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Richard HindView profile

Professor

• Differential Geometry
• Symplectic Geometry
• Complex Manifolds
• +3 more

Richard Hind is a Professor in the Department of Mathematics at the University of Notre Dame, part of the College of Science. He holds a B.A. from Cambridge University (1992) and a Ph.D. from Stanford University (1997). His research focuses on differential geometry, particularly complex and symplectic geometry, with an emphasis on the interplay between Riemannian metrics and canonical geometric structures. His work explores symplectic embeddings, Lagrangian submanifolds, and geometric rigidity, utilizing pseudoholomorphic curves as a key tool. Key research areas include symplectic packing problems, Stein manifolds, and the topology of symplectic manifolds. Recent work addresses symplectic barriers, packing stability, and geometric invariants of ellipsoids. His publications span journals like Geom. Funct. Anal., Duke Math. J., and Invent. Math., often collaborating with leading researchers in the field. Teaching includes MATH 10270: Mathematics in Architecture, linking geometric principles to historic structures. Professional roles include service on editorial boards and contributions to conferences. Office: 238 Hayes-Healy Bldg, Email: rhind@nd.edu and hind.1@nd.edu.

Betsy StovallView profile

Professor

• Harmonic Analysis
• Fourier Analysis
• Partial Differential Equations
• +2 more

Betsy Stovall is a Professor of Mathematics at the University of Wisconsin–Madison and holds the Letters and Science Mary Herman Rubenstein Professor chair. She serves as the AMS Associate Secretary for the Central Section . Education : Not explicitly stated in provided text. Appointments : Regular faculty at UW–Madison since at least 2012 Organizer of graduate analysis seminars Research Interests : Stovall specializes in harmonic analysis , focusing on operators involving curvature, oscillatory integrals, and Fourier restriction phenomena. Her work intersects with partial differential equations (PDEs) through the study of dispersive equations and geometric analysis problems. Teaching : Complex Analysis (Math 623) - Fall 2021 Calculus III (Math 234) - Fall 2020 Graduate Analysis Seminar - Spring 2022 Organized UW Madison undergraduate summer school in Analysis (2018) Scientific Contributions : Sole or joint author of 15+ publications NSF RTG grant in Analysis and PDE Active in harmonic analysis seminars and educational initiatives Administrative Roles : AMS Associate Secretary Co-organizer of RTG/Student seminars Summer school director

Hamsa BastaniView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Operations
• +5 more

Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.

• symmetric function theory
• algebraic combinatorics
• representation theory
• +3 more

Sami H. Assaf is a Gabilan Distinguished Professor of Science and Engineering and Professor of Mathematics at the University of Southern California. He currently serves as Director of Graduate Studies for the Department of Mathematics and was named a Dean's Leadership Fellow for Physical Sciences and Mathematics in 2024. His academic career spans from CLE Moore Instructor at MIT (2008-2011) through Assistant Professor (2012-2019), Associate Professor (2019-2022), to his current position as Professor (2022-present). Dr. Assaf earned his Ph.D. in Mathematics from the University of California, Berkeley in 2007 under Mark Haiman, with his dissertation titled "Dual equivalence graphs, ribbon tableaux and Macdonald polynomials." He completed his undergraduate studies at the University of Notre Dame in 2001, graduating summa cum laude with Honors in Mathematics and Philosophy. His research primarily focuses on symmetric function theory and its rich interplay with algebraic combinatorics, representation theory, and algebraic geometry. Dr. Assaf's recent publications center on polynomial generalizations of symmetric functions, including Schubert polynomials, Demazure characters, and nonsymmetric Macdonald polynomials. His work has established important connections between combinatorial structures and representation-theoretic objects, particularly through the development of dual equivalence and weak dual equivalence frameworks. Dr. Assaf's research has been consistently supported by multiple National Science Foundation grants (most recently DMS-2246785) and Simons Foundation Collaboration Grants (most recently Award 953878). His publication record shows remarkable productivity with over 40 papers in top mathematics journals since 2005, including numerous collaborations with graduate students and postdoctoral scholars. Among his honors are the Gabilan Distinguished Professorship (2023), multiple USC Mentoring Awards (2017, 2024), the Herb Alexander Prize for outstanding dissertation (2007), and both the National Science Foundation and National Defense Science and Engineering Graduate Research Fellowships. As an educator and mentor, Dr. Assaf has successfully guided numerous Ph.D. students to completion, including Henry Ehrhard, Grant Bowles, and Peter Kagey. He also founded and directs the Venice Math Circle, an innovative early math education program that uses creative approaches like dinosaur sorting and building blocks to teach deep mathematical concepts to children from Pre-K through 8th grade, emphasizing discovery and analytical thinking over rote memorization.

Christiana MavroyiakoumouView profile

Assistant Professor

• Fluid Dynamics
• Fluid-Structure Interactions
• Vortex Dynamics
• +6 more

Christiana Mavroyiakoumou is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. She specializes in fluid dynamics and fluid-structure interactions, with a focus on vortex dynamics, membrane flutter, and bio-inspired systems. Her research integrates modeling, numerical simulations, and experimental insights to study phenomena such as bird flock formations and fish swimming hydrodynamics. Mavroyiakoumou holds a Ph.D. from the University of Michigan (2022), an M.Sc. from the University of Oxford (2017), and a B.Sc. from Imperial College London (2016). Education: PhD in Applied & Interdisciplinary Mathematics, University of Michigan (2017–2022) MSc in Mathematical Modeling and Scientific Computing, University of Oxford (2016–2017) BSc in Mathematics, Imperial College London (2013–2016) Her research interests span fluid-structure interactions, vortex dynamics, and collective locomotion. She investigates how fluid flows mediate interactions between bodies, such as the aerodynamics of bird formations and the hydrodynamics of flapping foils. Her work bridges theoretical models with experimental observations, contributing to both fundamental science and bio-inspired engineering. Mavroyiakoumou has received prestigious awards including the Joseph B. Keller Fellowship (NYU), Peter Smereka Award (U-M), and ProQuest Distinguished Dissertation (U-M). She actively engages in academic service, organizing conferences and mentoring students. Her teaching experience includes courses on mathematical modeling, differential equations, and algebra at NYU and the University of Michigan. Key Research Themes: Flow-mediated collective behavior and instability mechanisms Vortex wake interactions and their role in locomotion Membrane dynamics in inviscid and viscous flows She collaborates with experimentalists like Leif Ristroph and Jun Zhang at NYU's Applied Math Lab, focusing on experimental validation of theoretical models. Her recent work explores self-amplifying waves in bird formations and the aerodynamic origins of flight coordination.

Benjamin GammageView profile

Research Fellow

• Geometry
• Algebra
• Mathematical physics
• +6 more

Benjamin Gammage is a Benjamin Peirce Fellow in the Department of Mathematics at Harvard University. Beginning in Summer 2025, he will transition to an Assistant Professor position at the University of Toronto. His academic journey includes a PhD from UC Berkeley under David Nadler, where his thesis focused on microlocal sheaves and mirror symmetry, and an undergraduate degree with honors from the University of Chicago. Dr. Gammage's research centers on quantum geometry, exploring the deep connections between geometry, algebra, and mathematical physics. His work particularly focuses on homological mirror symmetry and 3d mirror symmetry, investigating how quantum field theories reveal profound relationships between seemingly different geometric spaces. He has made significant contributions to understanding how the structure of SYZ fibrations mediates mirror symmetry and how perverse schobers can categorify aspects of geometric representation theory. His research has followed a clear trajectory from foundational work on mirror symmetry for affine hypersurfaces toward increasingly sophisticated structures in 3-dimensional field theories. The most recent publications demonstrate a progression from studying 1- and 2-dimensional quantum field theories toward fully categorified 3d mirror symmetry, revealing deep structural connections between representation theory and symplectic geometry. 2023-present: Principal Investigator, NSF DMS grant #2305257 2020: NSF Postdoctoral Research Fellowship 2019: Kenneth Ribet and Lisa Goldberg Award in Algebra 2015: NSF Graduate Research Fellowship 2014: Paul R. Cohen Prize Dr. Gammage has served as a minor thesis advisor for Grant Barkley (2022) and has been actively involved in mentoring through Harvard's Graduate Admissions Committee and as a Math Includes mentor. His research is supported by significant grant funding, including his current NSF grant. He has organized numerous seminars including the Open Neighborhood Seminar, Relative Langlands seminar, and various topics in mirror symmetry and string theory. His collaborative work spans multiple institutions, with frequent collaborations at institutions including the University of Toronto, UC Berkeley, and various international research centers. His research program bridges pure mathematics with theoretical physics, creating new frameworks for understanding geometric structures through quantum field theory perspectives.

Ying CaiView profile

Associate Professor

• AI
• Machine Learning
• Data Science
• +4 more

Ying Cai is an Associate Professor in the Department of Computer Science at Iowa State University, joining in 2003 after earning his Ph.D. in Computer Science from the University of Central Florida (2002). His research focuses on AI, machine learning, data science, cybersecurity, privacy protection, and database systems. He leads projects funded by the Air Force Research Laboratory, including work on authentication data structures for rank-aware queries, requiring U.S. citizenship and expertise in linear algebra/cryptography. Dr. Cai’s work spans cybersecurity (e.g., adversarial example defense, secure secret sharing), spatio-temporal systems (e.g., traffic risk prediction, check-in time modeling), and healthcare AI (e.g., cervical spine diagnosis with transformers). His publications emphasize practical applications of ML in privacy, security, and distributed systems. Professional roles include Associate Editor for Multimedia Tools and Applications (since 2009), Co-chair for COMPSAC TAIN/NCIW symposium (2014–2017), and TPC Chair for Mobilware 2010. His service includes contributions to INFOCOM, ICDCS, and MDM conferences. Current research opportunities exist for graduate students with strong programming/math skills, particularly in cryptography and linear algebra. He emphasizes interdisciplinary work, such as bridging AI with social sciences via large language models.

• Representation Theory
• Tensor Categories
• Lie Algebras
• +4 more

Pavel Etingof is Professor of Mathematics at the Massachusetts Institute of Technology (MIT), Department of Mathematics, where he has been a distinguished faculty member for many years. He serves as the Chief Research Adviser of MIT-PRIMES, an all-year high school math research program that provides exceptional research opportunities for talented high school students. Additionally, he holds the prestigious position of Editor-in-Chief of Selecta Mathematica. Professor Etingof's research spans multiple advanced areas of pure mathematics with a particular focus on representation theory, tensor categories, Lie algebras, Hecke algebras, and algebraic structures. His work consistently bridges algebra, geometry, and mathematical physics, revealing deep connections between abstract algebraic structures and physical phenomena. His research has evolved to increasingly explore tensor categories in positive characteristic, connections between representation theory and fractal structures, and applications to quantum field theory. His recent publications (2021-2025) demonstrate continued productivity and innovation, with numerous papers on tensor categories in various characteristics, representation theory of Lie groups, and connections to mathematical physics. These works show sophisticated exploration of representation theory in prime characteristic, novel applications to quantum field theory, and deep investigations into the structure of tensor categories. Editor-in-Chief of Selecta Mathematica Chief Research Adviser of MIT-PRIMES Professor Etingof has mentored numerous Ph.D. students at MIT and other institutions, establishing a significant mathematical genealogy in representation theory. His teaching includes advanced courses on algebraic groups, Lie theory, representation theory, and specialized topics. He has also co-organized many student seminars on cutting-edge mathematical topics including Deligne categories, symplectic reflection algebras, quantum cohomology, and double affine Hecke algebras, fostering collaborative research environments for students and colleagues.

Ann RivetView profile

Associate Professor

• Science Education
• Earth and Environmental Science Teaching and Learning
• Inquiry and Project-Based Science Learning
• +4 more

Ann Rivet is an Associate Professor of Science Education at Teachers College, Columbia University, and serves as the Associate Director of the Center for Sustainable Futures. She previously held the role of Program Officer in the Directorate for STEM Education at the National Science Foundation from 2016 to 2019. Her expertise lies in science education, particularly in Earth and environmental science, curriculum design, and urban school reform. Education: Ph.D. in Science Education, University of Michigan M.S. in Science Education (Earth Science), University of Michigan Sc.B. in Physics, Brown University Research Interests: Dr. Rivet's research focuses on how students develop deep understandings of Earth and environmental science concepts. She examines the intersections of scientific reasoning, instructional design, and assessment, primarily at the middle and secondary school levels. Her current work investigates how crosscutting scientific concepts influence students’ understanding of core disciplinary ideas, and how phenomena-based instructional approaches can support learning about large-scale Earth systems. She is actively involved in supporting the adoption and implementation of the Next Generation Science Standards (NGSS) through policy and practitioner-level initiatives, including the development of free, open-source curriculum materials for middle and high school science. Publications & Trends: Her work has been published in leading journals such as Science , the Journal of Research in Science Teaching , and the American Educational Research Journal . Her publications span topics from learning progressions in science to contextualizing instruction for urban students, reflecting a deep commitment to both theoretical and applied aspects of science education. Grants & Funding: Principal Investigator, NSF: "Collaborative Research: Bridging the Gap Between Tabletop Models and the Earth System" ($982,080) Science Team Member, GE Foundation: "Harlem Schools Partnership for Science and Math Education" ($5,000,000) Co-Principal Investigator, Carnegie Foundation: "Enhancing Teacher Preparation for Adolescent Literacy" ($100,000) Co-Principal Investigator, NSF: "GSE/RES Girls’ Science Practices in Urban High Poverty Communities" ($499,334) Principal Investigator, NSF (subaward): "Developing the Next Generation of Middle School Science Materials" ($137,198) Leadership & Outreach: Dr. Rivet has led multiple federally funded projects aimed at improving science education in urban schools. She has also contributed to national policy discussions through her role at the NSF and continues to shape science education standards and practices through her research and curriculum development efforts.

Carlos Fernandez-GrandaView profile

Associate Professor

• Machine Learning
• Medical AI
• Climate Science
• +7 more

Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications

Ruilin ShiView profile

Research Professor

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +3 more

Ruilin Shi serves as the William W. Elliott Assistant Research Professor in the Department of Mathematics at Duke University. Their office is located at 120 Science Drive, Durham, NC. Education: Ph.D. from Georgia Institute of Technology (2025) Dr. Shi specializes in combinatorial mathematics with particular focus on extremal graph theory and planar Turán problems . Their research investigates maximum edge counts in planar graphs avoiding specific substructures, particularly cycles of various lengths. This work bridges theoretical computer science and pure mathematics, contributing to fundamental understanding of graph limitations under planarity constraints. Recent publications demonstrate expertise in determining precise bounds for planar Turán numbers, with significant results for 7-cycles and general cycle graphs. Their work connects circuit graphs, near triangulations, and connectivity properties to solve longstanding conjectures in the field. No scientific awards are currently documented in the available information. For Fall 2025, Dr. Shi is teaching multiple sections of MATRICES AND VECTOR SPACES (MATH 218D and MATH 718D) across different time slots and locations including Physics 154, Physics 235, and LSRC A247. No laboratory or research team information is specified in the available documentation.

• Algebraic Geometry
• Hodge Theory
• Mathematical Physics
• +3 more

Matt Kerr is a Professor of Mathematics at Washington University in St. Louis, where he has been affiliated since 2010. He holds a doctorate in Mathematics from Princeton University (2003) and has held prior positions at UCLA, the Max Planck Institute, the University of Chicago, and Durham University. His research focuses on Algebraic Geometry, Hodge Theory, and Mathematical Physics, with notable contributions to period maps, normal functions, and the arithmetic of motives. He has been awarded the Guido L. Weiss Teaching and Service Award (2024), a Simons Foundation Travel Grant (2024-2029), and the Barry M. Goldwater Scholarship (1996-1997). Dr. Kerr has organized numerous conferences, including the Western Algebraic Geometry Symposium (2023), and co-edited volumes such as Period Domains, Algebraic Cycles, and Arithmetic . He currently supervises four Ph.D. students (Xiaojiang Cheng, RJ Acuna, Devin Akman, Rachel Wu) and has mentored postdoctoral researchers like Ivan Horozov and Patricio Gallardo. His teaching spans advanced graduate courses (e.g., Complex Analysis, Algebraic Geometry) and undergraduate programs, including contributions to the Washington University Math Circle and the National Alliance for Doctoral Studies mentorship initiative. His work bridges pure mathematics and theoretical physics, particularly through studies of Feynman integrals, Calabi-Yau varieties, and quantum curves. He is actively involved in academic service, including roles on hiring committees, grant reviewing, and mentoring programs. Recent research themes include regulators of algebraic cycles, singularities in Hodge theory, and compactifications of moduli spaces. Grants: NSF grants (2011–2024), FRG grants, Simons Foundation awards, and EPSRC funding. Awards: Multiple teaching and service recognitions, including the 2024 Weiss Award. Publications: Over 40 peer-reviewed articles and books, including collaborations with leading mathematicians like Phillip Griffiths and Spencer Bloch.

Nathan KaplanView profile

Professor

• Number Theory
• Algebraic Geometry
• Combinatorics
• +6 more

Nathan Kaplan is a Professor in the Department of Mathematics at the University of California, Irvine, where he conducts research in number theory, algebraic geometry, and combinatorics. His work spans rational points on varieties over finite fields, arithmetic statistics, coding theory, and the study of numerical semigroups. He is actively involved in the mathematical community, organizing seminars and conferences including the UC Irvine Number Theory Seminar and the Southern California Number Theory Day. Dr. Kaplan received his PhD from Harvard University in 2013 under the direction of Noam Elkies. Following his doctorate, he was a postdoctoral researcher at Yale University from 2013-2015 before joining the faculty at UC Irvine. His research interests focus on the intersection of number theory and algebraic geometry, with particular attention to problems involving rational points on varieties over finite fields, arithmetic statistics, and coding theory. He has made significant contributions to the study of numerical semigroups, cokernels of random p-adic and integer matrices, and quadratic forms and lattices. His work often bridges theoretical mathematics with applications in coding theory and cryptography. Analysis of his recent publications shows a strong trend toward combinatorial aspects of number theory, particularly in the study of numerical semigroups and their properties. He frequently collaborates with researchers across institutions, with recent work spanning algebraic geometry, combinatorics, and coding theory. His publications demonstrate expertise in both theoretical developments and computational aspects of number theory. Dr. Kaplan is deeply committed to undergraduate research and mentoring. He has experience as a mentor for undergraduate research projects through programs including SUMRY (a research program for Yale undergraduates), the University of Minnesota-Duluth REU program, and the Trinity University REU program. He actively encourages undergraduates to apply for summer research opportunities and has organized numerous outreach activities. He is an organizer of the UC Irvine Number Theory Seminar and the Southern California Number Theory Day conference series. In 2018, he co-organized the Conference on Open Questions in Cryptography and Number Theory in honor of Alice Silverberg's 60th Birthday. Dr. Kaplan has given numerous talks at mathematical venues including the Museum of Mathematics' Math Encounters series, where he presented "Error-Correcting Codes: The Mathematics of Communication" in July 2022. He has also spoken at the Yale Undergraduate Math Society, the UCI Math Circle, and various other outreach events.

Andrew LanView profile

Associate Professor

• Artificial Intelligence in Education
• Educational Data Mining
• Personalized Learning
• +5 more

Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.

David DamanikView profile

Professor

• Spectral Theory
• Dynamical Systems
• Aperiodic Order
• +3 more

David Damanik is the Robert L. Moody, Sr. Professor of Mathematics at Rice University, where he has established himself as a leading researcher in spectral theory, dynamical systems, and aperiodic order. His work bridges pure mathematics with mathematical physics, focusing on the spectral properties of operators arising in quantum mechanics and quasicrystal theory. Dr. Damanik received his academic training at Johann Wolfgang Goethe-Universität in Frankfurt, Germany, earning a Dipl.-Math. in 1995, Dipl.-Inform. in 1996, and Dr. phil. nat. in 1998. His educational background reflects a strong foundation in both mathematics and computer science, which informs his interdisciplinary research approach. His research interests center around spectral theory of Schrödinger operators, particularly those with ergodic, quasi-periodic, and aperiodic potentials. He has made significant contributions to understanding the spectral properties of operators associated with quasicrystals, substitution sequences, and other aperiodic structures. His work often connects spectral properties with dynamical systems concepts, particularly through the study of rotation numbers, Lyapunov exponents, and gap labeling theorems. Damanik's research has profound implications for understanding quantum transport in aperiodic media and the mathematical foundations of condensed matter physics. Analysis of his recent publications (2022-2024) reveals a continued focus on ergodic Schrödinger operators, with two comprehensive monographs providing a systematic treatment of the field. His work spans both theoretical foundations and specific applications, addressing problems in one-dimensional systems, quasi-periodic potentials, and aperiodic tilings. The research demonstrates strong connections between spectral theory, dynamical systems, and mathematical physics, with particular emphasis on the interplay between spectral properties and the underlying dynamics of the potential. Annales Henri Poincaré Prize (2014) for the paper "Continuum Schrödinger operators associated with aperiodic subshifts" Professor Damanik has mentored numerous PhD students and maintains an extensive network of collaborators across the globe, as evidenced by his long list of coauthors. His research has been supported by various grants that enable him to organize workshops and conferences, fostering collaboration in his field. He has been instrumental in organizing major conferences such as the Spectral Theory and Mathematical Physics conference honoring Barry Simon's 80th birthday (scheduled for 2026) and multiple workshops on aperiodic order at prestigious institutions like Banff International Research Station and Mathematisches Forschungsinstitut Oberwolfach. Through his teaching of specialized courses like "Mathematics of Aperiodic Order" and "Ergodic Theory and Topological Dynamics," Damanik has cultivated the next generation of researchers in his field. His leadership in organizing conferences and workshops has established him as a central figure in the international community studying spectral theory and aperiodic structures.