جست‌وجوی استاد و پژوهشگر

Pavel Veselýمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms
• Data Structures
• +۹ مورد دیگر

Pavel Veselý is an Assistant Professor at the Computer Science Institute of the Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in streaming, online, and approximation algorithms, with applications in bioinformatics and data privacy. His research interests include: Streaming algorithms for quantile estimation, geometric problems, and adversarial robustness Online algorithms, especially in scheduling and buffer management Approximation algorithms, including shortest superstrings and their use in genomic data (k-mer sets) Randomized algorithms and algorithmic data privacy His recent work has advanced the state of the art in streaming quantile estimation (KLL sketch), indexed k-mer representations using masked superstrings, and streaming facility location in high dimensions. His publications appear in top venues such as FOCS, STOC, PODS, and SODA. Notable scientific awards include: Best Paper Award at PODS 2021 2022 ACM SIGMOD Research Highlight Award Supervision of award-winning student theses, including Dean’s Award and Czech-Slovak competition wins He actively advises students, including PhD and master’s level researchers, and leads a research group focused on data sketches and streaming algorithms. He has taught courses such as Algorithmic Data Privacy, Randomized Algorithms, and Streaming Algorithms, and previously served as a postdoc at the University of Warwick under Graham Cormode.

Ayush Khaitanمشاهده پروفایل

استادیار

• Differential Geometry
• Geometric Analysis
• Conformal Geometry
• +۲ مورد دیگر

Ayush Khaitan serves as a Hill Assistant Professor of Mathematics within the Department of Mathematics at Rutgers, The State University of New Jersey. This tenure-track faculty position indicates active research and teaching responsibilities in the university's academic structure. His research interests center on Differential Geometry and Geometric Analysis, with specific focus on Conformal Geometry, Mathematical Physics applications, and Algebraic Combinatorics. Work includes weighted structures on manifolds, renormalized volume, GJMS operators, and symmetric polynomials under probabilistic measures, revealing intersections between geometric analysis and algebraic combinatorics. Recent publications (2022-2025) consistently explore weighted ambient metrics and related operators, demonstrating technical depth in curvature integrals on Poincaré-Einstein manifolds and connections to singular Ricci flows. This output shows specialization in conformal invariants with relevance to mathematical physics frameworks. No scientific awards were documented in the provided sources. Khaitan's faculty role implies graduate student mentoring, but no formal advisees or grant funding details appear in the directory. His position falls under standard faculty responsibilities without noted administrative duties. The Department of Mathematics at Rutgers provides institutional context, though specific labs or research teams are unmentioned in available text.

Kapila Rohan Atteleمشاهده پروفایل

استاد

• Several Complex Variables
• Operator Theory
• Functional Analysis
• +۹ مورد دیگر

Kapila Rohan Attele is a Professor in the Department of Computing, Information and Mathematical Sciences at Chicago State University, part of the College of Arts & Sciences. He has been a key academic and administrative figure since 1998, serving as Department Chair from 2007 to 2022. His research bridges pure mathematics and applied computational sciences, with international collaborations, particularly with Indian institutions. Research Interests: His primary research areas include Several Complex Variables, Operator Theory, Functional Analysis, and Computational Algebra. He has extended these into Mathematical Biology and Data Science, integrating mathematical rigor with real-world applications in image processing, environmental modeling, and stochastic geometry. His work reflects deep theoretical foundations and interdisciplinary innovation. The recent publications and projects show a strong trend toward applied mathematics and international educational development. While early works focus on operator models and function spaces, later outputs include interdisciplinary applications and higher education policy, especially in U.S.-India academic partnerships. This evolution highlights a shift from pure analysis to broader societal and educational impact. PI, Obama–Singh 21st Century Knowledge Initiative grant ($250,000) PI, NSF SMARTER grant ($506,000) PI, NSF MCDC Data Science grant (with Northwestern University) PI, Department of Education grant 'Minority Retirement Security' ($565,000) Dr. Attele has advised doctoral candidates externally and contributed to international academic capacity building. He has led grant-funded initiatives focused on STEM education, data science training, and international student recruitment. His leadership in arranging high-profile visits, signing MoUs with JNTU-Kakinada, JNTU-Anantapur, and Telangana University, and hosting Vice Chancellors and Chief Ministers underscores his role in program branding and global outreach. He has been instrumental in organizing U.S.-India cultural events, webinars, and academic summits. Through the Center for Teaching & Research and international partnerships, he fosters a collaborative academic environment that supports student success, faculty exchange, and technological advancement at Chicago State University.

Karl-Theodor Sturmمشاهده پروفایل

استاد

• Stochastic Analysis
• Geometric Analysis
• Metric Measure Spaces
• +۷ مورد دیگر

Karl-Theodor Sturm is a Full Professor at the Institute for Applied Mathematics of the University of Bonn, where he has been since 1997. His research focuses on Stochastic Analysis , Geometric Analysis , and Metric Measure Spaces , particularly in the context of Ricci Curvature , Optimal Transport , and Liouville Quantum Gravity . He currently serves as Director of the Hausdorff Research Institute for Mathematics (since 2024) and previously coordinated the Hausdorff Center for Mathematics (2012–2019). As Principal Investigator of the ERC Advanced Grant Metric measure spaces and Ricci curvature - analytic, geometric and probabilistic challenges (2016–2022), he advanced interdisciplinary research at the intersection of geometry, probability, and analysis. Research Themes : Synthetic Ricci curvature, Optimal transport, Stochastic analysis on singular and random spaces, Dirichlet forms, Geometric inequalities. Recent Publications : Highlighted works include studies on heat flows on nonconvex domains, Wasserstein diffusions, and conformally invariant random fields in quantum gravity. His work often bridges probabilistic methods with geometric and analytic structures. Scientific Awards : Notable accolades include the Heisenberg Fellowship of the DFG (1994) and the ERC Advanced Grant (2016–2022). He has declined offers from prestigious institutions like Imperial College London, Northwestern University, and the University of Kansas. Teaching and Leadership : He leads graduate seminars (e.g., on Gaussian Free Fields and Liouville Quantum Gravity) and advanced courses in geometric analysis. As Managing Director of his institute and member of scientific committees (e.g., Mathematische Forschungsinstitut Oberwolfach), he shapes academic governance.

Federico Camiaمشاهده پروفایل

استاد

• Statistical Physics
• Probability Theory
• Phase Transitions
• +۳ مورد دیگر

Federico Camia is a Professor of Mathematics at NYU Abu Dhabi and a Global Network Professor at the Courant Institute of Mathematical Sciences at New York University. His research lies at the intersection of statistical physics and probability theory , with a focus on phase transitions, spatial stochastic models, and random geometric structures. Education BA with honors in Physics, Università di Bologna, Italy MS and PhD in Physics, New York University Research interests Statistical physics of random geometric systems Probability theory in spatially interacting models Phase transitions and critical phenomena Stochastic processes in mathematical physics Scientific Awards Marie Curie Research Fellowship Professor Camia has secured grants from the US National Science Foundation and the Dutch Organization for Scientific Research . He has co-organized seminars and an international workshop on probability and stochastic systems. Teaching Integral Calculus (MATH-UH 1009JQ) Calculus with Applications to Science and Engineering (MATH-UH 1012Q) Probability and Statistics (MATH-UH 2011Q) Advanced Probability (MATH-UH 3213) Foundations of Science collaborative course Quantum Theory and Relativity (CSTS-UH 1001) Multivariable Calculus (MATH-UH 1020Q) He continues to collaborate with the probability group at NYU's Courant Institute while contributing to multidisciplinary research initiatives at NYU Abu Dhabi.

Jonas Augustمشاهده پروفایل

پژوهشگر

• Computer Vision
• Medical Imaging
• Image Regularization
• +۳ مورد دیگر

Jonas August is a Project Scientist at the Robotics Institute of Carnegie Mellon University , focusing on geometry and uncertainty in computer vision and medical imaging. He employs probabilistic techniques like Markov processes and random fields to develop algorithms for medical image enhancement, including streak artifact removal in X-ray CT scans and vascular structure inference. Education: Ph.D., Yale University (2001) M.Phil., Yale University (1999) M.Sc., Yale University (1999) M.Eng., McGill University (1996) B.Eng., McGill University (1993) His research spans medical imaging , image regularization , and mathematical modeling , with applications in robotic perception and neural architecture. His work includes scalable regularized tomography algorithms and the development of DOC , a GNU/Linux cluster for medical imaging. He is supported by the National Science Foundation (Grant No. 0305719). August explores image statistics , curve inference , and object representation stability , contributing to contour fragment grouping and perceptual organization theories.

Vincent Tassionمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Percolation Theory
• +۵ مورد دیگر

Vincent Tassion is an Associate Professor at the Department of Mathematics , ETH Zurich , Switzerland. His research focuses on probability theory and stochastic processes , with particular emphasis on percolation theory , Ising models , and random walks on transitive graphs . He supervises Master's theses in discrete probability, requiring a strong background in probability and attendance in advanced probability courses. Over the years 2017-2024, he taught courses such as Probability Theory , Stochastic Processes , and Percolation Theory , often collaborating with colleagues like J. Bertoin, A.-S. Sznitman, and W. Werner. His research explores critical phenomena in statistical physics, geometric properties of random structures, and probabilistic methods in mathematical physics. He leads a research group within the department and offers thesis opportunities in topics like branching processes, random matrices, and Boolean functions.

Raffaella Burioniمشاهده پروفایل

استاد

• Statistical Physics
• Complex Networks
• Random Walks
• +۵ مورد دیگر

Raffaella Burioni is a Full Professor of Theoretical Physics in the Department of Mathematics, Physics and Computer Science at the University of Parma. She serves as Chair of the Non-Linear and Statistical Physics Division of the European Physical Society (EPS) and co-founded the Italian Society of Statistical Physics (SIFS), where she holds the position of Vice President. Her academic leadership includes directing the Ph.D. School in Physics and previously managing quality assurance for the Master's program in Physics (2017-2023). Her educational background includes an MS in Physics with distinction from the 2nd University of Rome and a PhD in Theoretical Physics from the University of Rome 'La Sapienza'. Postdoctoral experience spans the Theoretical Physics Laboratory of the École Normale Supérieure in Paris, the University of Milan, and the National Institute for the Physics of Matter (INFM). Prof. Burioni's research centers on equilibrium and non-equilibrium statistical physics, with deep expertise in graph theory, complex networks, random walks, and stochastic processes. She pioneers interdisciplinary applications to biological systems, neuroscience, and machine learning. Her work on rare events and anomalous diffusion has established fundamental principles like the 'single big jump' mechanism in transport phenomena. Recent investigations bridge statistical physics with neural network theory, examining kernel renormalization and feature learning in deep architectures. Analysis of her 15 most recent publications (2022-2025) reveals three dominant trends: (1) Theoretical advances in rare event statistics for jump processes and extreme value theory, (2) Network-based epidemic modeling incorporating adaptive temporal dynamics and simplicial structures, and (3) Machine learning physics connecting neural network theory to statistical mechanics through Bayesian effective actions and kernel methods. Her work consistently demonstrates how statistical physics principles solve complex problems across disciplines. Her scientific accolades include: Two-time recipient of the Enrico Persico Prize from the Accademia Nazionale dei Lincei Fellow of the Institute for Scientific Interchange (ISI) since 2014 American Physical Society Outstanding Referee (2018) Fellow of the European Centre for Living Technology (2020) As Director of the Ph.D. School in Physics, she mentors doctoral candidates while serving on editorial boards for Physical Review E, JSTAT, and Journal of Physics A. Her research is supported by international collaborations with institutions including the Kavli Institute for Theoretical Physics, Max Planck Institute, and Ben-Gurion University, evidenced by frequent keynote invitations at major conferences like StatPhys, ECCS, and APS March Meetings. Prof. Burioni leads collaborative research networks through her EPS division chairmanship and SIFS leadership, fostering cross-institutional projects on statistical physics applications. Her campus-based studies leverage Wi-Fi data from Parma University to model pedestrian dynamics and epidemic spreading in real-world constrained environments.

• Computer Science
• Artificial Intelligence
• Cryptography
• +۵ مورد دیگر

Leandro Tortosa Grau is a Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante's Higher Polytechnic School, where he has been teaching and conducting research since November 2007. He directs the Data Analysis and Visualization in Networks (ANVIDA) research group and is affiliated with the Institute of Computer Research at the University. His academic credentials include: Doctorate in Computer Engineering and Computing (2000) Computer Engineering degree (2000) Graduate in Mathematics from the University of Valencia (1988) Professor Tortosa Grau's research spans three distinct phases. Initially focused on parallel systems of linear equations during his doctoral studies (1995-2000), he then shifted to computer security and cryptography after joining the Security and Cryptology research group. Since late 2012, his research has centered on urban planning and urban networks from a computer science perspective, particularly using Self-Organizing Map (SOM) neural network models. In 2014, he founded the ANVIDA research group, which studies urban networks as complex networks and applies modern network theory to city modeling and visualization. His recent publications demonstrate a strong interdisciplinary approach, combining computer science with urban planning, traffic safety, and materials science. His work integrates deep learning, neural networks, and network analysis to address complex real-world problems in urban environments, transportation safety, and material science. Professor Tortosa Grau has supervised four doctoral theses to completion, all receiving the highest distinction of 'Sobresaliente Cum Laude.' He has coordinated multiple research projects including 'Analysis and visualization of the city as a multiple data network' (TIN2017-84821-P) and 'Analysis and visualization of data in complex networks' (TIN2014-53855-P), and contributed to the 'Data Analysis to Prevent the Spread of COVID-19' project (2020). He leads the ANVIDA research group which focuses on studying urban networks as complex networks, applying network theory to urban modeling and visualization. The group's work bridges computer science, urban planning, and data science to create innovative approaches for understanding and improving urban environments through computational methods.

Jia-Liang Leمشاهده پروفایل

استاد

• fracture mechanics
• probabilistic mechanics
• scaling
• +۶ مورد دیگر

Jia-Liang Le is the James L. Record Professor and Associate Department Head at the University of Minnesota in the Department of Civil Engineering. His research focuses on fracture mechanics, probabilistic mechanics, and structural reliability, particularly for quasibrittle materials like concrete and fiber composites. Email: jle@umn.edu Office: 254 Civil Engineering Building, 500 Pillsbury Drive SE, Minneapolis, MN 55455 His work provides deterministic and probabilistic models for fracture and fatigue in structures, aiming to enhance understanding of quasibrittle behavior and improve design standards for buildings, infrastructure, aircraft, and medical implants. Recent publications emphasize stochastic fracture simulations, scaling effects, and advanced material modeling. Research trends highlight applications in aerospace, geomechanics, and sustainable pavement design, with a strong focus on computational modeling and experimental validation. Current projects include probabilistic failure criteria for composites, fishnet models, and asphalt material optimization using graphene nanoplatelets.

• Theoretical computer science
• Randomness and Pseudorandomness
• Communication Complexity
• +۳ مورد دیگر

Pooya Hatami is an Associate Professor in the Department of Computer Science and Engineering at Ohio State University's College of Engineering. He joined the CSE department in 2019 after completing postdoctoral research at UT Austin (hosted by David Zuckerman) and spending two years as a postdoctoral researcher at the Institute for Advanced Study at Princeton and DIMACS at Rutgers University. He earned his Ph.D. from the University of Chicago in 2015 under the supervision of Alexander Razborov and Madhur Tulsiani. His research interests focus on Theoretical computer science , with particular emphasis on Randomness and Pseudorandomness, Communication Complexity, Analysis of Boolean Functions, Additive Combinatorics, and Learning Theory . His work often bridges theoretical computer science with mathematical concepts, developing rigorous frameworks for understanding computational complexity and designing efficient algorithms. His research has been supported by NSF grant CCF-1947546, demonstrating the significance and impact of his contributions to the field. An analysis of his recent publications reveals a strong focus on the intersection of learning theory and computational complexity, with particular attention to replicability in machine learning, communication complexity lower bounds, and the theoretical foundations of Boolean functions. His work on the Implicit Graph Conjecture and Borsuk-Ulam applications to learning theory demonstrates innovative approaches to longstanding problems in theoretical computer science. Scientific Awards: Best Paper Award at ICALP 2023 for "Online Learning and Disambiguations of Partial Concept Classes" Professor Hatami actively mentors graduate students, currently advising Pushen Wang and Yuting Fang in Computer Science and Engineering, and Chavdar Lalov and Sivan Tretiak in Mathematics. His involvement in program committees for major conferences like SODA 2024 and FOCS 2025 highlights his standing in the theoretical computer science community. His research has established important connections between pseudorandomness, communication complexity, and learning theory, contributing significantly to our understanding of fundamental computational limits.

Victor Seixas Souzaمشاهده پروفایل

پژوهشگر ارشد

• Combinatorics
• Number Theory
• Probability Theory
• +۳ مورد دیگر

Victor Seixas Souza is a Klarman Fellow in the Department of Mathematics at Cornell University since July 2025, hosted by Martin Kassabov and Steven Strogatz. He is also a Research Fellow at Sidney Sussex College, Cambridge. Previously, he held a Research Associate position in the Algorithms and Complexity group at the University of Cambridge under Tom Gur's mentorship. PhD in Pure Mathematics, University of Cambridge (Department of Pure Mathematics and Mathematical Statistics), supervised by Professor Béla Bollobás Master's in Pure Mathematics, IMPA (Instituto Nacional de Matemática Pura e Aplicada), supervised by Rob Morris Undergraduate degree in Computer Engineering, University of Campinas Dr. Souza's research spans combinatorics, number theory, probability theory, and their connections to statistical physics and theoretical computer science. His recent work has focused on synchronization phenomena, which was featured in Quanta magazine. He has made significant contributions to extremal graph theory, geometric probability, and combinatorial game theory, with publications appearing in top mathematics journals. His research demonstrates deep connections between discrete mathematics and probabilistic methods. Dr. Souza's publication record shows a strong focus on combinatorial structures and probabilistic phenomena. His work often bridges theoretical mathematics with applications in computer science, particularly in problems involving phase transitions, extremal configurations, and random processes. The research demonstrates sophisticated mathematical techniques applied to fundamental questions in discrete mathematics. Dr. Souza collaborates extensively with leading researchers in combinatorics and probability, including V. Falgas-Ravry, A. Mond, R. Sarkar, C. Bortolotto, L. Hollom, and J. Portier. His work has attracted attention from the mathematical community, with coverage in prominent outlets like Quanta magazine.

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +۲ مورد دیگر

Anders Aamand is a Postdoc at the Department of Computer Science , University of Copenhagen , affiliated with the Faculty of Science . His research focuses on algorithms and computational complexity, particularly in computational geometry, hashing techniques, and theoretical guarantees for algorithmic efficiency.

Eric Corwinمشاهده پروفایل

استاد

• Soft Condensed Matter
• Granular Materials
• Jamming
• +۳ مورد دیگر

Eric Corwin is a Professor of Physics at the University of Oregon, College of Arts and Sciences. He has been at UO since 2010, conducting research in soft condensed matter physics with a focus on granular materials, jamming, and non-equilibrium statistical mechanics. Education: Ph.D., University of Chicago, 2007 Corwin employs experimental, simulational, and theoretical tools to study complex systems arising from simple interactions. His work addresses fundamental questions in the glass transition, jamming, and the behavior of granular materials, with implications for material design and biophysics. Analysis of his recent publications (2021-2025) reveals three dominant themes: (1) extreme diffusion and outlier statistics in many-particle systems, (2) mechanical stability of ultra-low density sphere packings (Dionysian packing), and (3) scaling relations in jamming critical phenomena, including high-dimensional prefactors. Leading the CORWIN LAB, Corwin explores how simple interactions generate complex behavior. Current projects include Dionysian Packing for lightweight materials, Extreme Diffusion frameworks for environmental fluctuations, and computational studies of jamming in high dimensions.

Hartmut Löwenمشاهده پروفایل

استاد

• Statistical Physics
• Soft Matter Physics
• Active Matter
• +۵ مورد دیگر

Hartmut Löwen is a Professor and Chair at the Institute of Theoretical Physics II - Soft Matter at Heinrich Heine University Düsseldorf, where he has held a professorship since 1995. His research spans multiple areas of theoretical physics with a particular focus on soft matter systems and statistical physics. He has served in numerous leadership roles including as Coordinator of the Sonderforschungsbereich TR6 (2002-2013), Coordinator of the German-Japanese bilateral cooperation on Soft Matter in nonequilibrium (2011-2013), and as a member of the Senate of the Deutsche Forschungsgemeinschaft (2014-2021). 1982-1986: Study of physics, mathematics and chemistry at University of Dortmund 1987: PhD in physics on Phase transitions in polaron systems at University of Dortmund 1988-1990: Postdoc at Ludwig-Maximilians-Universität München 1990-1991: Postdoc at Ecole Normale Supérieure de Lyon 1991-1995: Postdoc at Ludwig-Maximilians-Universität München 1993: Habilitation at Ludwig-Maximilians-Universität München Löwen's research focuses on structure, dynamics and phase transitions in colloidal suspensions, collective behavior of active particles, statistical mechanics of interfaces, nonequilibrium phenomena, glass transition, nucleation, computer simulations, density functional theory, polymers, polyelectrolytes and liquid crystals, biological physics including bacterial motility and protein crystallization, and dusty plasmas. His work bridges theoretical physics with experimental collaborations across multiple international institutions. His recent publications (2023-2025) demonstrate a strong focus on active matter systems, with particular emphasis on non-equilibrium dynamics, phase transitions in active systems, and the thermodynamics of self-propelled particles. The publications reveal a consistent pattern of interdisciplinary research that connects statistical physics with soft matter, biophysics, and materials science, often through collaborations with experimental groups to validate theoretical predictions. SigmaPhi Prize for seminal contributions to Statistical Physics (2023) Fellow of the Institute of Physics (since 1999) Gentner-Kastler-Prize (2003) ERC Advanced Grant INTERCOCOS (2010) Heisenberg-fellowship of the Deutsche Forschungsgemeinschaft (1994) Selected as Outstanding Referee for Physical Review journals (2017) Löwen has successfully secured significant research funding including the ERC Advanced Grant INTERCOCOS and leadership roles in multiple DFG-funded priority programs including SPP 1726 on microswimmers and SPP 1296 on heterogeneous nucleation. His current research group maintains active collaborations with institutions across Europe, Japan, and the United States, focusing on both theoretical development and experimental validation of models in soft matter physics. He has supervised numerous PhD students and postdoctoral researchers who have gone on to establish careers in academia and industry. Löwen leads a vibrant research group focused on theoretical soft matter physics with connections to multiple experimental collaborations. His group participates in the Science Priority Program SPP 2265 'Random geometric systems' of the DFG and the ITN Active Matter of the EU, maintaining active partnerships with researchers studying complex plasmas, liquid crystalline phases, microgel particles, active colloidal particles, and breathing particles.