جست‌وجوی استاد و پژوهشگر

Josh Hillerمشاهده پروفایل

دانشیار

• mathematical epidemiology
• combinatorics
• algorithmic graph theory
• +۵ مورد دیگر

Josh Hiller is an Associate Professor in the Department of Mathematics and Computer Science at Adelphi University's College of Arts and Sciences. He holds leadership roles as Past President of the university's American Association of University Professors chapter. His academic journey includes a Ph.D. in Mathematics from the University of Florida (2017), an M.S. in Applied Mathematics from Western Carolina University (2014), and a B.A. in Mathematics from Webster University (2005). Dr. Hiller's research integrates mathematical biology (epidemiology, carcinogenesis), discrete mathematics, and theoretical computer science with a focus on algorithmic graph theory. His interdisciplinary interests extend to math communication and labor rights. He teaches advanced courses including Discrete Structures, Foundations of Analysis, and specialized research seminars in computational Ramsey theory and hypergraph models. His recent publications demonstrate a dual focus on combinatorial mathematics (hypergraphs, group partitions, binomial matrices) and biomathematical modeling (carcinogenesis, epidemiology, Markov chains). A distinctive thread is his work in mathematical poetry, exploring creative intersections between abstract concepts and literary expression. Grants secured by Dr. Hiller include: SIAM-Simons Foundation Undergraduate Research Program ($69,552, 2025) for deforestation prevention modeling AMS Young Scholars Fund ($15,100, 2022-2025) for the Adelphi Summer Institute of Mathematical Epidemiology Dolciani Mathematics Enrichment Grant ($5,000, 2025) for speaker series development Center for Undergraduate Research in Mathematics awards ($32,800 total, 2019-2021) for hypergraph carcinogenesis projects These initiatives emphasize undergraduate mentorship in computational epidemiology and discrete mathematics research. As co-director of the Adelphi Summer Institute of Mathematical Epidemiology, he leads collaborative teams investigating hypergraph applications in biological systems. His work consistently bridges theoretical exploration with pedagogical innovation.

Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

• Applied Analysis
• Computational Probability
• Statistics
• +۵ مورد دیگر

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

Carola-Bibiane Schönliebمشاهده پروفایل

استاد

• Image Analysis
• Image Processing
• Inverse Problems
• +۳ مورد دیگر

Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.

• Mathematical Analysis
• Stochastic Analysis
• Metric Geometry
• +۴ مورد دیگر

Dr. Michael Hinz is a researcher at Bielefeld University's Faculty of Mathematics with significant involvement in multiple research initiatives. He is affiliated with the International Research Training Group 2235 as a participating scientist and contributes to the Collaborative Research Center 1283 (SFB 1283) project titled 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications,' specifically working on subproject A3: 'Analysis of manifolds, metric spaces and graphs.' Additionally, he is a member of the Bielefeld Graduate School in Theoretical Sciences academic staff. His research focuses on the intersection of mathematical analysis and stochastic processes, with particular emphasis on geometric structures. Dr. Hinz maintains offices at locations UHG V4-239 and UHG V3-237 on campus and can be reached through the Faculty of Mathematics secretariat at +49 521 106-4773. Research interests include: Analysis on manifolds and metric measure spaces Stochastic processes in geometric settings Spectral theory on graphs and fractals Dirichlet forms and potential theory Geometric analysis with applications to mathematical physics Dr. Hinz contributes to the 'Mathematical World' strategic research area at Bielefeld University, which develops fundamental mathematical concepts and theories with applications to solve long-standing open problems in economics and the natural sciences. His work aligns with the university's interdisciplinary research culture, particularly connecting mathematical theory with applications in complex systems.

Dmitriy (Tim) Kuniskyمشاهده پروفایل

استادیار

• Probability
• Statistics
• Mathematical Data Science
• +۷ مورد دیگر

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.

Sally Cockburnمشاهده پروفایل

استاد

• Set Theory
• Philosophy of Mathematics
• Combinatorial Optimization
• +۴ مورد دیگر

Sally Cockburn is a Professor in the Mathematics and Statistics Department at Hamilton College, where she has been part of the faculty since 1991. Her research spans combinatorial optimization, geometric graph theory, and the philosophical foundations of mathematics. Education: Ph.D. from Yale University, M.Sc. from Queen’s University, M.Sc. from University of Ottawa, B.Sc. (Honors) from Queen’s University Her research focuses on geometric graph homomorphisms and poset structures, with applications in combinatorial optimization and computational complexity. She has explored philosophical questions in mathematics education and developed pedagogical materials for undergraduate courses. Recent publications analyze geometric graph properties (like K2,n and K3,3), NP-hardness, and set theory. These works situate her at the intersection of discrete mathematics and educational innovation. Scientific Awards: Mathematical Association of America Carl B. Allendoerfer Award (2014) Class of 1963 Faculty Fellowship (2005, 1996) Cockburn mentors students through collaborative research, notably guiding Yonghyun Song and Joshua Lesperance. She actively participates in college governance as a faculty secretary and on academic policy committees. As Mellon curricular leader for quantitative reasoning and squash team coach, she integrates academic and athletic leadership.

Kurusch Ebrahimi-Fardمشاهده پروفایل

استاد

• Numerical integration methods
• Nonlinear control theory
• Stochastic calculus
• +۱۲ مورد دیگر

Kurusch Ebrahimi-Fard is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. He is an active member of the Differential Equations and Numerical Analysis (DNA) group at IMF-NTNU, with extensive international collaborations across Europe and North America. His research spans a remarkable breadth of mathematical disciplines, focusing on the surprising convergence of algebraic and combinatorial structures across seemingly disparate fields. His work bridges geometric integration methods, nonlinear control theory, rough paths theory, free probability theory, and perturbative quantum field theory. The common thread throughout his research is the central role of algebraic and combinatorial structures on graphs, partitions, and other combinatorial objects. Dr. Ebrahimi-Fard's recent publications reveal strong trends in the application of Hopf algebraic structures to probability theory and stochastic analysis, with significant contributions to understanding signatures in rough path theory and their applications to image analysis. His work consistently demonstrates how abstract algebraic structures can provide powerful tools for concrete problems in numerical analysis and mathematical physics. Juan de la Cierva fellowship Ramón y Cajal fellowship Fellow of the Studienstiftung des Deutschen Volkes Fellow of the Evangelisches Studienwerk e.V. Fellow of the German Academic Exchange Service (DAAD) Fellow of the European Post-Doctoral Institute (EPDI) Throughout his career, Dr. Ebrahimi-Fard has organized numerous international conferences and workshops, including the 18th Santaló Summer School on Algebraic and Combinatorial Methods in Stochastic Calculus. His editorial work includes special issues on theoretical and computational aspects of dynamical systems, non-commutative algebra, and algebraic structures in perturbative quantum field theory. He has also secured research funding through various grants including the BBVA Foundation Research Grants. Dr. Ebrahimi-Fard maintains active collaborations with researchers worldwide and participates in several research networks focused on the intersection of algebra, combinatorics, and mathematical physics. His work with the DNA group at NTNU continues to explore fundamental mathematical structures with applications across scientific disciplines.

• Quantum Chaos
• Wave Scattering
• Random Matrix Theory
• +۴ مورد دیگر

Martin M A Sieber is a Reader in Applied Mathematics at the School of Mathematics, University of Bristol, specializing in mathematical physics and quantum chaos. With a Ph.D. from Hamburg and an M.Sc. from California, his research focuses on the intersection of semiclassical physics, wave chaos, and random matrix theory in quantum systems. Sieber's primary research explores quantum chaotic systems through semiclassical approximations, optical microresonators, and wave scattering phenomena. His work connects mathematical physics with experimental realizations in microwave and optical systems, analyzing resonance statistics, eigenfunction distributions, and directional emission in complex cavities. Key interests include the universal properties of chaotic scattering, trace formulas for quantum systems, and statistical behavior of disordered systems. His publications demonstrate consistent focus on quantum chaos universality, semiclassical methods for wave systems, and optical/microwave resonator physics. Recent work shows progression toward symmetry-inclusive quantum models and experimental validations of random matrix theories. Research Projects: ASYMPTOTIC AND NUMERICAL APPROACHES TO THE THEORY OF OPTICAL MICRORESONATORS AND MICROLASERS (2006-2009): Investigated dielectric properties, Q factors, and scatterer effects in microresonators. SEMICLASSICAL THEORIES OF QUANTUM FLUCTUATION STATISTICS (2005-2006): Developed frameworks for fluctuation properties in chaotic quantum systems.

Joseph S.B. Mitchellمشاهده پروفایل

استاد

• Computational Geometry
• Algorithms and Data Structures
• Optimization
• +۳ مورد دیگر

Joseph S.B. Mitchell is a SUNY Distinguished Professor at the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook. His office is located in Math Tower, Room P-138A. His research spans computational geometry, algorithms, optimization, and related fields. Research Interests: Prof. Mitchell's work focuses on fundamental and applied problems in geometric computing, including: Algorithm design for spatial optimization (e.g., art gallery problems, dispersion) Robotics applications (multi-agent path planning, sensor deployment) Efficient solutions for geometric data structures and visibility constraints Recent Publications: His 15 most recent articles (2024-2025) demonstrate a consistent focus on geometric optimization, visibility problems, and algorithm design for polygons and networks. Common themes include approximation algorithms, multi-robot coordination, and combinatorial solutions for spatial challenges. Awards: SUNY Distinguished Professor (recognizing exceptional academic contributions)

• Birational Geometry
• Hodge Theory
• Hyperbolicity
• +۱ مورد دیگر

Sung Gi Park is a Veblen Research Instructor jointly affiliated with Princeton University and the Institute for Advanced Study. Previously, he completed his graduate studies at Harvard University under the supervision of Mihnea Popa. His academic appointments bridge two prestigious institutions with a focus on advanced mathematical research. His research specializes in: Birational geometry and applications of Hodge theory Analysis of singular varieties using Hodge modules Hyperbolicity phenomena and geometric structures Classification and properties of singularities in algebraic varieties Recent work emphasizes the intersection of Hodge theory with birational geometry, developing novel approaches to longstanding problems. Publication analysis reveals two dominant themes: Advanced algebraic geometry (5 preprints on singularities/Hodge theory) Contributions to commutative algebra and graph theory (2 peer-reviewed papers) This demonstrates both specialized depth in geometric methods and interdisciplinary versatility. Teaching experience includes: Upcoming instruction in Riemann Surfaces (MAT 531) and Multivariable Calculus (MAT 201) at Princeton Previous teaching roles in Multivariable Calculus (Math 21A) and Algebraic Topology (Math 231A) at Harvard His instructional responsibilities span foundational to advanced graduate-level mathematics courses.

Shachar Itzhakyمشاهده پروفایل

دانشیار

• Program Synthesis
• Automated Reasoning
• Functional Programming
• +۵ مورد دیگر

Shachar Itzhaky is an Associate Professor in the Department of Computer Science at Technion - Israel Institute of Technology, Haifa. His research spans multiple areas of programming languages, formal methods, and software engineering, with a focus on making program development and verification more accessible and efficient. He has served on program committees for numerous prestigious conferences including PLDI, POPL, SPLASH, and ICFP. Dr. Itzhaky's research interests center around program synthesis, automated reasoning, and formal verification. His work in program synthesis explores techniques for automatically generating programs from high-level specifications, with applications in end-user programming and software development. In automated reasoning, he has made significant contributions to e-graph based reasoning, invariant inference, and property-directed verification. His research in formal methods focuses on practical applications for program verification, particularly for data structures and security properties. An analysis of his recent publications reveals a strong focus on leveraging advanced formal techniques for practical program understanding and generation. His work consistently bridges theoretical foundations with practical applications, particularly in program synthesis, verification, and end-user programming tools. The trend shows increasing integration of machine learning techniques with traditional formal methods, as well as expanding applications to security and privacy domains. ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award Dr. Itzhaky has been actively involved in the programming languages research community, serving on numerous program committees and contributing to the advancement of formal methods and program synthesis. His work has practical implications for software development tools, security analysis, and end-user programming environments. While specific grant information isn't detailed in the provided text, his extensive publication record in top-tier venues suggests successful funding for his research endeavors. His work on projects like Object Spreadsheets and Lifty demonstrates a commitment to creating practical tools that address real-world programming challenges. Dr. Itzhaky's research is conducted within the vibrant programming languages and formal methods group at Technion's Computer Science department. His work intersects with multiple research threads including program synthesis, verification, and security, suggesting collaboration across these areas within the department. His tools like EPR-based Verification, PDR∀, and VeriCon represent significant technical contributions that likely form the basis of ongoing research projects with students and collaborators.

Chandrakana Nandiمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Equality Saturation
• +۵ مورد دیگر

Chandrakana Nandi is the Director of US R&D at Certora and an affiliate assistant professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She completed her PhD at the University of Washington working with Zachary Tatlock and Dan Grossman in the PLSE research group. Her research focuses on building tools for scaling automated formal verification to real-world programs, particularly for DeFi applications. She works extensively with equality saturation techniques (egg project) and has made significant contributions to computational fabrication through projects like Carpentry Compiler, Szalinski, and LambdaCAD. Her work bridges programming languages, compilers, and digital fabrication, creating novel tools that transform how we design and manufacture physical objects. Nandi's publication record shows a strong trajectory in programming language techniques applied to verification and fabrication. Her work on equality saturation has become foundational in the field, with the egg library enabling state-of-the-art results in compiler optimization and program synthesis. Recent work has expanded into formal verification of smart contracts, demonstrating the versatility of her research approach across different domains. Distinguished Paper Award at OOPSLA 2021 Sigplan Research Highlight for POPL 2021 As Director of US R&D at Certora, she leads research efforts on verification tools for languages like WASM and techniques to help users write formal specifications more easily using mutation testing. She has served in numerous organizational roles for major programming languages conferences including as Workshops Co-Chair for ICFP 2025 and Committee Member for PLDI Review Committee. Nandi has established herself as a leader in the intersection of programming languages and computational fabrication, with her work on equality saturation becoming particularly influential across multiple subfields of programming languages research.

Gregory J. Herschlagمشاهده پروفایل

استاد پژوهشی

• Computational Fluid Dynamics
• Markov Chains
• Fairness
• +۵ مورد دیگر

Gregory J. Herschlag serves as an Associate Research Professor in the Department of Mathematics at Duke University, where he conducts interdisciplinary research at the intersection of computational mathematics, political science, and fluid dynamics. His work focuses on developing rigorous mathematical frameworks to analyze political redistricting and gerrymandering while simultaneously advancing computational techniques in fluid dynamics. Dr. Herschlag's primary research interests include: Mathematical quantification of gerrymandering and electoral fairness Markov chain Monte Carlo methods for graph partitioning Computational fluid dynamics and lattice Boltzmann methods High-performance computing optimization for complex geometries Statistical sampling techniques for political district analysis His recent publications reveal a sophisticated methodological approach to redistricting analysis, particularly through innovations like Metropolized Forest Recombination, which enables more efficient sampling of graph partitions. His 2022 Georgia congressional districting analysis demonstrated that the enacted plan exhibited extreme non-responsiveness to public opinion shifts, with only 0.12% of sampled plans showing similar characteristics. In computational physics, his work on GPU data access patterns has yielded significant performance improvements of 10-40% for lattice Boltzmann simulations. Dr. Herschlag's interdisciplinary approach has established him as a key contributor to the growing field of mathematical analysis of electoral systems, with his methods being applied in legal contexts to evaluate partisan gerrymandering claims. His research bridges theoretical mathematics with practical applications that impact democratic processes and computational science.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +۷ مورد دیگر

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.