جست‌وجوی استاد و پژوهشگر

HanQin Caiمشاهده پروفایل

استادیار

• Mathematical Optimization
• Data Science
• Machine Learning
• +۵ مورد دیگر

Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.

Guifang Liمشاهده پروفایل

استاد

• Optical Communication
• Networking
• RF Photonics
• +۳ مورد دیگر

Guifang Li is a Professor of Optics and Electrical & Computer Engineering at the University of Central Florida (UCF), affiliated with CREOL, The College of Optics and Photonics. He holds the position of Editor-in-Chief of Advances in Optics and Photonics . His academic journey includes a Ph.D. from the University of Wisconsin-Madison and leadership roles such as Director of the NSF IGERT program in Optical Communications and Networking at UCF. Dr. Li's research focuses on optical communication and networking , RF photonics , and all-optical signal processing . His innovations include pioneering work on photonic computing architectures and high-capacity optical communication systems. He co-founded Optium, UCF's first venture startup, which became a public company (OPTM) in 2006 and later part of II-VI. His scientific contributions are recognized through prestigious awards, including the NSF CAREER Award, Office of Naval Research Young Investigator Award, and fellowships from IEEE, OSA, SPIE, and the National Academy of Inventors. He has advised over 20 Ph.D. students and leads a multidisciplinary research group involving postdoctoral scholars and graduate students. Recent research trends in his publications emphasize photonic computing (e.g., photonic matrix processors, floating-point arithmetic) and advanced optical systems (e.g., quantum cascade lasers, MPLC-based demultiplexers). His work bridges fundamental optics with practical applications in telecommunications and sensing. Labs/Teams: His research team specializes in optical communication systems, photonic integrated circuits, and computational optics.

Alex Gittensمشاهده پروفایل

استادیار

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +۳ مورد دیگر

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.

Jim Haglundمشاهده پروفایل

استاد

• Algebraic Combinatorics
• Enumerative Combinatorics
• Symmetric Functions
• +۲ مورد دیگر

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.

Harrison Huibin Zhouمشاهده پروفایل

استاد

• High-Dimensional Data Analysis
• Nonparametric Estimation
• Statistical Decision Theory
• +۳ مورد دیگر

Harrison Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University. He has held leadership roles, including Department Chair of Statistics and Data Science (2018–present) and former Chair of Statistics (2012–2017). His academic career at Yale spans over two decades, with promotions from Assistant Professor (2004–2009) to Associate (2009–2010) and full Professor (2010–present). Research Interests: Dr. Zhou specializes in high-dimensional statistical theory, including nonparametric estimation, minimax theory, and applications in network analysis, machine learning, and functional data analysis. His work bridges theoretical foundations with computational methods, addressing challenges in modern statistical decision-making. Publications: His recent work focuses on spectral clustering, quantum state tomography, and optimal estimation in high-dimensional models. Notable contributions include theoretical guarantees for algorithms like the EM method in Gaussian mixtures and advancements in community detection in networks. Teaching: He teaches advanced courses such as Functional Data Analysis, Nonparametric Estimation, and Decision Theory, reflecting his expertise in statistical methodology and theory. Professional Service: Organized workshops on topics like Empirical Processes (2015) and High-Dimensional Data (2012), underscoring his role in fostering academic collaboration.

Patricia Cahnمشاهده پروفایل

دانشیار

• Geometric Topology
• Low-Dimensional Topology
• Knot Theory
• +۲ مورد دیگر

Patricia Cahn is Associate Professor of Mathematical Sciences and Codirector of the Postbaccalaureate Program at Smith College. Her research focuses on geometric and low-dimensional topology, including trisections of 4-manifolds, branched coverings of 3- and 4-manifolds, and contact topology. Supported by an NSF CAREER grant. Education: Ph.D. and M.A. in Mathematics, Dartmouth College A.B. in Mathematics, Smith College Research explores: Algebraic invariants for topological intersections Knot theory under geometric constraints Branched coverings and dihedral invariants Computational methods in topology Publications demonstrate consistent focus on knot invariants and manifolds, with recent work on trisected branched covers (2023) and dihedral linking invariants (2021). Computational projects include algorithms for topological invariants. Grants: Currently funded by NSF CAREER award for research on branched covers in dimensions three and four. Teaching: Courses include Multivariable Calculus (MTH 212) and Topology (MTH 370) for 2024-2025. Laboratory: Leads computational topology projects with code repositories available on GitHub.

• Arithmetic Algebraic Geometry
• Algebraic Number Theory
• Modular Forms
• +۳ مورد دیگر

John Voight is a Professor in the School of Mathematics and Statistics at the University of Sydney, where he is also a member of the Algebra and Computational Algebra groups. His research focuses on arithmetic algebraic geometry, modular forms, and computational number theory. He collaborates with the Magma computational algebra system development team and contributes to databases like the LMFDB. Affiliations: University of Sydney (2024–present), Dartmouth College (2007–2024), University of Vermont (2007–2013). Key research interests include modular curves, Shimura varieties, elliptic curves, and abelian varieties. He explores algorithmic methods for computing with algebraic structures, such as quaternion algebras and ideal class groups. Publications span over 90 articles, including works on Hilbert modular forms, Belyi maps, and paramodular varieties. Notable achievements include the Selfridge Prize (2010 and 2018) and Simons Foundation grants. He advises numerous PhD students and has contributed to major projects like the LMFDB, advancing computational tools for number theory.

Jesse Wolfsonمشاهده پروفایل

دانشیار

• Geometry
• Topology
• Arithmetic Topology
• +۳ مورد دیگر

Jesse Wolfson is an Associate Professor and Vice-Chair for Inclusive Excellence in the Department of Mathematics at the University of California, Irvine. His research focuses on the interplay of arithmetic, geometry, and topology, with notable contributions to arithmetic topology, resolvent degree, and algebraic geometry. He co-directs the Southern California Geometry and Topology Center (SCGTC), an NSF Research Training Group (RTG) program actively recruiting graduate students in geometry and topology. He holds a PhD and has expertise spanning algebraic structures, homological algebra, and interdisciplinary applications such as fractals in music. His work bridges pure mathematics with educational outreach, including public lectures and collaborations with institutions like St. John’s College, Santa Fe. His email is wolfson@uci.edu , and he maintains an active research blog detailing his projects and collaborations. Wolfson’s research emphasizes foundational questions in mathematics, including Hilbert’s 13th problem and the epistemic role of proofs in AI. His recent talks and articles explore topics like prismatic cohomology, modular functions, and geometric gauge theories, reflecting his commitment to advancing both theoretical and applied mathematical frontiers.

Dora Erdosمشاهده پروفایل

مدرس ارشد

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +۳ مورد دیگر

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

Jonathan Novakمشاهده پروفایل

دانشیار

• Algebraic Combinatorics
• Special Functions
• Random Matrices
• +۳ مورد دیگر

Jonathan Novak is an Associate Professor of Mathematics at the University of California San Diego (UCSD). His research focuses on the combinatorial structure and high-dimensional behavior of multivariate special functions in random matrix theory, representation theory, and mathematical physics. He serves on the editorial board of the open-access journal Algebraic Combinatorics . Key research interests include algebraic combinatorics, special functions, and their intersections with random matrices and mathematical physics. His work explores combinatorial structures in high-dimensional spaces, asymptotic analysis of integrals, and connections between probability theory and algebraic structures. Recent publications emphasize topics like Berezin-Karpelevich integrals, quasimodular asymptotics, and topological expansions in matrix models. These studies highlight advancements in understanding complex systems through combinatorial and probabilistic lenses. No scientific awards are explicitly mentioned in the provided information. His contributions extend to editorial work and advancing interdisciplinary research in algebraic combinatorics and mathematical physics.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +۳ مورد دیگر

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

Jeffrey Dillerمشاهده پروفایل

استاد

• Complex Analysis
• Complex Dynamics
• Algebraic Geometry
• +۲ مورد دیگر

Jeffrey Diller is a Professor in the Department of Mathematics at the University of Notre Dame, specializing in complex analysis, complex dynamics, and algebraic geometry. Education B.A., University of Dayton, 1988 Ph.D., University of Michigan, 1993 Research Interests Diller studies multi-variable complex dynamics, using tools from pluripotential theory, complex algebraic geometry, and dynamical systems. His work centers on understanding the behavior of rational maps of two or more variables under iteration, particularly plane birational maps, and aims to extend this understanding to higher dimensions and non-invertible rational maps. Computer experimentation plays a crucial role in his investigations, aiding in the development of intuition and mathematical progress. Selected Publications Diller's recent publications span from 2001 to 2021, covering topics such as dynamics of birational and meromorphic maps, invariant forms, energy, and invariant measures in complex dynamics, and transcendental dynamical degrees. These works significantly advance the understanding of complex dynamical systems. Contact & Office Email: jdiller@nd.edu Office: 168B Hurley Building Phone: 574-631-7694

Christopher Schommer-Priesمشاهده پروفایل

دانشیار

• Topology
• Higher Category Theory
• Quantum Field Theory
• +۳ مورد دیگر

Christopher Schommer-Pries is an Associate Professor in the Department of Mathematics at the University of Notre Dame's College of Science. His research focuses on the intersection of topology, higher category theory, and quantum field theory, with notable contributions to classification of topological field theories and foundational work on higher categories. Education: Ph.D. in Mathematics (2009, UC Berkeley), B.S. in Mathematics (2003, Harvey Mudd College) Career: Taught at Notre Dame since 201?, previously at University of Bonn (2013) Research emphasizes dualizability in higher categories, TFTs' algebraic structures, and categorical unicity theorems. Key publications include foundational papers on tensor categories, invertible TFTs, and bordism classifications. Active in teaching advanced topology courses and mentoring graduate students, though no formal advisees listed here.

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +۷ مورد دیگر

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.

Bojan Popovمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Approximation Theory
• +۳ مورد دیگر

Bojan Popov is a Professor in the Department of Mathematics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on numerical analysis, nonlinear partial differential equations, and approximation theory, with a particular emphasis on invariant domain preserving schemes and hyperbolic conservation laws. He holds a Ph.D. from the University of South Carolina (1999) and an M.S. from the University of Sofia (1992). Popov has led or co-led numerous grants from agencies like NSF, DOD, and DOE, totaling over $30 million. He has advised four Ph.D. students and organized major conferences, including the 2007 'Approximation and Learning in High Dimensions' and the 2008 'Nonlinear Approximation Techniques Using L1'. His work bridges numerical methods with applications in fluid dynamics, materials science, and high-performance computing. Recent research includes invariant domain preserving techniques for hyperbolic systems, entropy viscosity methods, and robust finite element approximations. He teaches advanced courses such as Hyperbolic Conservation Laws (Math 638) and Linear Algebra (Math 304).