جست‌وجوی استاد و پژوهشگر

Jeffrey Negreaمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Artificial Intelligence
• +۳ مورد دیگر

Jeffrey Negrea is an Assistant Professor at the University of Waterloo. His research focuses on foundational aspects of machine learning, statistics, and optimization, with a particular emphasis on stochastic algorithms, Markov chain theory, and adversarial analysis. His work bridges theoretical computer science and applied statistics, addressing challenges in algorithmic convergence, generalization bounds, and scalable inference methods. Key research directions include developing robust methods for online learning, analyzing stochastic iterative algorithms through scaling limits, and exploring the interplay between information theory and machine learning generalization. He has contributed to advancements in Thompson Sampling for adversarial environments, minimax regret optimization, and the theoretical underpinnings of MCMC and approximate inference techniques. His publications span topics from adversarial analysis of Thompson Sampling to quantitative error bounds for stochastic processes. While no formal awards or grants are explicitly listed in the provided data, his active publication record reflects ongoing contributions to computational statistics and machine learning theory. No student advising or lab affiliations are mentioned in the available information.

Galen Reevesمشاهده پروفایل

استادیار

• Information Theory
• Machine Learning
• Statistical Signal Processing
• +۳ مورد دیگر

Galen Reeves is an Assistant Professor at Duke University with a joint appointment in the Department of Electrical and Computer Engineering and Department of Statistical Science since Fall 2013, reflecting his interdisciplinary expertise bridging engineering and mathematical sciences. His research establishes rigorous theoretical frameworks at the intersection of information theory, machine learning, and statistical signal processing, focusing on fundamental limits in high-dimensional inference problems. His academic background features elite training across top institutions: PhD in Electrical Engineering and Computer Sciences, University of California, Berkeley (2011) MS in Electrical Engineering, University of California, Berkeley (2007) BS in Electrical and Computer Engineering, Cornell University (2005) Reeves' research centers on mathematical foundations of data science, with seminal contributions to compressed sensing, tensor estimation, and coding theory. He investigates information-theoretic bounds for estimation problems, develops efficient algorithms like approximate message passing, and analyzes generative AI model behavior under recursive training conditions. His work demonstrates how statistical physics approaches solve complex problems in communication theory and high-dimensional statistics. Analysis of his 2021-2025 publications reveals three dominant trends: breakthroughs in channel capacity using Reed-Muller codes, theoretical analysis of generative models and diffusion sampling, and fundamental limits in tensor/matrix estimation. These works consistently integrate information theory with machine learning, emphasizing scalability challenges in high dimensions and algorithmic robustness under heteroskedasticity. His scientific recognition includes: NSF VIGRE fellowship supporting postdoctoral research at Stanford University (2011-2013) NSF CAREER award (2018) for Theoretical Foundations for Probabilistic Models with Dense Random Matrices While no specific students are documented in the source text, his faculty position entails graduate mentorship in both ECE and Statistical Science departments. Research funding primarily stems from the NSF CAREER grant advancing probabilistic modeling, complemented by earlier fellowship support. His collaborations span Stanford University, EPFL, TU Delft, and Microsoft Research. Though no dedicated lab is mentioned, his joint appointment fosters cross-departmental research at Duke, particularly in projects like 'Modeling Traffic with Self Driving Cars' which applies statistical learning to autonomous systems. His work maintains strong ties to industry through past Microsoft Research internships and ongoing computational applications in communications and AI.

Justin Schroederمشاهده پروفایل

استادیار

• Combinatorics
• Graph Theory
• Design Theory
• +۷ مورد دیگر

Justin Schroeder is an Assistant Professor in the Department of Mathematics at Dakota State University, within the College of Arts & Sciences. He holds a Ph.D. in Mathematics from Vanderbilt University (2012) and a B.A. in Mathematics from Carthage College (2007). Ph.D. in Mathematics, Vanderbilt University, 2012 B.A. in Mathematics, Carthage College, 2007 His research focuses on combinatorics and graph theory, with particular emphasis on prime labelings , graph embeddings , Steiner triple systems , and Latin squares . His work combines structural graph theory with number-theoretic and algebraic methods, often exploring labelings and symmetries in discrete structures. The recent publications show a consistent trend in combinatorial design theory and topological graph theory , with several papers on prime labelings of graph families and self-dual embeddings in pseudosurfaces. His collaborations often involve finite field constructions and extremal problems in labeling. The arXiv preprint on rough numbers indicates an interest in analytic number theory as well. Notable scientific awards include: Winner, 2019 Gen Cant Game Design Contest (Polyhedral Park Planner) Bjarni Jónsson Prize for Research, Vanderbilt University, 2012 B.F. Bryant Award for Excellence in Teaching, Vanderbilt University, 2012 AMS Graduate Student Travel Grant, 2012 Graduate Student Summer Research Award, Vanderbilt University, 2011 Dr. Schroeder has advised research projects and co-authored papers with collaborators such as S.A. Schluchter and E. Rarity. He has presented his work internationally at the Macedonian Workshop on Graph Theory and Applications. While no formal grant funding is listed in the text, his research awards and travel grants during graduate studies reflect early recognition of scholarly excellence. He is actively engaged in both theoretical research and academic dissemination through publications and conference presentations. There is no mention of a specific research lab or team, but his collaborative publications suggest participation in a network of combinatorics researchers focused on graph labeling and design theory.

Wolfgang Schiefمشاهده پروفایل

استاد

• Integrable Systems
• Soliton Theory
• Differential Geometry
• +۱۱ مورد دیگر

Professor Wolfgang Schief is a faculty member at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics . His research bridges integrable systems , nonlinear physics , and geometric analysis , with a focus on differential and discrete differential geometry and their applications in continuum mechanics , general relativity , and soliton theory . Research Interests His work centers on the interplay between integrable systems and geometric structures , particularly in nonlinear partial differential equations and their discrete analogs. Key areas include: Integrable discretizations of classical and modern geometric problems Backlund and Darboux transformations for physical systems Nonlinear elasticity and magnetohydrodynamics Self-dual Einstein spaces and heavenly equations Log-aesthetic curves and geometric design Recent Publications Recent studies highlight multi-dimensional integrability in affine manifolds, canonical reductions of TED equations, and geometric frameworks for fiber-reinforced fluids and relativistic systems. His work often reveals connections between soliton theory , Lagrangian mechanics , and discrete geometry . Scientific Awards Queen Elizabeth II Research Fellow (UNSW, 1999–2004) Academic Leadership Currently Professor at UNSW (since 2010), Schief has held roles including Senior Lecturer , Associate Professor (2005–2006), and Professor at TU Berlin (2006–2010). His career spans 2+1-dimensional integrable systems and geometric applications in physics and mechanics.

• Complex Systems Science
• Causality Inference
• Complex Networks
• +۵ مورد دیگر

Abd AlRahman AlMomani is an Assistant Professor of Data Science and Mathematics at Embry-Riddle Aeronautical University's College of Arts and Sciences. He holds a BS in Mechatronics Engineering from Philadelphia University (Jordan), an MS in Applied Mathematics, and a PhD in Electrical and Computer Engineering from Clarkson University. His career includes roles as a Research Associate at Clarkson's Center for Complex Systems Science and a Visiting Assistant Professor of Mathematics at Clarkson, with adjunct roles in Mechanical and Chemical Engineering. His research focuses on complex systems science, causality inference, and nonlinear dynamics, applying mathematical frameworks to real-world challenges like climate modeling and machine learning. Education: B.S. in Mechatronics Engineering, Philadelphia University, Jordan M.S. in Applied Mathematics, Clarkson University Ph.D. in Electrical and Computer Engineering, Clarkson University Research Interests: Dr. AlMomani specializes in complex systems, causality inference, and data-driven approaches for analyzing interconnected systems. His work addresses challenges such as system identification, network structure recovery, and trustworthy machine learning. Key areas include entropy-based methodologies, early warning signals for environmental tipping points, and applications in engineering, climate science, and epidemiology. Grants & Awards: EAGER Grant (2023): North American Monsoon Prediction Using Causality Informed Machine Learning Courses Taught: DS 150: Data Science I DS 411: Data Visualization MA 243: Calculus and Analytical Geometry III MA 335: Introduction to Linear and Abstract Algebra Labs & Collaborations: His work leverages interdisciplinary collaborations, particularly in designing graduate courses on engineering analysis and mathematical foundations of data science. Tools like ERFit (MATLAB package) exemplify his focus on practical, data-driven solutions.

Matthew Fickusمشاهده پروفایل

استاد

• Applied Harmonic Analysis
• Frame Theory
• Compressed Sensing
• +۷ مورد دیگر

Matthew Fickus is a Professor of Mathematics at the Air Force Institute of Technology (AFIT), within the Graduate School of Engineering and Management. His research lies at the intersection of applied harmonic analysis, frame theory, and mathematical signal processing, with a strong focus on equiangular tight frames, fusion frames, and combinatorial constructions in coding and geometry. His educational background includes a PhD in Mathematics from the University of Maryland, College Park (2001), an MS in Applied Mathematics, and a BS in Mathematics, both from the University of Maryland, Baltimore County. Dr. Fickus's research interests span Applied Harmonic Analysis , Frame Theory , Compressed Sensing , Signal Processing , and Combinatorial Design . He investigates structured mathematical frameworks for optimal signal representation, particularly focusing on equiangular tight frames and their algebraic, geometric, and combinatorial properties. His work often bridges pure mathematics with engineering applications in imaging, coding, and data science. The trend in his recent publications reveals a deep and sustained exploration of equiangular tight frames, Grassmannian packings, and their connections to finite geometry, difference sets, and algebraic structures. His research integrates tools from linear algebra, functional analysis, and discrete mathematics to solve problems in optimal frame design and signal reconstruction. These works are published in high-impact journals such as Applied and Computational Harmonic Analysis , IEEE Transactions on Information Theory , and Linear Algebra and its Applications . While no scientific awards are explicitly listed in the provided text, his extensive publication record and editorial contributions (e.g., book chapters in Finite Frames: Theory and Applications ) reflect significant recognition in his field. Dr. Fickus has mentored numerous students and collaborators, though specific names are not listed. He has received research support likely through AFIT and Air Force funding, enabling sustained contributions to mathematical engineering. His work frequently involves interdisciplinary teams, particularly in biomedical image analysis and signal processing applications. He is affiliated with the Department of Mathematics at AFIT, where he conducts research in mathematical signal processing and frame theory, contributing to both theoretical advances and practical implementations in defense and engineering contexts.

• Speech Recognition
• Neural Networks
• Cryptography
• +۵ مورد دیگر

Dr. Marcin Płonkowski is an Assistant Professor in the Department of Applied Computer Science at the Faculty of Natural and Technical Sciences, John Paul II Catholic University of Lublin. His work bridges Computer Science , Neural Networks , and Cryptography . Institution: John Paul II Catholic University of Lublin School: Faculty of Natural and Technical Sciences Department: Applied Computer Science Dr. Płonkowski’s research spans speech recognition , neurocryptography , and information security . He has extensively explored the application of non-traditional mathematical structures like dual numbers , quaternions , and split-complex numbers in cryptographic protocols and neural network optimization. His scientific publications focus on speech recognition systems (e.g., CMU Sphinx-III), chaos theory in hash functions, and algebraic structures in neural cryptography. Key journals include Przegląd Elektrotechniczny and International Journal of Speech Technology . Scientific Awards and Patents Patent application for a neural network-based secure communication device. Contractor for EU-funded project: Qualified staff as a chance for a better tomorrow .

• Concurrency
• Program Correctness
• Theorem Proving
• +۳ مورد دیگر

Prof. dr. Wim H. Hesselink is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Fundamental Computing Science department within the Bernoulli Institute. His research focuses on concurrency, program correctness, theorem proving, and formal methods in computer science and mathematics. He holds an office at Nijenborgh 9, room 5161.0374, and can be reached at w.h.hesselink@rug.nl. Key research contributions include lock algorithms (e.g., MCSH, Triangle), formal verification of distributed systems, and theoretical work on automata (Büchi) and algebraic structures (Lie algebras). His publications span journals like Formal Aspects of Computing and ACM Transactions on Parallel Computing . Education : Not explicitly detailed in text, but his work indicates expertise in advanced computer science and mathematics. Awards : None listed in the provided text. Grants : No specific grants mentioned. His recent work emphasizes high-performance concurrency solutions and algorithm verification, with applications in distributed systems and lock-free computing.

Mikhail Freerمشاهده پروفایل

مدرس

• Economic Theory
• Game Theory
• Experimental Economics
• +۳ مورد دیگر

Dr. Mikhail Freer is a Lecturer in the Department of Economics at the University of Essex. His teaching focuses on advanced topics such as Market Structure and Strategic Behaviour (EC366) and Topics in Economic Theory (EC993). He currently supervises doctoral student Yi Shi, whose thesis examines conditional cooperation in economics. Dr. Freer's research interests span economic theory, game theory, experimental economics, and behavioral economics. His work often explores revealed preference analysis, strategic behavior in markets, and welfare economics. Notable areas include the study of auction credibility, marital stability under mutual consent divorce, and the algebraic approach to preference reconstruction. His recent publications (2021–2024) reflect a focus on dynamic game strategies, utility representation theorems, and experimental studies of collective behavior. He has contributed to understanding limited attention in decision-making and prize-probability trade-offs in utility maximization. Dr. Freer holds a PhD in Economics and is affiliated with the University of Essex's Colchester Campus. His academic support hours are by appointment, and he can be contacted at m.freer@essex.ac.uk.

Flip van Spaendonckمشاهده پروفایل

پژوهشگر

• Model-Driven Engineering
• Model-Based Testing
• Mutual Exclusion
• +۵ مورد دیگر

Flip van Spaendonck is a Researcher at the School of Mathematics and Computer Science at Eindhoven University of Technology. His work focuses on Model-Driven Engineering , Model-Based Testing , and Mutual Exclusion Protocols , with a particular emphasis on improving State Space Generation and Conformance Relations . Recent research outputs include contributions to formal verification techniques, parallel computing frameworks, and term library design. Key subfields of interest span Mutual Exclusion Protocols Model Checking Parallel Systems Context-Free Grammars Boolean Equations Code Transformation . Publications highlight collaborations with J.F. Groote, M. Laveaux, and T.A.C. Willemse, addressing challenges in parallel model checking and state space optimization . No scientific awards or student advising details are explicitly mentioned in the provided data.

Etienne Vougaمشاهده پروفایل

دانشیار

• Physical simulation
• Geometry processing
• Discrete differential geometry
• +۴ مورد دیگر

Etienne Vouga is an Associate Professor in the Department of Computer Science and the Institute for Computational Engineering and Sciences (ICES) at the University of Texas at Austin. His interdisciplinary research spans physical simulation, geometry processing, and computer graphics, with applications ranging from special effects in films like The Hobbit and Tangled to Artec's Shapify 3D portrait service. Education: PhD from Columbia Computer Graphics Group Postdoctoral research at Harvard SEAS Professor Vouga's research focuses on physical simulation of thin, elastic materials like cloth, hair, and paper, as they deform and collide. He has made significant contributions to discrete differential geometry, developing algorithms that better understand how geometric properties affect physical behavior of deformable objects. His work bridges computer graphics, computational mechanics, and scientific computing, with applications in special effects, 3D printing, architectural design, and biological modeling. He has pioneered approaches in inverse problems where desired outcomes drive the design process rather than simply simulating physical phenomena. Award Highlights: Best Paper Award at GECCO (2017) for Evolutionary Decomposition for 3D Printing Professor Vouga teaches Computer Graphics: Honors (CS 378H/CS 384G) and Physical Simulation (CS 395T) graduate courses every spring semester. He also serves as head coach of UT's competitive programming teams. His research group has developed several important software tools including Discrete Shell Code for shell simulation and Asynchronous Contact Mechanics Code for robust collision handling. While he is not actively expanding his research group significantly, he remains open to working with exceptional graduate students whose research interests align closely with his work in physical simulation and geometry processing.

Nikolaj Popovمشاهده پروفایل

استاد پژوهشی

• Program Verification
• Symbolic Computation
• Formal Methods
• +۳ مورد دیگر

Nikolaj Popov is a Research Professor at the Research Institute for Symbolic Computation (RISC) of Johannes Kepler University, Linz, Austria. His work focuses on program verification, formal methods, and automated reasoning, particularly within the Theorema system. He specializes in verifying functional and recursive programs, combining symbolic computation with logical techniques to ensure correctness and termination. Education: PhD in Computer Science, RISC, Johannes Kepler University (2008) Research Interests: Popov’s research bridges theoretical computer science and practical applications, emphasizing automated theorem proving, program analysis, and formal verification. He develops methods for verifying complex program structures like mutual recursion and nested recursion, leveraging computer algebra systems. His work also explores the integration of formal methods into software engineering workflows. Key Contributions: Popov has co-authored foundational papers on verification condition generation, termination proofs, and automated debugging frameworks. His collaborations with Tudor Jebelean and others have advanced Theorema’s capabilities in handling functional programs and bridging logical and algebraic methodologies. Labs/Teams: Core member of RISC, contributing to its mission in symbolic computation and mathematical theory exploration.

Padmanabhan K. Aravindمشاهده پروفایل

استاد

• Quantum Information Theory
• Foundations of Quantum Mechanics
• Bell's Theorem
• +۳ مورد دیگر

Padmanabhan K. Aravind is a Professor of Physics at Worcester Polytechnic Institute (WPI). He holds a B.Sc. and M.Sc. from Delhi University, India, and a Ph.D. from Northwestern University, USA. His research focuses on the foundations of quantum mechanics, quantum information theory, and Bell's theorem, with notable contributions to quantum contextuality and entanglement. He is a Fellow of the Institute of Physics (UK). Education: B.Sc. Physics, St. Stephen's College, Delhi University, India (1971) M.Sc. Physics, Delhi University, India (1973) Ph.D. Physics, Northwestern University, USA (1980) Research interests include quantum nonlocality, Kochen-Specker theorems, and applications of quantum information theory to cryptography and computing. His work bridges foundational questions with experimental validations, such as parity proofs and geometric interpretations of quantum states. Teaching highlights include courses on quantum mechanics and general relativity, alongside contributions to WPI's Frontiers program for high school students. He advises Major Qualifying Projects (MQPs) and Interdisciplinary Projects (IQPs), emphasizing science communication and outreach. Notable awards include the Institute of Physics Fellowship, recognizing his contributions to quantum foundations. His publications span over 30 years, addressing topics like Bell’s theorem, quantum contextuality, and entanglement structures in high-dimensional systems.

• Complex Systems Theory
• Dynamical Systems Theory
• Complex Networks
• +۷ مورد دیگر

Michael Small is a Professor and the CSIRO-UWA Chair of Complex Systems at the University of Western Australia (UWA), holding dual appointments with UWA’s Faculty of Engineering and Mathematical Sciences and CSIRO’s Mineral Resources division. As Director of the UWA Data Institute, he leads research in complex systems theory and dynamical systems, with applications to health, mining, and energy infrastructure. His academic roles include Deputy Editor-in-Chief of Chaos and Editor of Physica A . He holds a PhD in Applied Mathematics (UWA, 1998) and a BSc(Hons) in Pure Mathematics (UWA, 1994). His research focuses on mathematical modeling of complex systems, including disease transmission networks, mental health dynamics, and industrial process optimization. Notable projects include the Transforming Indigenous Mental Health & Wellbeing initiative and collaborations with mining giants like BHP and Rio Tinto. His awards include the V. Afraimovich Award (2022) and UWA Senior Research Award (2016). Recent work spans reservoir computing, network science, and epidemic modeling, with 354 publications and leadership in 18 grants. His interdisciplinary approach bridges academia and industry, addressing challenges in data-driven decision-making and sustainability.

Jan Friso Grooteمشاهده پروفایل

استاد

• Formal Methods
• Verification Technology
• Software Engineering
• +۱۲ مورد دیگر

Jan Friso Groote is a Full Professor and Chair of the Formal System Analysis group in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He also holds professorial roles in the EAISI Foundational and EAISI High Tech Systems institutes. Since 2016, he has been working part-time at ASML, contributing his expertise in formal verification to industrial applications. Education: Born in 1965, studied Computer Science at Twente University of Technology (now University of Twente), 1983–1988. PhD in 1991 from the University of Amsterdam with thesis 'Process algebra and structured operational semantics', based on research at CWI (Centrum Wiskunde en Informatica). Jan Friso Groote is a leading researcher in formal methods and software verification. His work focuses on enabling the development of flawless software through rigorous formal analysis. Key research areas include structural operational semantics, model checking, branching bisimulation, protocol verification, and the development of the mCRL2 toolset. His current goal is to integrate formal techniques into complete software system design, improving both development speed and quality. His research has demonstrated that formal methods can reduce development time by a factor of three and increase quality tenfold, with potential for zero-defect software. His recent publications demonstrate sustained contributions in formal verification, including work on mutual exclusion algorithms, industrial control system modeling, probabilistic systems, and efficient bisimulation algorithms. The articles span topics such as tunnel control systems, simulation lower bounds, and formal methods for critical systems, reflecting both theoretical depth and practical application. Scientific Awards: Best Paper Award FACS 2018 FMICS-AVoCS Best Paper Award (2017) Jan Friso Groote has held significant leadership roles in education, including Director of Education for Computer Science (2000–2010) and for multiple bachelor’s and master’s programs. He has advised numerous researchers and supervised a large body of research output (over 320 publications). He leads the Formal System Analysis group and has been involved in projects such as 'Composable Embedded Systems for Healthcare'. His work bridges academia and industry, particularly through collaborations with ASML and Rijkswaterstaat, and he has been a visiting researcher at institutions across Europe and China. He is a key contributor to the mCRL2 toolset, which supports modeling and verification of software behavior with data, time, and probabilities. His research fingerprints highlight strong expertise in model checking, transition systems, software design, and process algebra. He teaches courses such as System Validation, Embedded Software, and Capita Selecta in Formal System Analysis.