جست‌وجوی استاد و پژوهشگر

Danielle Y Wangمشاهده پروفایل

استادیار

• Combinatorics
• Number Theory
• Graph Theory
• +۲ مورد دیگر

Danielle Y Wang serves as the Phillip Griffiths Assistant Research Professor in the Department of Mathematics at Duke University, where she teaches linear algebra courses including MATRICES AND VECTORS (MATH 218D) for Fall 2025. Her educational background features a Ph.D. from Massachusetts Institute of Technology (2024). Dr. Wang's research spans discrete mathematics with significant contributions to combinatorics, number theory, and graph theory. In combinatorics, she proved the γ-positivity of Eulerian distributions on involutions, resolving a conjecture by Guo and Zeng. Her graph theory work on Wiener polynomials established that real Wiener roots of trees are dense in (-∞,0] and complex roots are dense in ℂ, while also determining precise bounds for maximum modulus among tree Wiener roots. In number theory, she has advanced understanding of Erdös–Ginzburg–Ziv constants and rational points on curves with low Mordell-Weil rank. Her publication timeline (2019-2021) reveals progression from classical combinatorial problems toward sophisticated intersections of combinatorics, number theory, and algebraic geometry, with increasing emphasis on Diophantine aspects in her most recent work. Phillip Griffiths Assistant Research Professor (prestigious postdoctoral position) Dr. Wang maintains active teaching responsibilities while pursuing research, currently scheduled for two sections of MATRICES AND VECTORS in Fall 2025. As a recent Ph.D. graduate with multiple publications in top combinatorics and number theory journals, she represents an emerging scholar with promising research trajectory in discrete mathematics.

• Reinforcement Learning
• Learning in Games
• Applied Probability
• +۲ مورد دیگر

Zaiwei Chen is an Assistant Professor in the Edwardson School of Industrial Engineering at Purdue University. His research focuses on developing theoretical foundations for sequential decision-making under uncertainty, particularly in reinforcement learning, multi-agent systems, and optimization. He leads projects on data-efficient algorithms with applications in control and game theory. He holds a Ph.D. in Machine Learning and dual M.S. degrees in Mathematics and Operations Research from Georgia Institute of Technology, and a B.S. in Electrical Engineering from Zhejiang University. His research bridges applied probability, machine learning, and optimization to create robust algorithms for complex systems. Recent work emphasizes convergence guarantees in multi-agent reinforcement learning and scalable solutions for high-dimensional problems. Chen's publications demonstrate a consistent focus on theoretical guarantees for reinforcement learning algorithms, particularly in stochastic and game-theoretic settings. Key trends include Lyapunov stability frameworks, finite-sample analysis, and methods to overcome computational bottlenecks like the "curse of dimensionality." Awards include: Solberg Academic Excellence Scholarship PIMCO Postdoctoral Fellowship in Data Science (2022) Sigma Xi Best Ph.D. Thesis Award Runner-up for SIGMETRICS Doctoral Dissertation Award He actively recruits PhD students to work on reinforcement learning theory and maintains collaborations with Caltech and Georgia Tech. Future work includes scalable multi-agent learning frameworks and real-world applications in industrial systems.

Stephanos Venakidesمشاهده پروفایل

استاد

• Mathematical Physics
• Differential Equations
• Nonlinear Waves
• +۳ مورد دیگر

Stephanos Venakides is a Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. His research focuses on mathematical physics, differential equations, nonlinear waves, and micromagnetics. He obtained his Ph.D. from New York University in 1982, following degrees from Georgia Institute of Technology and the National Technical University of Athens. Venakides has held significant roles, including Director of Graduate Studies in Mathematics at Duke (2024–2025). His work bridges theory and computation, addressing problems in scattering phenomena, soliton dynamics, and biological morphogenesis. Key contributions include studies on magnetic skyrmions and the semiclassical limit of integrable systems. He has been awarded grants from the NSF and the Army Research Office, and served as an Abel Lecturer for the Norwegian Academy in 2005. Venakides teaches courses on differential equations and contributes to academic service at Duke. Education: Ph.D., New York University, 1982 M.S., Georgia Institute of Technology, 1979 B.S., National Technical University of Athens, 1969 Research Interests: He investigates micromagnetic configurations like skyrmions, acoustic/electromagnetic scattering in periodic media, and nonlinear wave phenomena. Recent work explores skyrmion dynamics in ferromagnets and their mathematical profiles. His approaches combine analytical methods with numerical simulations, addressing challenges in materials science and fundamental physics. Grants & Awards: NSF grants on wave-breaking phenomena (2007–2018) ARO funding for photonic materials research (1999–2003) Abel Lecturer honor (2005) Teaching: Teaches MATH 353/753 (Differential Equations) and MATH 453 (Partial Differential Equations). Innovates pedagogy, such as teaching PDEs through integrative frameworks to enhance student performance. Professional Service: Editorial and NSF panel roles Service on Duke’s academic committees International conference participation

Janusz Adamusمشاهده پروفایل

استاد

• Analytic Geometry
• Local Algebra
• Semialgebraic Geometry
• +۳ مورد دیگر

Janusz Adamus is a Professor in the Department of Mathematics at The University of Western Ontario. He holds a Ph.D. from the University of Toronto (2003) and a Habilitation from Jagiellonian University (2013). His research focuses on analytic geometry, semialgebraic/subanalytic geometry, and discrete mathematics, with notable contributions to arc-analytic functions and cycle structure in bipartite graphs. He has supervised multiple graduate students and holds an NSERC Discovery Grant for his work on analytic mappings. Education: Ph.D., University of Toronto, 2003 Habilitation, Jagiellonian University, 2013 Research Interests include algebraic aspects of analytic mappings, subanalytic set properties, and extremal graph theory. His work bridges algebraic, topological, and transcendental methods in geometry. Recent publications explore arc-symmetric sets, Hamiltonian cycles in bipartite digraphs, and flatness criteria in algebraic geometry. He has received awards including the Malcolm S. Robertson Prize and NSERC Fellowships. Advising and grants include mentoring over a dozen graduate students and securing NSERC funding. His teaching includes advanced courses like Real Analysis I and Complex Analytic Geometry.

Peihan Miaoمشاهده پروفایل

استادیار

• Cryptography
• Security
• Theoretical Computer Science
• +۳ مورد دیگر

Peihan Miao is an Assistant Professor in the Department of Computer Science at Brown University, affiliated with the Theory Group. She holds a PhD from UC Berkeley (2019) under Sanjam Garg and a BS from Shanghai Jiao Tong University. Prior to Brown, she was at the University of Illinois Chicago (2020–2022) and Visa Research (2019–2020). Her research focuses on cryptography and security, particularly secure multi-party computation (MPC), with applications in genomics and privacy-preserving machine learning. She has received NSF, Meta, Google, and Amazon awards. Her work bridges theoretical foundations and practical implementations, addressing challenges in private set intersection (PSI), updatable encryption, and federated learning. Teaching includes courses on cryptography and secure computation at Brown and UIC. She mentors PhD students and postdocs, leading collaborative projects in privacy-enhancing technologies. Her lab explores MPC protocols, privacy-preserving techniques, and interdisciplinary applications in bioinformatics. Key awards include the NSF CAREER Award and Google/Amazon Research Scholarships. Grants support projects like privateQTL for genomic data analysis and secure PSI protocols. Her service includes program committees for CRYPTO, TCC, and ASIACRYPT.

Gu Wangمشاهده پروفایل

دانشیار

• Mathematical Finance
• Stochastic Analysis
• Optimal Investment Strategies
• +۳ مورد دیگر

Gu Wang is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI). He holds a BS from Peking University (2007), MS from Boston University (2010), and PhD in Mathematics from Boston University (2013). Prior to WPI, he served as a Postdoctoral Assistant Professor at the University of Michigan. Education : BS Peking University (2007), MS/Boston University (2010), PhD Boston University (2013) His research focuses on Mathematical Finance and Stochastic Analysis , emphasizing optimal investment/consumption policies, stochastic control, and market dynamics. Recent work explores fund manager competition, commodity investor strategies, and variable annuity fee structures. His articles often bridge theoretical models with practical financial applications. Teaching interests include financial/actuarial mathematics courses, with a focus on mentoring students in MQP projects, master's, and doctoral theses. He actively engages students in research projects across financial and actuarial domains. No specific scientific awards are listed, but his tenure and promotions indicate sustained academic contributions. His advising and grants involve student-led projects in applied finance and actuarial science. No dedicated labs or teams are mentioned, though collaborations likely occur within his department and university.

Qingshuo Songمشاهده پروفایل

دانشیار

• Stochastic Control Theory
• Mathematical Finance
• Probability Theory
• +۴ مورد دیگر

Qingshuo Song is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), maintaining office SH 314 and contactable at +1 (508) 8315000 x6273. His academic journey includes a BSc from Nankai University, and MA and PhD in Mathematics from Wayne State University (2006). Prior to joining WPI in 2019, he held significant academic positions including Associate Professor at City University of Hong Kong (2010-2018), and postdoctoral positions at the University of Michigan (2009) and University of Southern California (2006-2009). His research focuses on Stochastic Control Theory, which bridges probability theory and PDE analysis, with substantial applications in mathematical finance and engineering problems. Dr. Song's recent scholarly output demonstrates consistent research activity through 2025, with primary focus on Mean Field Game/Control theory. His work explores connections between Mean Field Games and Navier-Stokes equations, develops numerical methods for McKean-Vlasov stochastic differential equations, and examines long-time behaviors of stochastic control systems. His publications span optimization and control, probability theory, numerical analysis, and mathematical finance, reflecting an interdisciplinary approach that connects theoretical mathematics with practical applications. His research has received support from organizations including the Simons Foundation as acknowledged in his publications. Dr. Song maintains an active professional presence through arXiv, Google Scholar, ORCID (0000-0002-9289-7462), and GitHub repositories that include educational materials for courses like MA2210 (Mathematical Methods in Decision Making) and various computational projects related to machine learning and stochastic processes.

Prashant Nalini Vasudevanمشاهده پروفایل

استادیار

• Cryptography
• Complexity Theory
• Theoretical Computer Science
• +۲ مورد دیگر

Prashant Nalini Vasudevan serves as an Assistant Professor and NUS Presidential Young Professor in the Department of Computer Science at the National University of Singapore's Faculty of Computing. His research focuses on the theoretical foundations of cryptography and complexity theory, with particular emphasis on identifying computational hardness assumptions that can support cryptographic constructions. Dr. Vasudevan's research interests span Cryptography , Complexity Theory , and Theoretical Computer Science , with specific focus on statistical zero-knowledge proofs, fine-grained cryptographic primitives, and the intersection of cryptography with data privacy regulations. His work explores how computational hardness can be leveraged to build secure systems while addressing modern privacy concerns such as the right to be forgotten. Analysis of his recent publications reveals a strong trend toward statistical zero-knowledge systems, batch verification techniques, and formal approaches to data deletion. His research bridges theoretical computer science with practical privacy concerns, particularly in the context of machine learning systems and regulatory compliance. The work demonstrates increasing sophistication in cryptographic protocols while maintaining rigorous security proofs. NUS Presidential Young Professorship (2021-2026): Computational Hardness Assumptions and the Foundations of Cryptography NRF Fellowship (2022-2027): Fine-Grained Cryptography Dr. Vasudevan actively mentors multiple PhD students including Haoxing Lin, Yunqi Li, and Kel Zin Tan, along with postdoctoral researcher Rohit Chatterjee. His research group focuses on theoretical cryptography with applications to privacy-preserving systems. Current projects include developing new cryptographic primitives based on fine-grained hardness assumptions and exploring the theoretical foundations of data deletion in machine learning contexts.

Hu Fuمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithmic Game Theory
• Computational Economics
• +۳ مورد دیگر

Hu Fu is an Associate Professor at the Institute for Theoretical Computer Science, Shanghai University of Finance and Economics. Previously, he held positions as Assistant Professor at the University of British Columbia (2016–2020) and postdoctoral researcher at Microsoft Research New England and Caltech. He earned his PhD in Computer Science from Cornell University under Robert Kleinberg. His research focuses on algorithmic game theory, computational economics, and theoretical computer science, emphasizing computational approaches to economic problems. He has contributed to mechanism design, auction theory, online algorithms, and optimization. His work bridges computer science and economics, addressing challenges in market design, resource allocation, and decentralized systems. Teaching highlights include courses on algorithms, big data structures, and mechanism design at both UBC and Shanghai University. He has organized workshops (e.g., First Workshop on Algorithmic Game Theory and Data Science) and served in roles like PC co-chair for WINE'21 and editor for SIGecom Exchanges. Notable awards include the Best Paper Award at AAMAS'24 and recognition for papers on data-driven inventory policies and machine learning in operations. His research has been published in top venues like EC, STOC, NeurIPS, and Operations Research.

• Geometry in valued fields
• Arithmetic in valued fields
• Model-theoretic methods
• +۵ مورد دیگر

Prof. Halupczok is a Professor at the Chair of Algebra and Number Theory within the Faculty of Mathematics and Natural Sciences at Heinrich Heine University Düsseldorf (HHU). His research focuses on advanced mathematical areas combining geometry, arithmetic, and model theory in valued fields. Key areas include singularities, stratifications, motivic integration, and representation theory of reductive groups over local fields. He also contributes to combinatorics, specifically polyomino games, zero-sum problems, and covering codes. Additional expertise includes model theory of (pseudo-)finite fields and representation theory of real reductive groups and symmetric varieties. Education details are not explicitly provided in the text. His current role emphasizes interdisciplinary research bridging algebra, geometry, and logic. No specific grants, awards, or academic advising details are mentioned here.

Eduard Paul ROTENSTEINمشاهده پروفایل

دانشیار

• Stochastic differential equations
• Stochastic variational inequalities
• Partial differential equations
• +۵ مورد دیگر

Eduard Paul Rotenstein is an Associate Professor at the Faculty of Mathematics of the Alexandru Ioan Cuza University of Iași, Romania. He holds a Ph.D. in Mathematics and serves as a Ph.D. supervisor at the School of Advanced Studies of the Romanian Academy. His research focuses on stochastic processes, differential equations, and their applications in finance and control theory. Education: B.Sc. (1995), M.Sc. in Partial Differential Equations (2002), and a Ph.D. (2020 Habilitation) from the University of Iași. Additional training includes CISCO courses and specialized programs in financial mathematics. Research interests include stochastic differential equations, variational inequalities, and financial market models. His work bridges theoretical mathematics with applications in risk management, stochastic control, and gene network dynamics. Key contributions involve backward stochastic differential equations (BSDEs), obstacle problems, and numerical schemes for multivalued systems. Recent publications explore topics such as unbounded subdifferential operators, parabolic SDEs with Hölder continuity, and controllability of Markov-switch systems. He has participated in over 30 international conferences, contributing to workshops on stochastic processes, mathematical finance, and control systems. Grants include leadership in Marie Curie ITN projects and participation in national initiatives on stochastic systems and invariance. His teaching spans courses in probability, statistics, and financial mathematics at both undergraduate and graduate levels.

• Computational Complexity
• Cryptography
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.

• Algorithmic Game Theory
• Stochastic Games
• Distributed Systems
• +۵ مورد دیگر

Hugo Gimbert is a Researcher at the LaBRI (Laboratoire Bordelais de Recherche en Informatique) within the Méthodes Formelles Team . His work bridges computational theory and practical applications, focusing on algorithmic game theory, stochastic processes, and formal methods in computer science. Research interests include: Decidability problems in distributed and stochastic games Partial observation in multi-player systems Cooperation and competition in bridging scenarios Social choice theory implementation Precision farming automation Probabilistic logics and humanoid robot control via MDPs Advising roles: PhD advisor for Soumyajit Paul, Edon Kelmendi, Quentin Rouxel, and Ludovic Hofer PhD co-advisor for Simon Mauras Former advisor for Youssouf Oualhadj Key projects: Stamina - Stabilisation monoids and regular language analysis Marmotte - CNRS committee digitalization platform Rhoban System - Real-time robotics for agriculture and soccer Parcoursup - Algorithmic college admission system for French government

• Number Theory
• Analytic Number Theory
• Prime Number Theory
• +۳ مورد دیگر

Alessandro Zaccagnini is an Associate Professor of Mathematical Analysis at the Department of Mathematical, Physical, and Computer Sciences, University of Parma. His academic career spans over three decades, with significant contributions to number theory and its applications. He has held teaching positions across multiple programs including both Mathematics and Computer Science degrees at various academic levels. His educational background includes a Mathematics degree earned in 1989 followed by a PhD in 1994 from the University of Turin under the supervision of Alberto Perelli. His doctoral research established important results related to the Hardy-Littlewood conjectures in additive number theory. Zaccagnini's research primarily focuses on analytic and additive number theory, with special emphasis on prime number distribution, Goldbach-type problems, and cryptographic applications. His work often employs the circle method of Hardy, Ramanujan, and Littlewood to tackle challenging problems in prime number theory. He has made significant contributions to understanding the distribution of primes in short intervals and the relationship between the Selberg integral and density theorems for zeta function zeros. His recent publications demonstrate a continued focus on additive problems involving prime powers, with particular attention to Cesàro averages and asymptotic formulas in short intervals. The research shows a strong pattern of collaboration, especially with A. Perelli and A. Languasco, across multiple decades. His scholarly output spans both highly technical mathematical papers and more accessible works on cryptography and prime number applications for general audiences. As an educator, Zaccagnini has taught a diverse range of courses including Calculus, Cryptography, Number Theory, and Probability across both undergraduate and graduate programs at the University of Parma. His teaching portfolio reflects his dual expertise in pure mathematics and practical applications in cryptography. Zaccagnini maintains an active research program with publications extending to 2025, demonstrating his ongoing contribution to the field of number theory. His work bridges theoretical mathematics with practical applications, particularly in cryptography, making him a significant figure in contemporary Italian mathematics.

• Optimization
• Product Design
• Configuration Design
• +۴ مورد دیگر

Yi Ren serves as an Associate Professor in the Department of Aerospace and Mechanical Engineering within the School for Engineering of Matter, Transport and Energy at Arizona State University. He leads the Design Informatics Laboratory, pioneering computational and data-driven methodologies to transform engineering and industrial design processes. His academic foundation includes: Ph.D. in Mechanical Engineering from the University of Michigan (2011) M.S. in Mechanical Engineering from the University of Michigan (2009) B.S. in Automotive Engineering from Tsinghua University, China (2007) Ren's research integrates optimization , product/configuration design , human-computer interaction , and machine learning to develop human-AI collaborative design frameworks. His work emphasizes differential games , reinforcement learning , and data-driven design augmentation , with applications spanning autonomous systems, swarm robotics, and secure AI development. This interdisciplinary approach bridges theoretical advances with real-world engineering challenges. Analysis of his recent publications reveals a pronounced focus on solving complex multi-agent decision problems using game-theoretic approaches combined with deep learning. His work systematically addresses state-constrained differential games, asymmetric information settings, and human feedback integration in control systems—primarily advancing autonomous driving safety, swarm coordination, and generative model security. Ren actively mentors graduate students through dissertation and research courses (MAE 792/799) and has secured research funding including a University of Michigan project on "Creativity through Collaborative Human-Machine Interactions" (2015-2016). His Design Informatics Laboratory at ASU develops computational tools that enhance engineering design through human-AI collaboration, with current emphasis on game-theoretic optimization, adversarial robustness, and physical interaction modeling.