جست‌وجوی استاد و پژوهشگر

Ricky Ini Liuمشاهده پروفایل

دانشیار

• Algebraic Combinatorics
• Algebraic Geometry
• Combinatorial Geometry
• +۵ مورد دیگر

Ricky Ini Liu is an Associate Professor in the Department of Mathematics at the University of Washington. Previously, he held positions at North Carolina State University, the University of Michigan, and the University of Minnesota. He earned his Ph.D. in Mathematics from MIT in 2010 under Alexander Postnikov. His research focuses on algebraic combinatorics, particularly its intersections with algebraic geometry, combinatorial geometry, and representation theory. Key interests include Schubert polynomials, polytopes, Hopf algebras, and Kronecker coefficients. He has contributed to foundational work on birational rowmotion, Gelfand-Tsetlin polytopes, and Fomin-Kirillov algebras. Liu has taught a wide range of courses at UW, including special topics in dynamical algebraic combinatorics, combinatorial theory, and problem-solving. He has also been a key instructor at the Mathematical Olympiad Summer Program since 2007 and mentored undergraduates in research programs at the University of Minnesota, Duluth. His publications span high-impact journals like Selecta Mathematica and Journal of Combinatorial Theory , with recent work addressing topics such as determinantal formulas for Schubert polynomials and applications of flow polytopes to diagonal harmonics. Though no specific awards are listed, his extensive publication record and academic roles reflect significant contributions to combinatorial mathematics.

Thomas Hartmanمشاهده پروفایل

استاد

• Quantum Gravity
• Quantum Field Theory
• String Theory
• +۴ مورد دیگر

Thomas Hartman is a Professor of Physics in the College of Arts and Sciences at Cornell University. He received his A.B. in Physics from Princeton University in 2004 and his Ph.D. in Physics from Harvard University in 2010. His professional journey includes being a Member of the School of Natural Sciences at the Institute for Advanced Study (2010-2013), Research Associate at the Kavli Institute for Theoretical Physics, UCSB (2013-2014), Assistant Professor at Cornell University (2014-2020), Associate Professor at Cornell University (2020-2022), and Professor at Cornell University (2022-present). Hartman's research focuses on theoretical aspects of quantum gravity and quantum field theory, with particular emphasis on black hole information and strongly interacting quantum fields. His work explores four major interconnected areas: gauge/gravity duality (examining how quantum field theory degrees of freedom organize into fluctuating spacetime), black hole information paradox (investigating the relationship between classical black hole solutions and quantum statistical systems), new approaches to quantum field theory using dualities and entanglement dynamics, and the physics of de Sitter space with implications for early universe cosmology. His research employs techniques from string theory, holographic duality, general relativity, and quantum information theory. Analysis of Hartman's publication record reveals a strong focus on resolving fundamental questions in quantum gravity, particularly through the development of replica wormhole techniques that address the black hole information paradox. His work spans both highly mathematical approaches to quantum gravity and connections to potentially observable phenomena, with increasing emphasis on connections between quantum information science and gravitational physics in recent years. Member, School of Natural Sciences, Institute for Advanced Study, 2010-2013 Hartman has advised graduate students including Jeevan Chandra Namburi and Wan Zhen Chua, contributing to the next generation of theoretical physicists. His research group actively investigates the emergence of spacetime from quantum information principles and develops new mathematical frameworks for understanding quantum gravity. The group maintains strong connections with other leading institutions through collaborative projects and participates in major theoretical physics initiatives including Snowmass planning for future research directions in high energy physics. Hartman's research program represents a vital bridge between abstract theoretical concepts in quantum gravity and potential experimental tests, working to develop frameworks that could ultimately connect quantum gravity to observable phenomena in both high-energy physics and cosmological observations.

• Artificial Intelligence
• Algorithmic Game Theory
• Multiagent Reinforcement Learning
• +۴ مورد دیگر

Amy R Greenwald is a Professor of Computer Science at Brown University. Her research spans artificial intelligence, algorithmic game theory, and computational economics, with a focus on multiagent reinforcement learning and market equilibrium computation. Education PhD, New York University (1999) MS, Cornell University (1995) MS, Oxford University (1992) BS, University of Pennsylvania (1991) Research Focus Greenwald's work explores strategic interactions in computational systems, including: Game-theoretic modeling of multiagent systems Algorithmic approaches to market equilibrium Simulation-based equilibrium learning Stackelberg game formulations for hierarchical decision making Applications to supply chain negotiations and economic design Her recent publications emphasize tractable equilibrium computation, social influence in economic models, and advanced reinforcement learning techniques for strategic settings. Teaching CSCI 0100 - Data Fluency for All CSCI 0180 - Computer Science: An Integrated Introduction CSCI 1440 - Algorithmic Game Theory CSCI 2440 - Advanced Algorithmic Game Theory CSCI 2951Z - Advanced Algorithmic Game Theory

Eric V. Mazumdarمشاهده پروفایل

استادیار

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

Eric V. Mazumdar is an Assistant Professor at the California Institute of Technology (Caltech), jointly appointed in the departments of Computing and Mathematical Sciences and Economics. He holds a B.S. from MIT (2015) and a Ph.D. from UC Berkeley (2021), co-advised by Michael Jordan and Shankar Sastry. His research bridges machine learning and economics, focusing on deploying algorithms into societal systems through theoretical and practical lenses. Key areas include strategic classification, multi-agent reinforcement learning, and distributionally robust optimization, with applications in healthcare, online markets, and intelligent infrastructure. Education: B.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2015 Ph.D., Electrical Engineering and Computer Science, University of California, Berkeley, 2021 Research Interests: Mazumdar’s work emphasizes understanding learning algorithms in strategic environments, including min-max optimization, game theory, and multi-agent systems. He explores how algorithms interact with human and algorithmic agents in dynamic systems, with practical applications in healthcare delivery, e-commerce, and autonomous systems testing. Awards: NSF CAREER Award (2023) Simons Institute Research Fellowship in Learning in Games Grants & Funding: Supported by NSF, DARPA, Amazon, and other organizations. His NSF CAREER grant focuses on strategic interactions in societal-scale systems. Teaching: Courses include Networks: Structure & Economics (CMS/CS/EE/IDS 144) and Topics in Learning and Games (CMS/Ec 248). At UC Berkeley, he contributed to courses like Data, Inference, and Decisions (DS 102). Students & Postdocs: Current students: Lauren Conger, Tinashe Handina, Yizhou Zhang Postdocs: Zaiwei Chen, Laixi Shi, Kishan Panaganti (co-advised with Adam Wierman)

Amir Ali Ahmadiمشاهده پروفایل

استاد

• Optimization Theory
• Control Theory
• Dynamical Systems
• +۷ مورد دیگر

Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.

Lars Rohwedderمشاهده پروفایل

دانشیار

• Algorithms
• Approximation Algorithms
• Online Algorithms
• +۳ مورد دیگر

Lars Rohwedder is an Associate Professor in the Algorithms Group at the University of Southern Denmark (SDU) in Odense. He previously held positions as an Assistant Professor at Maastricht University (Netherlands) and postdoc researcher at EPFL, Lausanne (Switzerland). He earned his Ph.D. in Computer Science from CAU Kiel (Germany), advised by Klaus Jansen, and is a recipient of the 2019 PhD of the year award from Förderverein der TF of Kiel University. His research focuses on algorithms for combinatorial optimization, including approximation algorithms, online algorithms, parameterized algorithms, and integer programming. He has contributed to solving scheduling problems, resource allocation, and optimization under uncertainty. Rohwedder has served on program committees for conferences like MAPSP, SODA, STACS, and ICALP. He is funded by NWO's Open Competition M1 project on quasi-polynomial time algorithms. His teaching includes courses on advanced algorithms, operations management, and optimization at SDU and Maastricht University. Key achievements include a quasi-polynomial approximation for the restricted assignment problem, FPT algorithms for scheduling, and contributions to the Submodular Santa Claus problem. His work bridges theoretical foundations and practical applications, with a focus on algorithmic efficiency and robustness.

• Stochastic Differential Equations
• Financial Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.

• Algorithmic Game Theory
• Concurrent Systems
• Formal Verification
• +۳ مورد دیگر

Dr. Rasmus Ibsen-Jensen is a Lecturer in Computer Science at the University of Liverpool. Previously, he held a Postdoctoral position at IST Austria under Krishnendu Chatterjee and completed his PhD under Peter Bro Miltersen. Research Focus: Algorithmic game theory, strategy complexity in two-player zero-sum games, control flow graph algorithms, edit distance for automata, and theoretical biology applications. Teaching: Module Coordinator for second-year courses in database development (COMP207), C++ programming (COMP282), and industrial placement (COMP299). His work bridges computational game theory and formal verification, with recent publications exploring memory constraints in partial-information games, algebraic path properties in concurrent systems, and evolutionary spatial dynamics. While no scientific awards are explicitly mentioned in the provided text, his contributions to algorithmic complexity and interdisciplinary research (e.g., theoretical biology) highlight his academic impact.

Tanja Eisnerمشاهده پروفایل

استاد

• Functional Analysis
• Operator Theory
• Dynamical Systems
• +۲ مورد دیگر

Tanja Eisner is a Professor at the University of Leipzig , affiliated with the Institute of Mathematics. Her work bridges functional analysis and operator theory with dynamical systems and ergodic theory . Research Interests : Functional analysis and operator theory Dynamical systems and ergodic theory Applications to number theory and additive combinatorics Recent Publications (15 most recent): Her articles focus on ergodic theorems, stability of operators, and connections between dynamics and number theory, with keywords spanning Mathematics , Operator Theory , Dynamical Systems , and Harmonic Analysis . Notable subfields include multiple recurrence , nilsystems , automatic sequences , and Wiener's lemma . Teaching & Collaboration : Organized miniworkshops on operator-theoretic aspects of ergodic theory in Leipzig, Wuppertal, Kiel, Feldkirch, and Tübingen Co-authored books with Bálint Farkas, Markus Haase, and Rainer Nagel Co-organized seminars like the Internet Seminar on Ergodic Theorems

Niels van Oortمشاهده پروفایل

دانشیار

• Public Transport Planning
• Data-Driven Design
• Transport Sustainability
• +۵ مورد دیگر

Niels van Oort is an Associate Professor of Public Transport at Delft University of Technology (TU Delft), co-director of the Smart Public Transport Lab, and affiliated with the Department of Transport & Planning within the Faculty of Civil Engineering and Geosciences. His work focuses on public transport planning, data-driven design, and societal impacts including inclusiveness, land-use, and sustainability. He holds a dual role as a part-time consultant via his company Van Oort Mobility Consultancy since 2020. Education: MSc in Public Transport from TU Delft (2003), PhD in Service Reliability in Public Transport (2011). Professional experience includes roles at HTM (public transport operator), MIT (2009), and Goudappel Coffeng consultants (2007–2012). Transitioned to academia as a part-time assistant professor at TU Delft in 2012, becoming full-time in 2018. Research emphasizes network optimization, timetable reliability, micromobility, and first/last mile solutions. Key areas include pandemic impacts on transport behavior, digital inequality in mobility access, and sustainable transport policies. Supervises PhD students on integrated networks and passenger behavior. Teaching includes courses on public transport operations, advanced modelling, and transport networks. Active in academic publishing and conferences, with over 20 peer-reviewed articles since 2010. Engaged in interdisciplinary labs like SUM Lab (Smart Urban Mobility) and contributes to public discourse through media and consultancy. Labs/Teams: Smart Public Transport Lab (co-director), SUM Lab, and Delft Transport Modelling Group. Grants and projects focus on data-driven solutions for public transport efficiency and equity.

Stephen A. Vavasisمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science
• First-Order Methods
• +۴ مورد دیگر

Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.

Markus Landمشاهده پروفایل

عضو هیئت علمی

• Algebraic Topology
• Homotopy Theory
• K-Theory
• +۳ مورد دیگر

Markus Land is a Tenure Track Professor at the Department for Mathematics, Ludwig Maximilian University of Munich, affiliated with the Algebraic Geometry Working Group. His research bridges algebraic topology, homotopy theory, and K-theory, focusing on their interplay with L-theory and manifold topology. He has held postdoctoral positions at the University of Copenhagen (supported by an EU Marie Curie Fellowship and DFG Grant) and the University of Regensburg, and earned his PhD at the University of Bonn under Wolfgang Lück. Education : PhD in Mathematics (University of Bonn, 2016) Positions : Postdoctoral (University of Copenhagen, 2019–2022; University of Regensburg, 2016–2019) Research Interests : Markus Land explores algebraic topology and homotopy theory , particularly algebraic K-theory , hermitian K-theory , and their connections to L-theory and manifold topology . His work often applies infinity-categories to foundational problems in operator K-theory and geometric topology. Recent Publications : His research includes groundbreaking studies on chromatic localization in K-theory, Grothendieck-Witt groups in ring theory, and additivity in cobordism categories . Collaborations with leading mathematicians like Georg Tamme and Ulrich Bunke highlight interdisciplinary approaches to algebraic and geometric problems. Awards & Grants : EU Marie Curie Individual Fellowship (2020–2022) DFG Individual Research Grant (2019–2020) Teaching & Academic Leadership : He has designed advanced courses including Topology I–V , Algebraic K-Theory , and Condensed Mathematics , emphasizing homotopy theory and infinity-categories . Future initiatives include student seminars on arithmetic and algebraic geometry to foster academic collaboration. Working Group : As part of the Algebraic Geometry Working Group at LMU Munich, he contributes to seminars and research projects on arithmetic geometry , manifold classification , and spectral algebra .

Jan de Gierمشاهده پروفایل

استاد

• Mathematical Physics
• Statistical Mechanics
• Solvable Lattice Models
• +۳ مورد دیگر

Jan de Gier is a Professor at the School of Mathematics and Statistics, The University of Melbourne . He is also the Founding Director of MATRIX , Australia’s residential research institute in the mathematical sciences, and a former Deputy Director and Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . Additionally, he co-founded the Australian and New Zealand Association for Mathematical Physics (ANZAMP) in 2011 and served as its inaugural Chair. His research focuses on solvable lattice models at the intersection of mathematical physics and statistical mechanics . Key areas include the application of quantum integrability , algebraic structures like the Yang-Baxter equation, Hecke algebras, and quantum groups, as well as analytical methods such as complex analysis and elliptic curves. His work bridges pure and applied mathematics through connections between enumerative combinatorics , representation theory , and real-world phenomena like traffic flow modeling via exclusion processes . The 15 most recent articles reflect his expertise in integrable systems , non-equilibrium statistical mechanics , and algebraic combinatorics . Topics span Macdonald polynomials , stochastic duality , quantum spin chains , and traffic modeling , with methodologies involving matrix product forms , exact solutions , and critical phenomena analysis. He has contributed to editorial efforts through the AustMS Gazette and MATRIX Annals, and has been involved in public science communication via opinion pieces on mathematics funding and applications. His work emphasizes the importance of fundamental research in driving technological innovation, as highlighted in media articles discussing pi calculation , zero-knowledge proofs , and mathematics education .

Christophe VIGNATمشاهده پروفایل

استاد

• Mathematics
• Number Theory
• Probability Theory
• +۴ مورد دیگر

Christophe VIGNAT is a Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). His research focuses on number theory, special functions, probability, and their applications in signal processing and control systems. He has held visiting professorships at École Polytechnique Fédérale de Lausanne (EPFL) and Tulane University. VIGNAT's work bridges pure mathematics and applied fields, with notable contributions to Bernoulli/Euler polynomials, multiple zeta values, and probabilistic methods in number theory. His recent publications explore topics like partition functions, theta functions, and Ramanujan-type identities. He has delivered talks at international conferences and collaborates widely with researchers in mathematics and physics. Research Interests: Number theory, special functions (Bessel, orthogonal polynomials), probability theory, signal processing, control systems, analytic combinatorics, and their interconnections. His work often employs symbolic computation and probabilistic approaches to uncover identities and structures in mathematical analysis. Publications Trends: Recent articles emphasize partition theory, zeta functions, and integrals related to classical polynomials. His collaborations highlight interdisciplinary efforts between pure mathematics and applied sciences. Over 150 refereed papers and conference contributions demonstrate his prolific output across diverse mathematical domains. Education: While specific academic history isn’t detailed, his roles and publications suggest advanced training in mathematics and engineering, typical for a full professor in systems and control.

Clyde Kruskalمشاهده پروفایل

دانشیار

• Parallel Computing
• Algorithms
• Computer Architecture
• +۳ مورد دیگر

Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.