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Pamela E. HarrisView profile

Professor

• Algebraic Combinatorics
• Representation Theory
• Graph Theory
• +4 more

Pamela E. Harris is a Professor of Mathematics in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee. She is also a cohost of the podcast Mathematically Uncensored and President and co-founder of Lathisms: Latinxs and Hispanics in the Mathematical Sciences. Education: PhD in Mathematics (2012), University of Wisconsin-Milwaukee; M.S. in Mathematics (2008), University of Wisconsin-Milwaukee; B.S. in Mathematics (2005), Marquette University; Associate Degrees in Sciences and Liberal Arts (2003), Milwaukee Area Technical College. Her research focuses on algebraic combinatorics, particularly representation theory of Lie algebras, with applications to parking functions, graph theory, and discrete mathematics. She is a leading advocate for diversity, equity, and inclusion in mathematics, mentoring underrepresented students and co-organizing symposia for SACNAS and the American Mathematical Society. Dr. Harris has authored over 65 peer-reviewed articles and coauthored books such as Asked And Answered: Dialogues On Advocating For Students of Color in Mathematics and Practices and Policies: Advocating for Students of Color in Mathematics . Her work includes developing community agreements and DEI frameworks for academic settings. Scientific Awards: MAA Deborah and Franklin Tepper Haimo Award (2023), MAA Northeast Section Award (2020), MAA Henry L. Alder Award (2019), Council on Undergraduate Research Mentor Award (2019), Karen Uhlenbeck EDGE Fellow (2020). She actively engages in promoting community-building initiatives and has contributed to national dialogues on mentorship, equity, and the intersection of mathematics with social justice.

Wolf-Dieter RichterView profile

Professor

• Probability Theory
• Mathematical Statistics
• Multivariate Analysis
• +7 more

Wolf-Dieter Richter is a Professor of Probability Theory and Mathematical Statistics at the University of Rostock's Institute of Mathematics. His research focuses on advanced probability theory, statistical distributions, and geometric probability. He has supervised numerous PhD, Master's, and diploma students, with notable advisees including Jana Davids (1992), Uta Boldt (1993), and Klaus Müller (2017). His teaching includes courses on stochastic processes, multivariate statistical methods, and specialized seminars on topics like integral asymptotics and geometric statistics. Research interests: Geometric representations of distributions, generalized complex numbers, large deviation probabilities, and non-Euclidean statistical models. Supervised over 20 students in areas like statistical analysis, stochastic modeling, and geometric probability. Active in curriculum development for mathematics programs, including teacher training and applied statistics. Key contributions include work on (p₁,…,p_k)-spherical distributions, star-shaped distributions, and geometric interpretations of statistical concepts. His research bridges algebraic structures with probabilistic methods, often involving high-dimensional and non-Euclidean frameworks. Recent articles explore complex numbers in higher dimensions, hyperbolic number systems, and advanced distribution theory. He has pioneered methods for analyzing skewed elliptically contoured distributions and their geometric properties.

Benjamin BraunView profile

Professor

• Algebraic Combinatorics
• Topological Combinatorics
• Geometric Combinatorics
• +3 more

Benjamin Braun is a Professor of Mathematics at the University of Kentucky, serving as the Director of Graduate Studies in the Department of Mathematics (College of Arts & Sciences). His research focuses on geometric, algebraic, and topological combinatorics, with particular emphasis on polytopes, integer points in polyhedra, and combinatorial algebra. He also investigates interdisciplinary topics such as active learning, mathematics education, and the integration of critical theory (including feminist and reader-response perspectives) into STEM disciplines. His work bridges pure and applied mathematics, addressing areas like Ehrhart theory, flow polytopes, and social justice in education. He co-organizes the University of Kentucky Mathematics Community and Ethics Working Group, fostering dialogue on ethics and societal impacts in mathematical research. Braun’s contributions span over 50 peer-reviewed articles, with recent work emphasizing the intersection of mathematics with pedagogy and equity. Research interests include: Algebraic and topological combinatorics Mathematics education reform Interdisciplinary applications of combinatorics Notable research trends reflect his dual focus on foundational mathematics and educational innovation. Articles from 2023–2025 highlight studies on Ehrhart polynomials, flow polytopes, and the role of identity and belief systems in student success. His scholarship of teaching and learning emphasizes student-centered assessment and institutional support structures. Braun’s professional service includes leadership in graduate education and curriculum development. His website ( braunmath ) provides access to research, teaching materials, and collaborative projects.

• Theoretical Computer Science
• Algorithm Design
• Online Algorithms
• +3 more

Dr. Denis Pankratov is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on theoretical computer science, including algorithm design, complexity theory, and information theory, with an emphasis on bridging the gap between theoretical guarantees and practical algorithm performance. He has taught undergraduate and graduate courses in algorithm design, probability theory, and online algorithms, often for large classes (80–150 students). His mentoring includes supervising postdocs, Masters and PhD students, and undergraduate researchers. Education: Bachelors from the University of Toronto, PhD and Master’s from the University of Chicago, followed by a postdoc at the University of Toronto before joining Concordia. Professional experience includes roles at Toyota Technological Institute at Chicago, Google Inc., and Zurich Insurance Company. Research interests span algorithm design, online algorithms, communication complexity, and proof complexity. His work often employs an information-theoretic lens to understand algorithmic behavior beyond worst-case scenarios. Hobbies include programming contests (e.g., ICPC-style), woodworking, and 3D printing. For a full list of publications, visit his Google Scholar profile . Teaching and professional activities emphasize research, mentoring, and service to the academic community.

Wenjia JingView profile

Associate Professor

• Analysis
• Partial Differential Equations
• Stochastic Homogenization
• +3 more

Wenjia Jing is an Associate Professor at the Yau Mathematical Sciences Center, Tsinghua University in Beijing, China. He joined Tsinghua University as an Assistant Professor in 2016 and was promoted to Associate Professor in 2022. He also serves as an Adjunct faculty at the Yanqi Lake Beijing Institute of Mathematical Sciences and Applications since July 2021. His academic journey began with a B.S. in Theoretical and Applied Mechanics from Peking University in 2006, followed by a Ph.D. in Applied Mathematics from Columbia University in 2011 under the supervision of Guillaume Bal. His postdoctoral training included positions as an L.E. Dickson Instructor at the University of Chicago (2013-2016) mentored by Panagiotis E. Souganidis, and as a Postdoctoral Researcher at Ecole Normale Supérieure Paris (2011-2013) mentored by Habib Ammari and Josselin Garnier. Dr. Jing's research focuses on Analysis and Partial Differential Equations, with specific expertise in Stochastic Homogenization and Quantitative Estimates, Waves Propagation in Random Media, and other areas of Applied Analysis. His work bridges theoretical mathematics with applications in physics and engineering, particularly in understanding complex phenomena in heterogeneous and random media. His research has been supported by multiple grants including NSFC (China) grants and The Recruitment Program of Global Experts of China. Dr. Jing has made significant contributions to the field through his publications in top mathematical journals. His work shows a consistent focus on homogenization theory, partial differential equations in complex media, and quantitative analysis of mathematical models. He has developed novel approaches to understanding wave propagation, front dynamics, and effective properties in heterogeneous media. NSFC (China) Grant No. 11871300 (co PI, 2019-2022) The Recruitment Program of Global Experts of China (2019-2021) NSFC (China) Grant No. 11701314 (PI, 2018-2020) NSF (USA) Grant DMS-1515150 (PI, 2015-2016) Dr. Jing is also active in academic service, serving as an Associate editor for Frontiers of Applied Mathematics by AIMS. He has taught a variety of courses at both undergraduate and graduate levels, including Real Analysis, Linear Algebra, and specialized topics courses on homogenization theory and waves in random media.

Elisabeth WernerView profile

Professor

• Convex Geometry
• Analysis
• Probability
• +3 more

Elisabeth Werner is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University (CWRU), located in Cleveland, Ohio. Her primary affiliations include the College of Arts and Sciences and the Department of Mathematics. She holds an office at 2145 Adelbert Rd, Room 102C, and can be reached via elisabeth.werner@case.edu or 216.368.2901. Her research focuses on convex geometry, analysis, and probability, with applications to approximation theory, mathematical physics, and quantum information theory. Key areas include the study of floating bodies, geometric inequalities, and high-dimensional probabilistic structures. Recent work explores topics like Lp-affine surface areas, spherical convex hulls, and the interplay between convex geometry and functional analysis. Her publications from 2023–2025 highlight advancements in floating body theory, curvature functionals, and stochastic geometric models. Notable trends include investigations into isoperimetric inequalities, entropy theory, and the geometric properties of convex bodies in high dimensions. Despite extensive contributions to the field, no scientific awards are explicitly mentioned in the provided materials. No advisees or grant details are listed, though her research often intersects with quantum information theory and mathematical physics. She maintains a departmental website at case.edu/artsci/math/werner , which likely contains additional professional details.

Alexander LitvakView profile

Professor

• Convex Geometry
• Random Matrix Theory
• Probability Theory
• +3 more

Alexander Litvak is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on convex geometry, random matrix theory, and probability theory with applications to high-dimensional geometry and stochastic processes. Education: He holds a Ph.D. from Tel Aviv University and an M.Sc. from Saint Petersburg State University. Research Interests: Litvak explores geometric probability, random structures, and their applications in optimization and functional analysis. His work often addresses questions related to convex bodies, matrix singularity, and the behavior of high-dimensional random systems. Key topics include minimal dispersion, random polytopes, and spectral properties of random graphs. Publications: His recent work (2020-2024) emphasizes probabilistic methods in convex geometry and random matrix theory, with notable contributions to linear bandits, sparse matrix analysis, and the circular law for digraphs. These studies reveal trends in understanding high-dimensional phenomena and probabilistic structures. Grants and Awards: No specific grants or awards listed in the provided materials. Advising and Labs: No student advisees or lab affiliations explicitly mentioned in the text.

Eliza O’ReillyView profile

Assistant Professor

• Mathematics of Data Science
• Stochastic and Convex Geometry
• Point Processes
• +4 more

Eliza O’Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University (JHU) and a member of the Data Science and AI Institute. Her research focuses on the mathematical foundations of data science, combining stochastic and convex geometry, high-dimensional probability, and statistical learning theory. She develops geometric models and algorithms for analyzing complex data structures, with applications in machine learning, optimization, and high-dimensional statistics. Dr. O’Reilly holds a B.S. in Mathematics from the University of Pittsburgh (2013) and a Ph.D. in Mathematics from the University of Texas at Austin (2019), where she was advised by François Baccelli. She completed a postdoctoral fellowship at the California Institute of Technology (2019–2022), sponsored by Venkat Chandrasekaran and Joel Tropp. Her work has been supported by NSF fellowships, including a NSF Graduate Research Fellowship and a NSF Postdoctoral Research Fellowship. Her research interests include: Randomized partitioning algorithms (e.g., Mondrian forests) Regularization in statistical inference and inverse problems Convex and nonconvex optimization methods Determinantal point processes and repulsive models High-dimensional geometry of random convex sets Her publications span theoretical guarantees for machine learning algorithms, optimization frameworks for data-driven problems, and geometric analysis of stochastic processes. Notable contributions include minimax analyses for random tessellation forests and spectral methods for convex regression. She actively collaborates with researchers in applied mathematics, statistics, and computer science.

Marni MishnaView profile

Professor

• Analytic Combinatorics
• Enumerative Combinatorics
• Algorithmic Solutions
• +5 more

Marni Mishna is a Full Professor in the Department of Mathematics at Simon Fraser University’s Faculty of Science. Her academic roles include Associate Dean, Equity, Diversity, and Inclusion (2023–2028) and PIMS Deputy Director (2019–2021). She has held visiting positions at institutions such as CNRS (France), LaBRI (University of Bordeaux), and the Fields Institute. Education: PhD in Mathematics (Université du Québec à Montréal, 2003), MSc in Pure Math (SFU, 2000), BMath (University of Waterloo, 1997). Her research spans analytic and enumerative combinatorics , focusing on generating functions , lattice paths , D-finite symmetric functions , and algorithmic solutions . She explores the intersection of combinatorics with art and design for mathematical communication. Recent articles analyze differential transcendence , Kronecker coefficients , and lattice path universality classes , emphasizing computational methods and asymptotic analysis. Her work often bridges graph theory , symmetric functions , and algebraic structures . She supervises students and postdocs in projects related to combinatorial theory , graph enumeration , and symmetric function applications . Her teaching includes advanced courses like MATH 443/743 (Combinatorics) and MATH 322 (Complex Analysis).

• Algorithmic Algebra
• Computational Complexity
• Invariant Theory
• +4 more

Prof. Peter Bürgisser is a Professor in the Institute of Mathematics at Technische Universität Berlin, leading the Research Group on Algorithmic Algebra. His work focuses on computational complexity, invariant theory, algebraic geometry, and probabilistic methods in mathematics. He holds a prominent role in advancing theoretical computer science and algorithmic algebra through his research and publications. Research interests include Algorithmic Algebra, Computational Complexity, Invariant Theory, Optimization, and Probability Theory. His contributions span foundational work in geometric complexity theory, probabilistic algebraic geometry, and efficient algorithms for algebraic problems. Publications highlight his expertise in topics like random fewnomials, nonarchimedean geometry, and polynomial time algorithms for invariant theory. His work is published in top journals such as Journal für die reine und angewandte Mathematik and Foundations of Computational Mathematics . Prof. Bürgisser is actively involved in academic activities, including teaching and advising, though specific student names are not listed in the provided texts. His research has been supported by grants and collaborations with institutions worldwide.

Rad NiazadehView profile

Associate Professor

• Online Algorithms
• Algorithmic Mechanism Design
• Data-Driven Decision Making
• +4 more

Rad Niazadeh is an Associate Professor of Operations Management at the University of Chicago Booth School of Business and holds a courtesy appointment at the Toyota Technological Institute at Chicago (TTIC) . He previously served as a visiting researcher at Google Research NYC and a Motwani postdoctoral fellow at Stanford University's Computer Science Department. He earned his PhD in Computer Science (with a minor in Applied Mathematics) from Cornell University. Research Interests: Focus on data-driven online decision-making and algorithmic mechanism design for platforms and non-profit operations. Key areas include fair resource allocation, dynamic matching algorithms, and socially-aware AI. Recent Publications: 15+ works on topics spanning online algorithms (e.g., refugee resettlement, reusable resource matching), fairness in AI (e.g., equity-constrained search), and market design (e.g., pricing with cancellations, combinatorial Bernoulli factories). Scientific Awards: Recipient of the Asness Junior Faculty Fellowship (2023), multiple INFORMS Rothkopf Junior Researcher Prizes , and IJCAI Distinguished Paper Award (2024). His co-authored works have won best paper honors at INFORMS and MSOM. Advising: Mentoring PhD students Mohammad Reza Aminian (awarded INFORMS Service Science Best Student Paper), Soonbong Lee (INFORMS AMD First Place), and Yueyang Zhong . Collaborates with principal researchers Yiding Feng and Pranav Nuti .

• Algorithms
• Counting and Sampling
• Random Structures
• +7 more

Mary Cryan is a Senior Lecturer in the School of Informatics at the University of Edinburgh, where she has been a faculty member since July 2003. She is affiliated with the Laboratory for Foundations of Computer Science (LFCS) and conducts research in theoretical computer science, with a focus on algorithms, counting and sampling, random structures, learning theory, and pseudorandom generators. Her educational background includes a joint undergraduate degree in Computer Science and Mathematics from University College Dublin (1993), an MSc by research at UCD (1995), and a PhD from the University of Warwick (awarded 2000) under the supervision of Leslie Ann Goldberg. She held postdoctoral positions at BRICS, University of Aarhus (1999–2001), and the University of Leeds (2001–2003) working with Martin Dyer. PhD, University of Warwick (2000) MSc, University College Dublin (1995) BSc (Joint Hons), Computer Science and Mathematics, University College Dublin (1993) Her research interests lie in the design and analysis of algorithms, particularly randomized algorithms for counting and sampling combinatorial structures. She has made significant contributions to the understanding of Markov chains for sampling matchings, contingency tables, Euler tours, and phylogenetic trees. Her work often bridges theoretical computer science with probability and discrete mathematics. The trend in her publications shows a sustained focus on fundamental algorithmic problems involving random structures and probabilistic methods. Her work spans exact and approximate counting, mixing times of Markov chains, and learning models in phylogenetics. Recent work includes analyzing log-Sobolev inequalities for log-concave distributions and exact counting on bounded-treewidth graphs. She has supervised several PhD students, including Veselin Blagoev ("Counting and Sampling Acyclic Orientations", 2018), Páidí Creed ("Counting and Sampling problems on Eulerian graphs", 2010), and James Matthews (co-supervised, "Markov chains for sampling matchings", 2008). She has also been active in academic service, serving as Deputy Director of the Informatics Graduate School since August 2020. Her research has been published in top-tier venues such as FOCS, STOC, SIAM Journal on Computing, and Journal of the ACM. She regularly lectures courses such as "Informatics 2 - Introduction to Algorithms and Data Structures".

Heng GuoView profile

Associate Professor

• Algorithms
• Computational Complexity
• Approximate Counting
• +5 more

Heng Guo is an Associate Professor in Algorithms and Complexity at the School of Informatics, University of Edinburgh. He leads the ERC starting grant project New Approaches to Counting and Sampling (NACS), which runs from 2021 to 2026. Previously, he has worked and studied at Berkeley, London, Madison, and Beijing. His research lies at the intersection of theoretical computer science, combinatorics, and statistical physics. Guo's research focuses on algorithms from a complexity perspective, particularly computational counting and sampling. He is renowned for his work on the Lovász local lemma, Markov chain Monte Carlo methods, phase transitions in computational complexity, and complexity classifications. His approach often involves discovering unseen links between different areas of theoretical computer science. Key contributions include confirming a conjecture of Gorodezky and Pak through partial rejection sampling, establishing rapid mixing for Swendsen-Wang dynamics, and developing a polynomial-time approximation algorithm for all-terminal network reliability. His publication record shows a strong trajectory of impactful research in top venues including FOCS, STOC, SODA, J. ACM, and SIAM Journal on Computing. His work on the all-terminal network reliability problem won the Best Paper Award at ICALP 2018. Guo has organized several significant workshops including JerrumFest 2025, MCMC 2.0 (Shonan seminar), and a STOC 2020 workshop on new frontiers of approximate counting. These events highlight his leadership role in the theoretical computer science community. Best Paper Award at ICALP 2018 EATCS Distinguished Dissertation Award 2016 Guo has advised several PhD students including Giorgos Mousa, Jiaheng Wang, and Graham Freifeld, and mentored postdocs such as Weiming Feng, Vishvajeet Nagargoje, and Konrad Anand. His ERC grant supports multiple research associates working on counting and sampling problems. He has taught courses including Computational Complexity, Randomness and Computation, and Algorithmic Game Theory at the University of Edinburgh.

Felix KrahmerView profile

Associate Professor

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +7 more

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

Károly Böröczky Jr.View profile

Research Professor

• Lattice points in large bodies
• Minkowski type Monge-Ampere equations
• Logarithmic Brunn-Minkowski conjecture
• +9 more

Károly Böröczky Jr. is a Research Professor at the Alfréd Rényi Institute of Mathematics (Department of Geometry). His work spans convex geometry, discrete geometry, and geometric analysis, with a focus on inequalities like the Brunn-Minkowski and Prekopa-Leindler theorems, logarithmic curvature problems, and approximation of convex bodies. Research Grants : Logarithmic-Brunn-Minkowski conjecture (2019.12.01 - 2023.11.30) Stochastic and analytical geometry (2018.09.01 - 2020.08.31) Evaluations on lattice polytopes (2016.01.01 - 2019.12.31) Collaborations : Co-edited volumes honoring Ted Bisztriczky, Gabor Fejes Tóth, and Endre Makai (2018), and contributed to New Trends in Intuitive Geometry (Springer, 2018). Scientific Awards : 2015 Alfred Rényi Award 2005 János Bolyai Research Scholarship 2004 Pál Erdős Award Contact : carlos@renyi.hu , boroczky.karoly.j@renyi.hu