborated with several researchers, though formal advisees are not listed in the provided text. His work has been supported by prestigious fellowships and collaborations with institutions such as the Simons Institute and TU Berlin. He has taught courses including CO 250: Introduction to Optimization, MATH 239: Introduction to Combinatorics, and CO 739: Lorentzian Polynomials at the University of Waterloo and TU Berlin. While specific lab or research group names are not mentioned, Leake's collaborative work with researchers like Petter Brändén, Nisheeth Vishnoi, and Leonid Gurvits suggests active participation in research teams focused on algebraic combinatorics, optimization, and theoretical computer science. His publicly shared code for sampling from HCIZ densities and verifying positivity in Lie-theoretic contexts indicates an active computational research component.

Bernd SturmfelsView profile

Professor

• Algebraic Geometry
• Combinatorics
• Commutative Algebra
• +4 more

Bernd Sturmfels is a leading mathematician serving as Director of the Max Planck Institute for Mathematics in the Sciences in Leipzig since 2017. He is also Professor Emeritus of Mathematics, Statistics, and Computer Science at the University of California, Berkeley, and holds honorary professorships at the Technical University of Berlin and the University of Leipzig. His research bridges pure and applied mathematics, with foundational contributions to algebraic geometry, combinatorics, and computational biology. Education: Sturmfels earned dual Ph.D. degrees in 1987 from the University of Washington and Technische Universität Darmstadt, followed by an honorary doctorate from Goethe University Frankfurt in 2015 and additional honorary doctorates from the University of Bern (2023) and the University of Chicago (2024). Research Interests: His work spans algebraic geometry , combinatorics , commutative algebra , algebraic statistics , convex optimization , and computational biology . He explores deep connections between abstract algebraic structures and practical applications in statistics, optimization, and the life sciences. Publications and Trends: With over 300 research articles and 11 books, his recent work (2022–2025) focuses on advanced topics like Grassmannian geometry, tropical implicitization, quantum chemistry applications, and algebraic statistics. His research increasingly integrates computational methods with theoretical insights, addressing problems in machine learning, phylogenetics, and optimization. Awards and Honors: Sturmfels has received numerous prestigious awards, including: George David Birkhoff Prize in Applied Mathematics (2018) SIAM von Neumann Lecturership (2010) Humboldt Senior Research Prize (2007–2008) David and Lucile Packard Fellowship (1992–1997) Fellowships of the AMS and SIAM Membership in the Berlin-Brandenburg Academy of Sciences and Humanities Mentoring and Grants: He has supervised 60 doctoral students and numerous postdocs, with many securing positions at leading institutions. His mentoring philosophy emphasizes diversity and excellence, as highlighted in his Notices of the AMS article. Funding sources include the NSF, DARPA, and the German National Science Foundation (DFG). Labs and Teams: At the Max Planck Institute, he leads the Nonlinear Algebra group, fostering interdisciplinary collaboration between mathematics and the sciences. His team focuses on developing algebraic methods for data analysis, optimization, and theoretical physics.

Arindam RoyView profile

Associate Professor

• Analytic Number Theory
• Algebraic Number Theory
• Special Functions
• +6 more

Arindam Roy is an Associate Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte. He serves as the Director of the Math and Stat Honors Program and is affiliated with research in analytic number theory, algebraic number theory, and graph theory. Current Position: Associate Professor, UNC Charlotte (2025–) Previous Positions: Assistant Professor, UNC Charlotte (2018–2025); G.C. Evans Instructor, Rice University (2015–2018) His research focuses on zeros of the Riemann zeta-function and L-functions, partial sums of zeta functions, divisor problems, integral transforms, and graph theory. Recent work explores value distributions, Turán inequalities, and connections to Ramanujan's theories. Key trends in his publications include analytic number theory, special functions, and the interplay between modular forms and graph theory. His articles often address critical line behavior, approximation techniques, and arithmetic functions.

• Algebraic Combinatorics
• Matroid Theory
• Polytopes
• +3 more

Luis Ferroni is a Tenure-track Assistant Professor in the Department of Mathematics at Università di Pisa since April 2025. He concurrently holds a Postdoctoral Member position at the Institute for Advanced Study (IAS) in Princeton, New Jersey, under the mentorship of June Huh. Previously, he was a Postdoctoral Researcher at KTH Royal Institute of Technology (2022–2024) and earned his PhD in Mathematics from the University of Bologna (2018–2021), advised by Luca Moci. His Licentiate Degree in Mathematics was obtained from the Universidad Nacional de Córdoba (2013–2018). His research focuses on Algebraic Combinatorics , Matroid Theory , Polytopes , and Ehrhart Theory . Notable contributions include work on Ehrhart positivity, Kazhdan-Lusztig polynomials of matroids, and the Merino-Welsh conjecture. His research bridges combinatorial geometry, algebraic structures, and discrete mathematics. Ferroni has been awarded prestigious honors such as the Premio INdAM-UMI-SIMAI 2023 (for his PhD thesis on matroid polytopes), the Premio Mario Baldassarri 2023 (for his article on matroid non-Ehrhart positivity), and the Michele Cuozzo Prize 2022 for his thesis. He is also recognized for academic excellence during his undergraduate studies, including multiple top rankings at the Universidad Nacional de Córdoba. His teaching experience spans roles as a Teaching Assistant (TA) and Tutor at institutions like the University of Bologna and KTH. He has taught courses such as Enumerative Combinatorics and Geometry. Ferroni actively participates in academic outreach, serving as a jury member for the CIMA Olympiad and coaching programming teams in international competitions like the ICPC World Finals. He has presented at leading venues including the Harvard/MIT Combinatorics Seminar, the Institute for Pure & Applied Mathematics (UCLA), and international conferences like the British Combinatorial Conference. His work appears in top journals such as Advances in Mathematics , SIAM Journal on Discrete Mathematics , and Combinatorial Theory .