جست‌وجوی استاد و پژوهشگر

Rohil Prasadمشاهده پروفایل

پژوهشگر ارشد

• Geometry
• Topology
• Dynamical Systems
• +۶ مورد دیگر

Rohil Prasad is a Miller Research Fellow at the University of California, Berkeley, appointed in 2023, and will join Princeton University as an Assistant Professor in 2025. His research lies at the intersection of geometry, topology, and dynamical systems, with a particular focus on conservative dynamics, pseudoholomorphic curves, and Floer theory. Education: PhD in Mathematics, Princeton University (2018–2023), advised by Helmut Hofer Research Interests: Prasad’s work spans several deep areas of modern mathematics, including: Conservative Dynamics: Studying systems that preserve volume or symplectic structures. Symplectic Geometry: Exploring geometric structures preserved under Hamiltonian flows. Low-Dimensional Topology: Investigating the topology of 3- and 4-dimensional manifolds. Floer Theory: Using pseudoholomorphic curves to study periodic orbits and invariants. His research has led to significant advances in understanding periodic orbits, invariant measures, and the structure of area-preserving maps. Awards and Honors: 2024 Brin Dynamical Systems Prize for Young Mathematicians Contact: rrprasad@berkeley.edu | Office: 968 Evans Hall

• Differential Geometry
• Riemannian Geometry
• Lie Groups
• +۳ مورد دیگر

Megan Kerr serves as the Katharine and Claudine Malone '63 Professor of Mathematics at Wellesley College, where she teaches across the mathematics curriculum from calculus to advanced topics in geometry. Her academic home is within the Mathematics & Statistics Department at this prestigious women's liberal arts college. Her educational journey began as an undergraduate at Wellesley College, where she later returned as faculty, completing a full circle in her academic career. She earned her Ph.D. from the University of Pennsylvania under the supervision of Wolfgang Ziller, with research focused on Homogeneous Einstein Metrics. Professor Kerr's research centers on global differential geometry, particularly exploring the interplay between curvature constraints and large symmetry groups. She specializes in homogeneous and low-cohomogeneity spaces, investigating fundamental questions about the existence of geometric structures, their rarity or commonality, and potential obstructions. Her work bridges the analytic concept of curvature with the algebraic framework of Lie groups, and she has recently expanded into geometric analysis where topology plays a significant role. Analysis of her publication record reveals a consistent focus on homogeneous Einstein metrics across three decades, with particular attention to curvature properties, symmetry constraints, and classification problems in both positive and negative curvature settings. Her research has evolved from foundational work on symmetric spaces to more complex non-symmetric examples and specialized curvature conditions, demonstrating both depth and breadth in differential geometry. Katharine and Claudine Malone '63 Professor of Mathematics Radcliffe Institute Fellowship As an alumna of Wellesley College, Professor Kerr maintains a strong commitment to encouraging women in mathematics. She teaches a diverse range of courses including calculus, linear algebra, combinatorics, real analysis, non-Euclidean geometry, differential geometry, topology, knot theory, and matrix groups as an introduction to Lie groups. Her teaching philosophy emphasizes developing mathematical understanding and confidence that benefits students regardless of their major. Her research has taken her to international destinations including Australia, Germany, and Mexico, reflecting the global nature of her scholarly collaborations.

Ciprian Manolescuمشاهده پروفایل

استاد

• Geometry
• Topology
• Gauge Theory
• +۴ مورد دیگر

Ciprian Manolescu is a Professor of Mathematics at Stanford University, where he joined after serving as a professor at UCLA since 2008. He holds both his undergraduate degree and doctorate from Harvard University, advised by Peter Kronheimer. His research focuses on gauge theory, low-dimensional topology, and symplectic geometry, with notable contributions to Heegaard Floer theory, Khovanov homology, and the resolution of the Triangulation Conjecture. Manolescu’s academic accolades include the 2019 E.H. Moore Research Article Prize, the 2012 European Mathematical Society Prize, and a 2004 Clay Research Fellowship. He delivered an invited lecture at the 2018 International Congress of Mathematicians and became a Fellow of the American Mathematical Society in 2017. His work bridges advanced algebraic structures with geometric problems, particularly in understanding manifold invariants and topological constraints. His teaching includes the Polya Problem Solving Seminar (Math 193) at Stanford, and he advises students competing in the Putnam Mathematics Competition. Research trends in his publications emphasize applications of Floer homology to knot theory, 4-manifold topology, and the interplay between algebraic topology and quantum field theories. Education: PhD and BA in Mathematics, Harvard University Key Research Themes: Floer homology frameworks, geometric topology, knot invariants, and manifold classification Grants & Collaborations: Involved in NSF-funded projects like the FRG: Collaborative Research: Floer Homotopy Theory (2016)

John Baillieulمشاهده پروفایل

استاد

• Robotics
• Nonlinear control theory
• Mechanical systems control
• +۶ مورد دیگر

John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.

Ying Chenمشاهده پروفایل

دانشیار

• Financial Statistics
• Machine Learning
• Quantitative Finance
• +۷ مورد دیگر

Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects

Clay Córdovaمشاهده پروفایل

دانشیار

• Theoretical Physics
• Quantum Field Theory
• Particle Physics
• +۳ مورد دیگر

Clay Córdova is an Associate Professor at the University of Chicago, associated with the Enrico Fermi Institute, James Franck Institute, Kadanoff Center, and Kavli Institute. His research focuses on theoretical physics, particularly quantum field theory, non-invertible symmetries, and their applications in particle and condensed matter physics. Córdova’s work explores topological phases, gauge theories, and string theory, with recent contributions to non-invertible symmetry classification and their role in phase transitions. His research interests include categorical symmetries, topological defects, and anomaly matching in quantum field theories. He has pioneered studies on soliton-particle degeneracies, anyon condensation mechanisms, and anomalies in non-invertible symmetry frameworks. Córdova’s work bridges high-energy physics with condensed matter systems, often employing advanced mathematical techniques from category theory and algebraic topology. His 2023 Sloan Research Fellowship highlights recognition of his contributions. Key research trends span non-invertible symmetries across dimensions, topological field theory applications, and interdisciplinary methods combining machine learning with lattice gauge theory. Current projects include exploring duality defects, gapped phase obstructions, and symmetry-enriched phases in (3+1)D systems.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ مورد دیگر

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

• Numerical Modeling
• Computational Simulation
• Materials Science
• +۵ مورد دیگر

Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.

Nati Srebroمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Deep Learning
• +۴ مورد دیگر

Nati Srebro is a Professor at the Toyota Technological Institute at Chicago with a cross-appointment as a Part-Time Professor in the Department of Computer Science and Committee on Computational and Applied Mathematics at the University of Chicago. He earned his PhD from MIT in 2004 and has held previous positions including post-doctoral fellow at the University of Toronto, Visiting Scientist at IBM, and Associate Professor at the Technion. Professor Srebro's research focuses on methodological, statistical and computational aspects of Machine Learning and Optimization. His work spans foundational contributions to learning theory, matrix reconstruction, and optimization techniques. He is particularly known for introducing the use of nuclear norm for machine learning, work on wider Markov networks, and advancing our understanding of the relationship between learning and optimization. His current research interests include understanding deep learning through optimization, distributed and federated learning systems, algorithmic fairness, and practical adaptive data analysis. His publication record shows consistent contributions to core machine learning conferences and workshops, with recent work focusing on symmetric and asymmetric hashing techniques, matrix parameter learning, and optimization methods. The publications demonstrate a strong theoretical foundation with practical applications across various machine learning domains. Professor Srebro has been actively involved in several research programs including the Federated and Collaborative Learning program (Spring 2026, as Visiting Scientist and Program Organizer), Modern Paradigms in Generalization (Fall 2024), and multiple summer clusters on Deep Learning Theory and Fairness. His program participation reflects his leadership in emerging areas of machine learning research. Contact: nati@ttic.edu | (773) 834-7493 | Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637

Hannah Hogansonمشاهده پروفایل

پژوهشگر ارشد

• Geometric Group Theory
• Low Dimensional Topology
• Mapping Class Groups
• +۴ مورد دیگر

Hannah Hoganson is an NSF Postdoctoral Fellow in the Department of Mathematics at the University of Maryland, mentored by Christian Rosendal after previously holding a Brin Postdoctoral Fellowship under Lei Chen. Her research bridges geometric group theory, low-dimensional topology, and descriptive set theory, with a focus on mapping class groups of infinite-type surfaces and topological groups. Her educational background includes a PhD from the University of Utah (2022) advised by Ken Bromberg, and prior graduate studies at Miami University where she investigated Thompson's groups. She has taught multiple calculus courses at UMD and the University of Utah, receiving exceptional student evaluations for her clarity and supportive teaching style. Hoganson's research explores the coarse geometry of mapping class groups, connections between topological groups and descriptive set theory, and geometric structures on infinite-type surfaces. Her work often combines algebraic, geometric, and topological methods to address fundamental questions about group actions and classification problems in low-dimensional topology. Her recent publications demonstrate a strong trend toward interdisciplinary approaches, integrating geometric group theory with descriptive set theory to analyze infinite-type mapping class groups and Polish groups. Key themes include geometric finiteness, coarse boundedness, and the interplay between algebraic structures and topological dynamics in infinite settings. NSF Postdoctoral Fellowship (DMS-2303365) Brin Postdoctoral Fellowship Hoganson has advised an undergraduate reading course in geometric group theory (Spring 2024) and served as a mentor for REU students at SUMSRI. Her current research is supported by NSF grant DMS-2303365, which funds her postdoctoral work on geometric and topological aspects of infinite-type surfaces and groups. She actively collaborates with researchers including George Domat, Sanghoon Kwak, and Robbie Lyman across multiple projects. She co-organizes the University of Maryland Geometry and Topology Seminar and has co-led specialized workshops including the Big Mapping Class Groups log cabin workshop in Young, AZ (2024) and the AWM special session on Women in Groups, Geometry and Dynamics (2023), fostering collaborative research environments in geometric topology.

Peng Xiongمشاهده پروفایل

استاد

• Mesoscale Physics
• Spintronics
• Organic/Solid-State Hybrid Structures
• +۱ مورد دیگر

Peng Xiong is a Professor in the Department of Physics at Florida State University, with a research focus on electron and spin transport in low-dimensional quantum materials. He is affiliated with the Integrative NanoScience Institute (INSI) and has made significant contributions to mesoscale physics, spintronics, and organic/solid-state hybrid systems. B.S. in Physics (1987, University of Science and Technology of China) Ph.D. in Physics (1993, Brown University) Postdoctoral Fellowship (1993-1997, University of California at San Diego) Research Interests: Mesoscale Physics: Quantum phase transitions and fluctuation effects in 2D and 1D systems, semiconductor nanowires, carbon nanotubes, and nano-magnetism. Spintronics: Spin-polarized transport in hybrid structures (ferromagnet/normal metal, ferromagnet/superconductor, ferromagnet/semiconductor), magnetic semiconductors, and spin injection/detection. Organic/Solid-State Hybrids: Nanoscale biosensors utilizing magnetic and electrical principles, bio-mechanical devices, organic/solid interfaces, and template-directed nanostructure self-assembly. Publication Trends: His recent work spans superconducting fluctuations in ultrathin films, chirality-induced spin transport in semiconductors, interplay between structural chirality and spin-orbital effects, ion migration dynamics in 1D hybrids, quantum interference in nanowire loops, and modulation of nanomaterial properties through surface defect engineering. These studies often combine material synthesis, nanofabrication, cryogenic transport, and tunneling measurements. Scientific Recognition: Alfred P. Sloan Research Fellowship (1998) University Teaching Award (2003) PAI Award for Excellence (2004) Developing Scholar Award (2007) Fellow of the American Physical Society (2012) Advising Legacy: He has mentored numerous graduate students including Jeffrey Parker (Ph.D. 2003) Yongqing Li (Ph.D. 2003) Tianhan Liu (Ph.D. 2021) Jacob Hudis (Ph.D. 2021) and continues to guide current candidates like Yuwaraj Adhikari and Zhenqi Hua. Experimental Facilities: The lab at FSU features advanced equipment for nano-fabrication, including clean rooms, mask aligners, and thin film deposition systems. Cryogenic capabilities extend to dilution refrigerators (15 mK) and He3/He4 cryostats for ultralow-temperature studies of magnetic and superconducting systems.

Yi Laiمشاهده پروفایل

استادیار

• Geometric Analysis
• Ricci Flows
• Steady Gradient Ricci Solitons
• +۳ مورد دیگر

Yi Lai is an Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI). He earned his Ph.D. from UC Berkeley in 2021 under Richard Bamler and served as a Szego Assistant Professor at Stanford University (2021–2024), mentored by Otis Chodosh. His research centers on geometric analysis, with a focus on Ricci flows, steady gradient solitons, and the geometry of 3-manifolds. Dr. Lai's work explores the construction and classification of geometric structures like flying wing solitons, convergence properties of Ricci flow, and curvature behavior in low-dimensional manifolds. His publications frequently address long-time existence of geometric flows and symmetry in solitons, blending PDE theory with differential geometry. At UCI, he teaches courses such as Linear Algebra (Math 121A). No awards, grants, or supervised students are mentioned in available sources.

Emilio Artachoمشاهده پروفایل

استاد

• Condensed Matter Physics
• Computational Physics
• Molecular Dynamics
• +۴ مورد دیگر

Professor Emilio Artacho is a faculty member in the Department of Physics at the University of Cambridge, based at the Cavendish Laboratory. He transitioned from the Department of Earth Sciences in 2011, where he was granted a Professorship in 2006. His research focuses on computational simulations of non-equilibrium processes in condensed matter, particularly using first-principles molecular dynamics and density-functional theory. He co-developed the SIESTA program for linear-scaling electronic structure calculations, widely utilized in computational materials science. Artacho’s work spans far-from-equilibrium phenomena in irradiated matter, multiferroics, nanoconfined water systems, and surface chemistry. His contributions include studies of electronic stopping power in materials, 2D electron gas formation at ferroelectric interfaces, and the structural dynamics of water under confinement. His academic roles include adjunct positions at Ikerbasque (Nanogune, Spain) and visiting professorships at institutions like the University of California, Berkeley, and École Normale Supérieure de Lyon. Research interests are anchored in theoretical condensed matter physics, with applications to nanomaterials, radiation effects, and interfacial phenomena. His computational methods bridge quantum mechanics and classical dynamics, enabling insights into complex systems like proton-irradiated solar cells and confined water films.

Steven Sivekمشاهده پروفایل

استاد

• Pure Mathematics
• Topology
• Contact Geometry
• +۴ مورد دیگر

Steven Sivek is a Reader in Pure Mathematics at Imperial College London, affiliated with the Geometry group within the Department of Mathematics. His research focuses on contact and symplectic geometry, knot theory, gauge theory, and low-dimensional topology. He has taught courses on contact geometry, symplectic geometry, mapping class groups, and Morse theory at institutions including Imperial College London, Harvard University, and the University of Bonn. His work includes advanced topics such as Floer homology, Legendrian knots, and the interplay between contact structures and 3-manifolds. Sivek's contributions bridge geometric topology with algebraic structures, addressing questions about knot invariants, representation varieties, and topological field theories. His courses emphasize foundational texts and advanced research topics, such as the classification of tight contact structures, Morse theory applications, and symplectic fiber bundles. He has organized seminars on mapping class groups and taught graduate-level material on advanced geometry and topology. Sivek's research has been published in numerous high-impact papers, exploring themes like Khovanov homology, instanton Floer homology, and the geometry of 3- and 4-manifolds. His academic profile reflects a commitment to both teaching and cutting-edge research, with a focus on geometric and topological questions that intersect algebraic and differential structures.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.