جست‌وجوی استاد و پژوهشگر

• Mathematics
• Coding Theory
• Combinatorics
• +۳ مورد دیگر

Leslie Hatfield is a Professor of Mathematics at the University of Lynchburg, affiliated with the School of Liberal Arts & Sciences. They hold a PhD in Mathematics from the University of Virginia (2003), an MS in Mathematics from the same institution (2001), and a BS in Mathematics from Virginia Polytechnic Institute and State College (1996). Education: PhD in Mathematics, University of Virginia, 2003 MS in Mathematics, University of Virginia, 2001 BS in Mathematics, Virginia Tech, 1996 Research Interests: Focus on coding theory, combinatorics, and finite geometry, with applications in algebra and bioinformatics. Recent work explores dual codes of projective planes and voting theory. Active in presenting accessible mathematics to students and the public through activities like mathematical origami and voting system analyses. Awards: Shirley Rosser Excellence in Teaching Award Nominee (2005) Graduate Teaching Assistant of the Year (2003) Professional Engagement: Liaison to the Mathematical Association of America (2003–present), member of the American Mathematical Society (1998–2006). Active in organizing academic conferences and colloquia, including the SUMS Conference and Lynchburg Science Gang events.

Robert B. Ellisمشاهده پروفایل

دانشیار

• Combinatorics
• Graph Theory
• Adaptive Coding Theory
• +۳ مورد دیگر

Robert B. Ellis is an Associate Professor of Applied Mathematics at Illinois Institute of Technology (IIT), serving as Program Director for the Bachelor of Science in Data Science. His academic journey includes a Ph.D. in Mathematics from the University of California, San Diego. Ellis specializes in combinatorics, graph theory, adaptive coding theory, and probabilistic methods, with notable contributions to random geometric graphs and liar games. He has held roles like Project NExT Fellow and received awards such as the 2023 Michael J. Graff Award for Innovation in Teaching and IIT IPRO Outstanding Faculty Award. Education: Ph.D. in Mathematics, University of California, San Diego Research Interests: Focuses on asymptotic analysis of codes, liar games' connections to packing/covering problems, random geometric graphs in wireless networks, probabilistic methods, and algorithm design. His work bridges theoretical mathematics with applications in data science and network systems. Publications & Grants: Over 20 peer-reviewed articles in journals like Journal of Combinatorics and Algorithmica . Secured grants from NSA Young Investigator and AFRL programs. Advised graduate students on topics like graph partitioning and group testing matrices. Awards: 2023 Michael J. Graff Award for Innovation in Teaching IIT IPRO Outstanding Faculty Award (2009) Project NExT Fellow (2005-2006) Teaching: Courses include Calculus II, Discrete Mathematics, and Applied Algebra. Emphasizes innovative pedagogy through Menger Day talks and IPRO interdisciplinary projects.

Albert Victorمشاهده پروفایل

استادیار مدعو

• Quantum Information Science
• Quantum Error Correction
• Theoretical Physics
• +۳ مورد دیگر

Albert Victor is an Adjunct Assistant Professor in the Department of Physics and the Institute for Advanced Computer Studies at the University of Maryland College Park, and a Physicist at the National Institute of Standards and Technology (NIST). He holds a Ph.D. in Physics from Yale University (2017) and was a postdoctoral researcher at Caltech. His research focuses on quantum information science, including quantum error correction, bosonic coding, quantum metrology, and theoretical aspects of open quantum systems. Notably, he has contributed to Lindbladian dynamics with multiple steady states, molecular rotational qubits, and concatenated quantum codes. Education: B.S. in Physics from University of Florida, Ph.D. in Physics from Yale University (2017). Postdoctoral work at Caltech (2017-2019). Research interests emphasize leveraging quantum systems for fault-tolerant computing, exploring non-Hermitian quantum dynamics, and developing novel error-correcting codes for continuous-variable systems. His work bridges abstract theoretical frameworks with experimental realizations in superconducting circuits and molecular physics. Publications span high-impact topics like transversal quantum gates, quantum metrology bounds, and molecular-based quantum encodings. He contributes actively to the Quantum Information Group at NIST and collaborates with institutions globally. Awards: While no named prizes are documented, his impactful contributions reflect recognition in the field through numerous citations and research grants. Labs/Teams: Affiliated with the Joint Quantum Institute (JQI), QuICS (Quantum Information and Computer Science), and NIST's Quantum Computing Division.

• Algebraic Geometry
• Combinatorics
• Coding Theory
• +۴ مورد دیگر

Lei Chen is a researcher in the Faculty of Mathematics at Bielefeld University, Germany. He is actively involved in the Collaborative Research Centre / Transregio 358 titled Integral Structures in Geometry and Representation Theory , specifically contributing to subproject A3: Codes and designs . His work is supported by the German Research Foundation (DFG), indicating a strong research profile in fundamental mathematics. His research focuses on deep theoretical structures in mathematics, particularly at the intersection of combinatorics, algebra, and geometry. Key areas include coding theory , combinatorial design theory , and representation-theoretic methods in discrete structures. These fields are central to modern pure mathematics and have applications in information theory and cryptography. Lei Chen is based at the university’s main campus in office UHG M5-120 and can be reached via phone at +49 521 106-67541. His email is not publicly visible due to access restrictions on the university directory, but likely follows the institutional format. He collaborates within a large interdisciplinary DFG-funded consortium, suggesting active engagement in the international mathematical research community. Research Project: SFB/Transregio 358 – A3: Codes and designs Funding Source: German Research Foundation (DFG)

• Algebra
• Geometry
• Representation Theory
• +۵ مورد دیگر

Sebastian Degen is a researcher and doctoral student at the Faculty of Mathematics, University of Bielefeld. He is actively involved in the Collaborative Research Centre / Transregio 358 "Integral Structures in Geometry and Representation Theory", specifically working on Project A7: Matroids, codes and their q analogs. He is also a member of the Bielefeld Graduate School in Theoretical Sciences. Institution: University of Bielefeld School: Faculty of Mathematics Email: sdegen@math.uni-bielefeld.de Office: UHG V4-204 Phone: +49 521 106-4986 His research centers on advanced mathematical structures, particularly in algebra and combinatorics, with applications to coding theory and geometry. His work lies at the intersection of discrete mathematics and algebraic structures, contributing to foundational theoretical developments. Sebastian is engaged in high-level theoretical research within structured collaborative projects funded by the German Research Foundation (DFG), indicating strong integration into the national research landscape. He is supervised or collaborating within a well-established research network focused on integral structures in pure mathematics. He is affiliated with significant research initiatives such as SFB/TRR 358 and contributes to the academic environment of the Bielefeld Graduate School in Theoretical Sciences, supporting interdisciplinary and deep theoretical inquiry.

Sourav Chakrabortyمشاهده پروفایل

استاد

• Theoretical Computer Science
• Boolean Functions
• Quantum Complexity
• +۴ مورد دیگر

Sourav Chakraborty is a Professor in the Advanced Computing and Microelectronics Unit (ACMU) of the Computer and Communication Sciences Division at the Indian Statistical Institute (ISI), Kolkata, India. He joined ISI in July 2018 after serving as faculty at Chennai Mathematical Institute from 2010-2018. Previously, he held postdoctoral positions at Centrum Wiskunde & Informatica (CWI) in Amsterdam and Technion in Israel. Education: Ph.D. in Computer Science, University of Chicago (2008) M.S. in Computer Science, University of Chicago (2005) B.Sc. in Mathematics, Chennai Mathematical Institute (2003) Research Focus: Professor Chakraborty specializes in Theoretical Computer Science with emphasis on classical and quantum complexity of Boolean functions, including sensitivity analysis, property testing, and quantum database search. His work extends to graph algorithms, electronic commerce mechanisms, and coding theory. His research explores fundamental questions in computational complexity through innovative mathematical frameworks. Publication Trends: Recent work demonstrates a strong focus on property testing, sampling algorithms, and complexity theory, with significant contributions to streaming algorithms, Boolean function analysis, and quantum query complexity. His publications frequently appear in top theoretical computer science venues and exhibit consistent innovation in algorithm design and complexity boundaries. Awards & Honors: Praise from Donald E. Knuth for streaming algorithms research Inclusion in Oded Goldreich's 'my choices' list for Conditional Sampling and Huge-Object Model work Chakraborty's function named in his honor for Sensitivity Conjecture contributions Academic Service: Teaches courses in discrete mathematics and theoretical computer science, with detailed course materials available through institutional pages. Organized workshops including the 2020 Workshop on Sensitivity and Query Complexity at ISI.

Omar Rivasplataمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +۶ مورد دیگر

Omar Rivasplata is a Senior Lecturer in Machine Learning and Robotics, specializing in theoretical and applied machine learning. He is affiliated with the MCAIF: Centre for AI Fundamentals, focusing on advancing foundational aspects of artificial intelligence. His research spans reinforcement learning, Bayesian analysis, neural networks, and generalization bounds. Key research interests include the theoretical underpinnings of deep learning architectures, optimization strategies for large-scale models, and probabilistic methods in machine learning. Notable contributions include work on gradient clipping for wide/deep networks and semi-pessimistic reinforcement learning frameworks. Leading the MCAIF project (2021–2026), a multidisciplinary initiative exploring core AI principles. Active collaborations with institutions worldwide, emphasizing open-access publications (e.g., Transactions on Machine Learning Research). His articles explore topics like PAC-Bayesian theory, convergence of diffusion models, and meta-analyses of Bayesian methods. He advises a diverse cohort of postgraduate researchers in AI fundamentals.

• Applied Mathematics
• Pure Mathematics
• Algebraic Geometry
• +۳ مورد دیگر

Assoc Prof Chee Whye Chin is an Associate Professor of Mathematics at the National University of Singapore (NUS), affiliated with the Department of Mathematics. His research focuses on arithmetic algebraic geometry, monodromy groups, and representation theory of reductive groups. He has contributed to foundational work on ℓ-independence of monodromy groups and compatible systems of lisse sheaves. His teaching philosophy emphasizes student-driven learning through interactive lectures and continual assessments, aiming to foster deep understanding of mathematical concepts. Research Interests: Monodromy groups and their structural properties Reductive algebraic groups and their representations ℓ-Independence conjectures in arithmetic geometry Applications of group theory in algebraic geometry Publications: His work spans algebraic geometry, number theory, and computational methods. Notable contributions include studies on rationality of homogeneous varieties, optimization of matrix multiplication algorithms, and independence of ℓ results in compatible systems. Over 15 key publications since 1997 reflect his interdisciplinary impact. Awards & Recognition: No specific awards mentioned, but his research has been cited widely in areas like genomics and computational methods. Teaching & Mentorship: Emphasizes active learning through interactive sessions and rigorous assessments. Encourages students to develop self-directed learning strategies by avoiding pre-prepared lecture notes, fostering independent exploration of mathematical concepts.

Renjie Liaoمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

Joel Friedmanمشاهده پروفایل

استاد

• Algebraic Graph Theory
• Combinatorics
• Computer Science Theory
• +۵ مورد دیگر

Joel Friedman is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. His research focuses on Algebraic Graph Theory, Combinatorics, and Theoretical Computer Science, with significant contributions to spectral graph theory, sheaf theory, and computational complexity. He has supervised numerous PhD and Master's students in these areas. Friedman teaches courses such as CPSC 421 (Introduction to the Theory of Computing) and CPSC 531F (Discrete Hodge Theory and Topological Data Analysis). His work bridges pure mathematics and computer science, including studies on Riemann-Roch theorems for graphs, eigenvalue conjectures, and applications of linear algebra in discrete structures. He has authored over 50 publications, with recent contributions on topological data analysis, graph expansions, and algorithmic methods. Awards and honors include the Izaak Walton Killam Memorial Faculty Research Fellowship. Friedman actively engages in graduate supervision, advising students on topics like sparsifier constructions and coded caching problems. His research is affiliated with the Institute of Applied Mathematics at UBC. He has held academic positions since the 1980s, with a consistent record of contributions to theoretical computer science and discrete mathematics.

Ehrhard Behrendsمشاهده پروفایل

استاد

• Functional Analysis
• Probability Theory
• Stochastic Processes
• +۴ مورد دیگر

Prof. Ehrhard Behrends is a Professor of Mathematics at the Department of Mathematics and Computer Science, Freie Universität Berlin. His research focuses on functional analysis, probability theory, and stochastic processes. He is also deeply involved in mathematical education and public outreach, having authored numerous popular mathematics books and articles. Key roles include leading the European Mathematical Society's Raising Public Awareness committee (2009–2015) and developing the mathematics portal mathematics-in-europe.eu . Behrends has organized major exhibitions like 'Mathematics for all the Senses' and contributed to initiatives such as the Year of Mathematics 2008. His work bridges advanced research with accessible communication, reflected in his books on Markov chains, analysis textbooks, and the Five Minutes of Mathematics column.

Caitlin Lienkaemperمشاهده پروفایل

پژوهشگر ارشد

• Neural Networks
• Dynamical Systems
• Combinatorial Geometry
• +۳ مورد دیگر

Caitlin Lienkaemper is a Research Fellow at Boston University, affiliated with the Applied Mathematics and Dynamical Systems research groups. She holds an office in CDS 342 and can be reached at clienk@bu.edu. Her work bridges neuroscience, combinatorics, and dynamical systems, focusing on neural coding, network dynamics, and mathematical modeling of biological systems. Education: She earned her Ph.D. in Mathematics from Penn State University in 2022, advised by Carina Curto. Her doctoral thesis, Combinatorial geometry of neural codes, neural data analysis, and neural networks , laid the foundation for her interdisciplinary research. Research Interests: Lienkaemper’s research integrates algebraic and geometric tools with neuroscience, exploring topics such as oriented matroids in neural data analysis, inhibition-stabilized neural networks, and olfactory system dynamics. Her recent work emphasizes low-dimensional structure in nonlinear systems and combinatorial approaches to neural code analysis. Publications: Her recent articles reflect a focus on neural network dynamics, olfaction models, and combinatorial methods. Key themes include mean-field theory, inhibition-driven network stability, and geometric interpretations of genetic interactions. Lienkaemper has presented her research at conferences like the Graduate Student Combinatorics Conference (2021) and the Fields Institute’s New Mathematical Methods for Neuroscience (2020), highlighting her contributions to theoretical and applied neuroscience.

• Quantum Information Science
• Quantum Mechanics
• Tensors
• +۱۰ مورد دیگر

Matthias Christandl is Professor in the Department of Mathematical Sciences at the University of Copenhagen, where he leads the Quantum for Life Center (funded by the Novo Nordisk Foundation) and co-PIs the QMATH center for quantum theory mathematics. He directs the UCPH Quantum Hub , a cross-departmental initiative. Previously, he was Assistant Professor at ETH Zurich (2010–2014) and University of Munich (2008–2010), and a Postdoctoral Fellow at University of Cambridge (2005–2008). He holds a PhD in Mathematics (2006) from Cambridge and a Diploma in Mathematics (2002) from ETH Zurich.

Monica Jinwoo Kangمشاهده پروفایل

استادیار

• Quantum Gravity
• Quantum Field Theory
• String Theory
• +۷ مورد دیگر

Monica Jinwoo Kang is an Assistant Professor at Texas A&M University's Department of Physics and Astronomy, specializing in mathematical physics and quantum gravity. Her research bridges theoretical physics and mathematics through holography, operator algebras, and quantum error correction. Education: PhD in Physics (Harvard, 2019), Bachelor's in Mathematics and Physics (UC Berkeley, 2012), Korea Science Academy Her work focuses on non-perturbative aspects of quantum field theory and quantum gravity, particularly through entanglement structures, bulk reconstruction in AdS/CFT, and geometric engineering of SCFTs. She has developed novel operator-algebraic frameworks for understanding holography in infinite-dimensional Hilbert spaces and discovered isomorphic superconformal theories with identical central charges. Recent publications demonstrate her expertise in multiparty entanglement holography, generalized symmetry constraints on 4D SCFTs, and nonperturbative gravity corrections to bulk reconstruction, with implications for baby universe formalism and quantum error correction. Scientific Awards: AKPA Outstanding Young Researcher Award (2025), Sherman Fairchild Fellowship, Purcell Fellowship, J. D. Jackson Award She has organized international conferences on stringy geometry and quantum field theory, and contributed to pedagogical lectures at institutions like Universidad Complutense de Madrid and the Simons Center. Her interdisciplinary approach combines algebraic geometry, lattice gauge theory, and quantum information to address fundamental questions in gravity and field theory.

Patric Östergårdمشاهده پروفایل

استاد

• Discrete Mathematics
• Information Theory
• Coding Theory
• +۴ مورد دیگر

Patric Östergård is a Professor at Aalto University's Department of Information and Communications Engineering. His research focuses on fundamental problems in discrete mathematics and information theory, utilizing combinatorial algorithms and massive computations to study existence and classification of mathematical structures with applications in ICT. Department: Information and Communications Engineering Institution: Aalto University His key research areas include coding theory, design theory, graph theory, and Shannon theory. He leads a high-performance computing cluster Medusa for computational work. His research is supported by the Academy of Finland's project 'Construction and Classification of Discrete Mathematical Structures' (2015–2019). Notable trends: Steiner triple systems, Hadamard matrices, and error-correcting codes Collaboration: Active in international partnerships, particularly in combinatorics and coding theory Scientific Awards Kirkman Medal (1997) for contributions to combinatorial research Doctor et Professor Honoris Causa from University of Pécs, Hungary (2013) He has supervised 7 doctoral theses and actively participates in academic service through editorial board memberships, conference committee roles, and hosting visiting scholars.