جست‌وجوی استاد و پژوهشگر

• High Frequency Superconductivity
• Quantum Physics
• Nonlinear Dynamics
• +۴ مورد دیگر

Steven M. Anlage is a Professor in the Physics Department and Electrical & Computer Engineering Department at the University of Maryland, College Park. His research focuses on High Frequency Superconductivity, Quantum Physics, Nonlinear Dynamics, and Chaos Theory, with affiliations including the Maryland Quantum Materials Center (MQMC), Materials Research Science and Engineering Center (MRSEC), and the Institute for Research in Electronics and Applied Physics (IREAP). He leads the Anlage Research Group, which explores topics such as superconducting metamaterials, microwave microscopy, and wave-chaotic systems. His work spans experimental and theoretical studies, including investigations into topological insulators, vortex dynamics in superconductors, and applications of machine learning in wave physics. Anlage has received grants such as the MURI grant on 'Taming Chaotic Systems Using AI' and collaborates with institutions like the Kharkiv Quantum Seminar. He has advised students like Chung-Yang Wang (2025 Ph.D.), Jingnan Cai (2024 Ph.D.), and Bakhrom Oripov, who contributed to groundbreaking projects such as a superconducting single-photon camera. His experimental facilities include advanced microwave measurement systems, such as Vector Network Analyzers and programmable metasurfaces, enabling studies of complex electromagnetic environments. Recent research highlights include discoveries in UTe₂ superconductivity, nonlinear wave phenomena, and reservoir computing using chaotic systems. Anlage's contributions have been recognized through publications in journals like Physical Review Letters and collaborations with industry and academic partners. His lab continues to push boundaries in nanophysics, quantum materials, and applied electromagnetism.

Alexandre Staufferمشاهده پروفایل

استاد

• Discrete Probability
• Interacting Particle Systems
• Markov Chain Mixing Times
• +۳ مورد دیگر

Alexandre Stauffer is a Professor in Probability at the Department of Mathematics, King's College London. He holds a PhD from the University of California, Berkeley (2011), with postdoctoral positions at Microsoft Research and Università Roma Tre. He previously worked at the University of Bath (2013–2024) before joining King’s in 2024. His research focuses on discrete probability, interacting particle systems, and Markov chain mixing times. Key areas include first passage percolation, random walks, and phase transitions in stochastic systems. Stauffer has organized workshops on topics like strongly correlated random processes and random interacting systems. He secured grants such as the EPSRC Early Career Fellowship (2016–2022) and the Marie Curie Career Integration Grant (2013–2016). His work bridges theoretical probability with applications in statistical physics and computer science. Recent articles explore coexistence in competing percolation models and mixing times in dynamic systems. Stauffer’s research emphasizes multi-scale analysis and non-equilibrium phenomena in stochastic processes. Grants and organizational involvement highlight his leadership in probability theory. His publications span top journals like the Annals of Probability and Inventiones Mathematicae, reflecting rigorous mathematical contributions to understanding complex stochastic systems.

• Applied Probability
• Stochastic Processes
• Queueing Theory
• +۷ مورد دیگر

Bert Zwart is a Professor and Scientific Staff Member at Centrum Wiskunde & Informatica (CWI) in Amsterdam, where he leads the Stochastics group. He also holds a professorship by special appointment at Technische Universiteit Eindhoven (TU/e). His research spans applied probability, stochastic processes, queueing theory, and operations research, with applications in power systems, supply chains, and risk modeling. His research interests focus on extreme value theory , large deviations , asymptotic analysis of stochastic systems , and random graphs . He develops theoretical tools to analyze rare events and performance limits in complex systems such as fork-join queues, power grids, and stochastic networks. His work combines deep probabilistic insights with practical applications in engineering and operations management. Recent publications (2023–2025) reveal a strong focus on tail behavior in queueing systems, large deviations in heavy-tailed processes, and optimization in power networks. The articles span high-impact journals like Mathematics of Operations Research , Queueing Systems , and Random Structures & Algorithms , indicating sustained leadership in applied probability and stochastic modeling. His scientific awards include: Erlang Prize (2008) Dantzig Prize (2015) Best publication award, INFORMS Revenue Management & Pricing (2016) IBM Faculty Award (2008) ASML Prize (2002) Gijs de Leve Prize (2002) Applied Probability Trust Award (2001) Best paper, Performance conferentie, Namen, Belgium (2010) Zwart has secured significant research funding, including a Vici grant from NWO on rare events, multiple NWO TOP and STAR cluster grants, and funding for PhD positions and postdocs. He has advised numerous students (though not explicitly listed here) and collaborates widely with researchers such as Maria Vlasiou, Remco van der Hofstad, and Onno Boxma. He is actively involved in teaching, offering advanced courses like Stochastic Decision Theory and Asymptotic Methods in Queueing Theory . He leads the Stochastics group at CWI, a leading research unit in probability and operations research.

Anthony Dooleyمشاهده پروفایل

استاد

• Modern Analysis
• Harmonic Analysis
• Dynamical Systems
• +۲ مورد دیگر

Prof Anthony Dooley is a Professor and Head of the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS). He previously held positions at the University of Bath (UK) and UNSW, serving as Head of School and leading strategic roles. His research focuses on Modern Analysis, including harmonic analysis and dynamical systems, with over 90 peer-reviewed publications and continuous ARC grants for 20 years. He has supervised 18 PhD students and held visiting roles at institutions like Yale, Kyoto University, and the Mittag-Leffler Institute. Education: PhD from ANU; Diplôme des Études Approfondies from Paris 6. Awards: Fellow of the Australian Institute of Company Directors. Research interests span ergodic theory, Lie groups, and stochastic processes. Contributions include work on non-singular ergodic theorems, G-measures, and entropy in dynamical systems. Service roles: Board memberships at NIDA, Australian Graduate School of Management, and international research panels. Grants: Chief Investigator for the ARC Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS). Active in governance across academia and research institutions.

Matthias Fresacherمشاهده پروفایل

مدرس

• Abstract Algebra
• Semigroups and Monoids
• Diagram Monoids
• +۴ مورد دیگر

Matthias Fresacher is a PhD candidate in Pure Mathematics at Western Sydney University under Assoc. Prof. James East, researching diagram monoids and their generalizations. He holds a Master's and Bachelor's from the University of Adelaide, with an exchange at the National University of Singapore. His academic roles include Lecturer at Western Sydney, tutor at UTS, and Indigenous academic mentor at the University of Adelaide. Education: PhD (Pure Mathematics), Western Sydney University (2022–ongoing) MPhil (Pure Mathematics), University of Adelaide (2019–2021) Bachelor of Mathematical Sciences (Advanced), University of Adelaide (2016–2018) Exchange at National University of Singapore (2018) Research Interests: Abstract algebra, semigroups/monoids, diagram monoids, category theory, algorithms, and interdisciplinary collaborations. His publications span theoretical mathematics and applied computing, with recent focus on categorical representations and diagram monoid properties. He has received awards including the AustMS Bernhard Neumann Prize and AAG Gordon Preston Prize. Active in teaching, supervision, and academic leadership, he co-founded the Association of Australian Mathematics Students (AAMS) and serves on various committees.

• Business Process Management
• Process Mining
• Applied Data Analytics
• +۴ مورد دیگر

Prof. Jochen De Weerdt is an Associate Professor at the Department of Decision Sciences and Information Management, KU Leuven, affiliated with the Faculty of Economics and Business. He is part of the Information Systems Engineering Research Group (LIRIS) and a member of Leuven.AI. His research focuses on Business Process Management, Process Mining, IoT-integrated analytics, and data-driven decision-making. He teaches courses such as Business Information Systems and chairs the Postgraduate Studies in Big Data and Analytics. His professional activities include organizing international workshops (e.g., BPI'14 and BPI'13) and serving as a reviewer for top journals like Information Systems and Decision Support Systems . Academically, he holds a PhD (2012) and MSc/BSc degrees (2009/2007) in Business Economics from KU Leuven. His work bridges theory and practice, addressing challenges in process optimization, fraud detection, and cross-domain applications of machine learning. He has pioneered methods for IoT-enhanced predictive process monitoring and graph-based analytics in dynamic systems. Research interests span predictive process analytics, adversarial robustness in models, and geospatial data integration. Teaching emphasizes practical skills in business informatics and data science, with a focus on industry-relevant outcomes. He actively collaborates on projects funded by institutions like the Australian Research Council (ARC).

Sayan Dasمشاهده پروفایل

مدرس

• Probability Theory
• Statistical Physics
• Combinatorics
• +۱ مورد دیگر

Sayan Das is a Dickson Instructor in the Department of Mathematics at the University of Chicago. He received his PhD in Mathematics from Columbia University in 2023 under the supervision of Ivan Corwin, focusing on probability theory and its applications. His research explores stochastic processes, statistical physics, and combinatorial structures, with emphasis on KPZ universality, polymer models, and large deviation principles in random environments. Recent publications demonstrate consistent focus on stochastic analysis and mathematical physics, particularly examining scaling limits of polymer models and fluctuation behaviors in KPZ-type systems across diverse initial conditions and boundary effects.

• Experimental Mathematics
• Artificial Intelligence
• Legal aspects of technology
• +۴ مورد دیگر

Michael Ian Shamos is a Distinguished Career Professor at Carnegie Mellon University's School of Computer Science, with appointments in the Language Technologies Institute and Software and Societal Systems Department. His career spans academia, law, and technology entrepreneurship, with expertise in experimental mathematics, artificial intelligence, and legal aspects of technology. Dr. Shamos earned his educational credentials through an impressive multidisciplinary path: A.B. in Physics from Princeton University (1968) under John Wheeler M.A. in Physics from Vassar College (1970) M.S. in Technology of Management from American University (1972) M.S. and M.Phil. in Computer Science from Yale University (1973-1974) Ph.D. in Computer Science from Yale University (1978) J.D. from Duquesne University (1981) His research interests bridge multiple domains with exceptional depth. In experimental mathematics, he develops computational systems that automatically generate and prove novel mathematical theorems, particularly in number theory, having contributed hundreds of new results to the field. As a leading expert in electronic voting security, he has examined over 120 voting systems for seven states and testified before Congress multiple times. His work uniquely integrates legal expertise with technical knowledge, especially regarding intellectual property in the digital age. He directs the M.S. in Artificial Intelligence and Innovation program at CMU, advising 74 students in this cutting-edge field. His publication trajectory reveals an evolution from foundational computational geometry (co-authoring the seminal "Computational Geometry: An Introduction") to contemporary issues in voting security and experimental mathematics. The consistent thread is applying computational methods to solve real-world problems with careful attention to legal and societal implications. His notable recognitions include: Industry Service Award of the Billiard and Bowling Institute of America (1996) Black and White Scotch Achiever's Award for contributions to bagpipe musicography (1991) As an educator and mentor, Dr. Shamos directs the M.S. in Artificial Intelligence and Innovation program and teaches courses including "AI & Future Markets" and "The Law of Computer Technology." He has served as an expert witness in over 360 legal cases involving computer technology and has been a statutory examiner of computerized voting systems for Pennsylvania since 1980. His extensive industry experience includes founding technology companies and serving as General Counsel for an AI company. Dr. Shamos is actively involved with the Universal Library project, which has scanned over 1.5 million books. He also serves as faculty advisor to the Carnegie Mellon Pool Team and is Curator of The Billiard Archive, reflecting his deep commitment to preserving billiards history alongside his academic pursuits.

Leonidas Lampropoulosمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Formal Verification
• +۵ مورد دیگر

Leonidas Lampropoulos is an Assistant Professor of Computer Science at the University of Maryland, with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He leads research in Programming Languages and Software Engineering, focusing on formal verification, proof assistants, and scalable software development methodologies. His work addresses challenges in formalizing security properties, optimizing proof workflows, and advancing verified software for critical systems like distributed systems and autonomous vehicles. He received the NSF CAREER Award in 2022 and co-led a $540K NSF-funded project (2021) to improve proof engineering tools and protocols. His academic contributions span programming language design, type systems, and testing frameworks, with notable collaborations through the Maryland Cybersecurity Center (MC²). Advised PhD students: Segev Elazar Mittelman, Alperen Keles, Oliwia Kempinski, Jacob Prinz, Finn Voichick. Key honors: NSF CAREER Award (2022), NSF Award for Proof Engineering (2021). Research partnerships: Collaborates with institutions like the University of Texas at Austin on proof assistant scalability. His grants emphasize bridging software engineering practices with formal verification to enhance reliability in large-scale projects. Current efforts include improving proof assistant usability and integrating formal methods into mainstream software development.

Manou Rosenbergمشاهده پروفایل

پژوهشگر ارشد

• Optimization
• Energy Infrastructure
• Evolutionary Algorithms
• +۳ مورد دیگر

Dr Manou Rosenberg is a Research Fellow at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), part of the Faculty of Science and Engineering. Their work focuses on optimization and energy systems, particularly in hydrogen infrastructure networks, electricity distribution networks, and evolutionary algorithms. Rosenberg is affiliated with the Centre for Optimisation and Decision Science. Research interests include applying optimization techniques to solve complex engineering problems in energy infrastructure, rural electrification, and renewable energy systems. Their work often integrates evolutionary algorithms with geospatial analysis and machine learning methods. Publications emphasize innovative solutions for hydrogen pipeline design, rural electricity networks, and microgrid optimization. Recent work explores multi-stage approaches for spatially-aware infrastructure planning and the application of genetic algorithms to network design challenges. Rosenberg collaborates with institutions and researchers in Australia, contributing to projects that bridge theoretical optimization and practical energy system applications. They are based at the Curtin Perth campus in the New Technologies Building, Room 463.

Colin Cooperمشاهده پروفایل

استاد

• Computer Science
• Theoretical Computer Science
• Algorithms
• +۳ مورد دیگر

Prof. Colin Cooper holds the position of Professor at the University of Hamburg, affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences and the Department of Computer Science. He is a Mercator Fellow supported by the German Research Foundation (DFG). His research interests include theoretical computer science, algorithms, and network analysis. He is located in office G-214 at Vogt-Kölln-Str. 30, Hamburg. Key recognitions include the Mercator Fellowship (DFG), reflecting his contributions to academic research. While specific grants or advising records are not detailed here, his affiliation and research focus suggest active engagement in computational and algorithmic studies. Contact details include fax +49 40 42883-2246 and email colin.cooper@kcl.ac.uk.

Andrzej Dudekمشاهده پروفایل

استاد

• Extremal combinatorics
• Probabilistic combinatorics
• Random graph theory
• +۳ مورد دیگر

Andrzej Dudek is Professor of Mathematics at Western Michigan University, specializing in extremal and probabilistic combinatorics. His research examines Ramsey theory, random graphs, hypergraph problems, and combinatorial optimization through mathematical analysis. Funded by multiple Simons Foundation and NSA grants, he investigates threshold behaviors in generalized Ramsey numbers and structure emergence in random ordered graphs. His publications demonstrate consistent focus on bridging extremal combinatorics with probabilistic methods. Recent work establishes new bounds for Erdős-Gyárfás problems, analyzes Hamiltonian cycles in Dirac graphs, and characterizes unavoidable substructures in random matchings. Collaborative projects frequently address Ramsey-type questions at linear and quadratic thresholds. Supervised four PhD dissertations in combinatorics. Education includes PhD from Emory University (2008) and habilitation from Adam Mickiewicz University (2013). Recognitions include the Open Mind Prize (2010) and solution prize for an Erdős problem (2008). Organizes the Lake Michigan Workshop on Combinatorics and Graph Theory with NSF support.

Eric Ruppertمشاهده پروفایل

دانشیار

• Distributed Computing
• Shared-Memory Algorithms
• Data Structures
• +۳ مورد دیگر

Eric Ruppert is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University, where he has taught computer science since 2000. He is a member of the Theory of Computing Group and teaches foundational computer science courses including algorithms and theory of computation. He has served on program committees for major conferences including PODC 2025 and WADS 2025. Ruppert received his education at the University of Toronto. He has held postdoctoral positions at Brown University and served as a visiting professor at the École Polytechnique Fédérale de Lausanne. His research focuses on the theory of distributed computing, with particular emphasis on algorithm design and lower bound proofs. His recent work has centered on shared-memory data structures for multi-core architectures. His research interests span distributed algorithms, computational complexity, and the theoretical foundations of concurrent systems. He has made significant contributions to understanding the computational power of population protocols, consensus problems, and non-blocking data structures. His publication record shows a consistent focus on distributed computing theory, with recent work emphasizing non-blocking and wait-free data structures. Over the past decade, his research has increasingly addressed challenges in concurrent programming for multi-core systems, developing efficient algorithms for queues, deques, and binary search trees that provide strong progress guarantees. Ruppert has been actively involved in the distributed computing research community, serving on program committees for major conferences including PODC and WADS. His work has been published in top-tier venues such as Distributed Computing, Journal of the ACM, and proceedings of ACM SIGACT/SIGOPS conferences. At York University, Ruppert teaches courses on algorithms, theory of computation, and computational complexity. He has supervised numerous graduate students through project courses and thesis work. He is known for his dedication to teaching, including coaching students for the annual ACM programming contest. Ruppert is a member of the Theory of Computing Group at York University, which focuses on foundational aspects of computer science including algorithms, complexity theory, and distributed systems. His work bridges theoretical computer science with practical applications in concurrent and parallel computing.

Ly Dinhمشاهده پروفایل

استادیار

• Computational Social Science
• Network Analysis
• Social Simulation Models
• +۵ مورد دیگر

Dr. Ly Dinh is an Assistant Professor at the School of Information within the College of Arts and Sciences at the University of South Florida. Her research focuses on computational social science, leveraging network analysis, social simulations, and text mining to study social and organizational systems. She explores network science applications in crisis informatics, emergency response coordination, and interdisciplinary collaboration dynamics. Her work has been recognized with the 2020 Grace Hopper Scholar for Women in Computing and the 2018 Network Science Fellowship. Dr. Dinh holds a Ph.D. and M.A. from the University of Illinois at Urbana-Champaign and a B.A. from the University of Southern California. Her research spans structural balance theory, crisis communication patterns, and computational methods for analyzing social media data during disasters. Recent projects include studying hurricane response networks, disinformation propagation, and the impact of team science on collaboration diversity. Education: Ph.D., University of Illinois at Urbana-Champaign M.A., University of Illinois at Urbana-Champaign B.A., University of Southern California Her publications emphasize methodological advances in network analysis, crisis informatics, and computational linguistics. Recent work highlights the use of ERGM models for peer review networks, taxonomy alignment experiments, and geospatial text mining for disaster zone identification. She has also contributed to understanding public perception disparities through crowdsourced data and the role of structural balance in signed directed networks. Awards: 2020 Grace Hopper Scholar for Women in Computing 2018 Network Science Fellow at Visible Networks Labs Dr. Dinh’s research bridges theory and practice, with applications in emergency response coordination, academic collaboration optimization, and social media-driven crisis management. She is affiliated with interdisciplinary teams advancing computational methods for societal challenges.

• Optimization
• Big Data Analytics
• Complex Networks
• +۵ مورد دیگر

Foad Mahdavi Pajouh is an Associate Professor at the School of Business, Stevens Institute of Technology, where he holds the Jack Howe Fellowship. Previously, he served as an Assistant Professor at the University of Massachusetts Boston (2014–2021) and a Research Assistant Professor at the University of Florida (2012–2014). He earned his PhD in Industrial Engineering from Oklahoma State University (2012), and earlier degrees from Tarbiat Modares University (MS, 2006) and Sharif University of Technology (BS, 2004). His research focuses on theoretical, computational, and algorithmic optimization, with applications in big data analytics of complex networks. Key areas include business analytics, social network analysis, financial network analysis, and cybersecurity. His work emphasizes clique relaxations, network interdiction, and resilient network design. Recent studies address influential group detection, risk-averse clustering, and robust infrastructure planning. Publications highlight contributions to clique detection algorithms, network resilience, and optimization models. Over 30 peer-reviewed articles appear in top journals like European Journal of Operational Research , INFORMS Journal on Computing , and Annals of Operations Research . His work bridges graph theory and real-world network challenges in finance, cybersecurity, and environmental systems. Notably absent are explicit mentions of awards or grants, though his prolific publication record and academic roles suggest significant professional recognition. No student advisees are listed in the provided information.