جست‌وجوی استاد و پژوهشگر

Tianqing Zhangمشاهده پروفایل

عضو هیئت علمی

• Cosmology
• Astrophysics
• Weak Lensing Analysis
• +۳ مورد دیگر

Tianqing Zhang is a Research Assistant Professor in the Department of Physics & Astronomy at the University of Pittsburgh, affiliated with the Dietrich School. His research focuses on cosmology and astrophysics, with expertise in weak lensing analysis, photometric redshift estimation, and large-scale survey methodologies. Key projects include studies with the Hyper Suprime-Cam (HSC) survey, Rubin Legacy Survey of Space and Time (LSST), and Nancy Grace Roman Space Telescope. He develops advanced tools like the Redshift Assessment Infrastructure Layers (RAIL) and BlendingToolKit to address challenges in galaxy cataloging and systematics mitigation. His work spans cosmic shear analysis in harmonic and real spaces, baryonic effects on small-scale structures, and chromatic impacts on shear measurements. Zhang collaborates extensively on next-generation survey simulations (e.g., OpenUniverse2024) and neural posterior estimation techniques for astronomical image processing. His contributions bridge observational data challenges with theoretical cosmological modeling, emphasizing precision in parameter estimation and survey design optimization. While no specific awards are listed, his affiliation with high-impact cosmology projects indicates recognition in his field. Advising and grant details are not explicitly provided in available texts.

Alina Bucurمشاهده پروفایل

دانشیار

• Analytic Number Theory
• Arithmetic Statistics
• Automorphic Forms
• +۲ مورد دیگر

Alina Bucur is Associate Professor of Mathematics at UC San Diego. She received her PhD from Brown University and held postdoctoral positions at the Institute for Advanced Study and MIT. Her research focuses on analytic number theory, particularly arithmetic statistics, multiple Dirichlet series, and moments of L-functions. Bucur investigates distribution patterns in number theory using tools from arithmetic geometry and automorphic forms. Her work includes studies of point counts on curves over finite fields, Artin-Schreier covers, and zero distributions of zeta functions. She co-directs the Southwest Center for Arithmetic Geometry and co-organized the Women in Numbers research network. Her publications demonstrate innovations in finite field arithmetic, effective Sato-Tate applications, and Li-type criteria for L-functions. Collaborative projects emphasize interdisciplinary approaches bridging number theory, geometry, and statistical modeling. Bucur received an NSF FRG grant, Hellman Fellowship, and Simons Collaboration Grant. She mentors students in arithmetic geometry and coordinates the Arizona Winter School.

• Optimization
• Control Theory
• Partial Differential Equations (PDEs)
• +۳ مورد دیگر

Prof. Daniel Walter is a Junior Professor at the Institute of Mathematics, Humboldt University of Berlin, within the Faculty of Mathematics and Natural Sciences. His research focuses on non-smooth optimization, optimal control, and numerical analysis of partial differential equations (PDEs). He specializes in developing advanced methods for sensor placement in inverse problems and stabilizing control systems using computational techniques. His work integrates theoretical analysis with practical applications, addressing challenges in feedback stabilization, convergence of optimization algorithms, and data-driven approaches. Notable contributions include studies on extremal points in optimization norms, linear convergence rates of conditional gradient methods, and semiglobal stabilization using neural networks. Key Research Areas: Non-smooth optimization and sparse methods PDE-constrained optimization and control Inverse problems and sensor placement strategies Numerical methods for parabolic and elliptic equations Recent publications emphasize algorithmic advancements in optimization, with a focus on acceleration and convergence guarantees. His interdisciplinary approach bridges mathematical theory with engineering applications, particularly in stabilization and parameter estimation. Advising & Grants: While no specific grants or advisees are listed, his research indicates active involvement in training through cutting-edge projects in optimization and control.

• Theoretical Condensed Matter Physics
• Quantum Many-Body Problem
• Superconductivity
• +۴ مورد دیگر

Massimo Boninsegni is a Professor in the Department of Physics at the University of Alberta, Faculty of Science. His research focuses on theoretical condensed matter physics, the quantum many-body problem, superconductivity, superfluidity, and computational physics. He holds a BSc from the Università degli Studi di Genova (1986) and a PhD from Florida State University (1992). Professional Background: Postdoctoral Research Associate, NCSA, University of Illinois (1992-1995) Postdoctoral Fellow, University of Delaware (1995-1997) Assistant Professor, San Diego State University (1997-2001) Associate Professor, San Diego State University (2001-2002) Associate Professor, University of Alberta (2002-2005) Professor, University of Alberta (2005–present) Research Interests: Dr. Boninsegni explores quantum phenomena in low-dimensional systems, including superfluidity in confined helium and hydrogen clusters, Bose condensation, and quantum Monte Carlo simulations. His work addresses fundamental questions in many-body physics, such as phase transitions, defect dynamics in quantum solids, and the interplay between quantum statistics and material properties. Teaching: In Spring Term 2025, he taught PHYS 126 - Fluids, Fields, and Radiation (targeted at life/environmental/medical sciences students) and PHYS 130 - Wave Motion, Optics, and Sound (for engineering students). These courses cover fluid dynamics, electromagnetism, and wave phenomena with a computational physics focus. Awards: Fellow, American Physical Society, Division of Computational Physics (2008) Advising & Grants: While no specific advisees or grants are listed, his academic rank and publications indicate active research supervision and likely grant-funded projects in computational physics and quantum many-body systems. He collaborates with interdisciplinary teams, including those in computer science and materials engineering, as reflected by his affiliation with the Centennial Center for Interdisciplinary SCS II.

Linglong Kongمشاهده پروفایل

استاد

• Functional and Neuroimaging Data Analysis
• Statistical Machine Learning
• Robust Statistics
• +۴ مورد دیگر

Linglong Kong is a Professor and Canada Research Chair in Statistical Learning at the University of Alberta's Department of Mathematical and Statistical Sciences. He is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and holds a Canada CIFAR AI Chair. His expertise spans statistical machine learning, neuroimaging data analysis, and AI-driven healthcare solutions. Education: PhD in Statistics, University of Alberta (2009) MS in Statistics, Peking University (2002) BS in Probability and Statistics, Beijing Normal University (1999) Research Interests: Kong focuses on functional neuroimaging data analysis, robust statistical methods, and applications of machine learning in healthcare. His work emphasizes fairness, privacy, and interpretability in AI systems. Key areas include differential privacy, quantile regression, and predictive modeling for medical imaging and precision medicine. Recent Trends in Articles: His publications address cutting-edge challenges in privacy-aware machine learning, federated learning frameworks, and neuroimaging biomarkers. Recent work includes advancements in fairness for precision medicine and scalable methods for high-dimensional data analysis. Awards: Fellow of Amii (2021) Great Supervisor Award (2018) Josephine Mitchell Mentoring Award (2017) Grants & Advising: Kong leads major grants on AI ethics, neuroimaging biomarkers, and labor market equality. He has advised numerous students, including PhD candidates and postdocs, in statistical machine learning and healthcare analytics. Labs & Teams: Active in the Alberta Machine Intelligence Institute (Amii) and collaborates with interdisciplinary teams in neuroimaging, precision medicine, and AI ethics initiatives.

Alexander Litvakمشاهده پروفایل

استاد

• Convex Geometry
• Random Matrix Theory
• Probability Theory
• +۳ مورد دیگر

Alexander Litvak is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on convex geometry, random matrix theory, and probability theory with applications to high-dimensional geometry and stochastic processes. Education: He holds a Ph.D. from Tel Aviv University and an M.Sc. from Saint Petersburg State University. Research Interests: Litvak explores geometric probability, random structures, and their applications in optimization and functional analysis. His work often addresses questions related to convex bodies, matrix singularity, and the behavior of high-dimensional random systems. Key topics include minimal dispersion, random polytopes, and spectral properties of random graphs. Publications: His recent work (2020-2024) emphasizes probabilistic methods in convex geometry and random matrix theory, with notable contributions to linear bandits, sparse matrix analysis, and the circular law for digraphs. These studies reveal trends in understanding high-dimensional phenomena and probabilistic structures. Grants and Awards: No specific grants or awards listed in the provided materials. Advising and Labs: No student advisees or lab affiliations explicitly mentioned in the text.

Josep Callمشاهده پروفایل

استاد

• Animal Cognition
• Comparative Psychology
• Primate Behavior
• +۷ مورد دیگر

Josep Call is a Professor in the School of Psychology and Neuroscience at the University of St Andrews, focusing on the evolutionary origins of mind. His research investigates technical and social problem-solving in animals, particularly great apes, exploring causal reasoning, tool use, long-term memory, gestural communication, and mindreading. He holds the Wardlaw Professorship and is a Fellow of multiple prestigious societies, including the British Academy and Royal Society of Edinburgh. His work spans comparative psychology and cognitive evolution, with recent publications analyzing primate behavior in virtual environments, social learning dynamics, and decision-making processes. Key contributions include studies on chimpanzees' logical reasoning, bonobos' responses to inequity, and the ontogeny of vocal communication. He leads projects funded by the European Research Council, focusing on coordination, communication, and cultural transmission in social minds. Research areas: Primate cognition, social learning, evolutionary psychology Key collaborations: Institute of Behavioural and Neural Sciences, Centre for Social Learning & Cognitive Evolution PhD supervision: Four current students in primate behavior and cognition Awards: Wardlaw Professorship, Fellowships in APA, Cognitive Science Society, and Royal Society Publications highlight his interdisciplinary approach, bridging animal behavior with human cognitive development. Current research emphasizes virtual reality applications for studying primate foraging and spatial navigation, alongside investigations into cultural techniques and behavioral flexibility in non-human animals.

Tim Kuniskyمشاهده پروفایل

استادیار

• Computational Complexity of Statistical Problems
• Spectral Algorithms
• Random Matrix Theory
• +۴ مورد دیگر

Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.

Sabrina Streipertمشاهده پروفایل

استادیار

• Mathematical Biology
• Ecology
• Epidemiology
• +۳ مورد دیگر

Sabrina Streipert is an Assistant Professor in the Department of Mathematics at the University of Pittsburgh, affiliated with The Dietrich School of Arts and Sciences. Her research focuses on applying discrete mathematical models to ecological and epidemiological systems, particularly examining how intrinsic factors (e.g., trait evolution, delays) and extrinsic factors (e.g., environmental changes, harvesting) influence population dynamics and disease spread. She joined the university in 2022 and is actively involved in mentoring students and securing research funding. Her work bridges Mathematical Biology , Epidemiology , and Ecology , with a strong emphasis on discrete-time models to analyze population growth, disease transmission, and recovery dynamics. Recent projects explore pathogen tolerance evolution, delayed reproduction effects, and host-parasite coevolution. Streipert’s mathematical tools include time-scale calculus, bifurcation analysis, and game theory. Current and past funding includes the NSF grant #2424684 (2024-2027) on neuronal network analysis, Pitt Momentum Fund (2024-2025), and AWM-NSF Mentoring Grant (2023-2024). She serves as an Associate Editor for Journal of Biological Systems , Environmental Modeling and Assessment , and Journal of Mathematics and Computer Science . Streipert actively seeks students for collaborative research projects and maintains an office in Thackeray Hall 504 (Pittsburgh, PA 15260).

Stefan Meinelمشاهده پروفایل

دانشیار

• High Energy Physics
• Nuclear Physics
• Lattice Quantum Chromodynamics
• +۳ مورد دیگر

Stefan Meinel is an Associate Professor of Physics at the University of Arizona, affiliated with the Department of Physics. His primary role involves research and teaching in theoretical particle physics, specializing in lattice quantum chromodynamics (QCD). He holds a PhD from the University of Cambridge (2010) and has held postdoctoral positions at MIT and the College of William & Mary. He is a key member of the Flavour Lattice Averaging Group and the US Lattice QCD Collaboration, contributing to Snowmass studies on particle physics. Research focuses on precision lattice QCD calculations to study hadron properties, including resonance form factors, bottomonium behavior in quark-gluon plasma, and exotic tetraquark states. His work aids experimental efforts like LHCb, predicting hadron masses and probing Standard Model extensions. Teaching includes advanced courses such as Quantum Field Theory and Electromagnetic Theory at both undergraduate and graduate levels. Recent grants include a DOE award (Co-PI) and an NSF grant (Sole PI) for flavor physics and collider-related studies. Scientific contributions include the 'RHIC Physics Fellow' (2014–2019) and leadership in topical groups for weak decays of b and c quarks. Advised students include Callum Farrell, Forrest Guyton, and others. His research spans over 100 publications, with recent work emphasizing high-precision form factors and resonance dynamics.

Wolf-Udo Schröderمشاهده پروفایل

استاد

• Nuclear Physics
• Radiochemistry
• Laser Acceleration
• +۴ مورد دیگر

Wolf-Udo Schröder is a dual Professor of Chemistry and Physics at the University of Rochester's School of Arts & Sciences. He holds a PhD from the University of Darmstadt (Germany, 1971) and leads the Nuclear Science Research Group. His research focuses on nuclear dynamics at intermediate/high energies, laser-driven ion acceleration, and tritium behavior in materials. Education: PhD in Chemistry, University of Darmstadt, Germany (1971) Research Interests: Heavy-ion reactions and cluster physics Neutron source applications from inertial confinement fusion Tritium adsorption/diffusion in metals Laser-accelerated ion beam experiments Nuclear decay mechanisms (spinodal vaporization, surface boiling) Publications Trends: Over 15 key papers since 2011, emphasizing laser-accelerated ion applications (2019-2022), tritium surface behavior (2015-2020), and novel reaction mechanisms (2011-2015). Collaborations include the Laboratory for Laser Energetics (LLE) and international teams like the ISODEC and CECIL collaborations. Advising & Grants: While no students are listed, extensive collaborative work with postdoctoral researchers (e.g., M. Sharpe, C. Fagan) and institutional grants supporting LLE projects are evident. Labs/Teams: Lead the Nuclear Science Research Group and collaborate with LLE's fusion energy programs. Active in detector development (NSTAR neutron detector) and plasma-material interactions research.

Juan Rivera-Letelierمشاهده پروفایل

استاد

• Dynamical Systems
• Number Theory
• Ergodic Theory
• +۳ مورد دیگر

Juan Rivera-Letelier is a Professor of Mathematics at the University of Rochester's School of Arts & Sciences. He holds a Doctorat en Mathématiques Pures (equivalent to a PhD) from Université de Paris Sud, obtained in 2000. His research focuses on dynamical systems, number theory, and ergodic theory, with particular emphasis on non-Archimedean dynamics, statistical properties of maps, and applications to arithmetic geometry. He has authored numerous influential papers on topics such as Fatou components, Gibbs states, and p-adic distributions of algebraic points. His recent work explores phase transitions in intermittent maps, sensitive dependence in geometric Gibbs states, and equidistribution of CM points in toric varieties. He has contributed to foundational results in ultrametric dynamics, including studies on wildly ramified power series and residue fixed point indices. Rivera-Letelier’s research bridges complex dynamics, real analysis, and number theory, often employing techniques from thermodynamic formalism and non-uniform hyperbolicity. His work has implications for understanding the interplay between arithmetic structures and dynamical systems, with applications to Diophantine geometry and statistical mechanics.

Konstantinos Daniilidisمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• 3D Perception
• +۷ مورد دیگر

Konstantinos Daniilidis is the Ruth Yalom Stone Professor at the Department of Computer and Information Science, University of Pennsylvania, within the School of Engineering and Applied Science. He holds an adjunct position at Archimedes, Athena Research Center, Greece. His research focuses on computer vision, robotics, and geometric methods in AI, with a strong emphasis on 3D perception, event-based vision, and neural networks for motion analysis. Education : - PhD in Computer Science, University of Karlsruhe (1992), advised by Hans-Hellmut Nagel. - Diploma (Master's equivalent) in Electrical Engineering, National Technical University of Athens (1986). Research Interests : Daniilidis explores equivariant neural networks for structure from motion and robotics, event-based vision systems for low-latency perception, and 3D human motion capture using monocular and multi-sensor approaches. His work integrates geometric priors with deep learning to address challenges in SLAM, object pose estimation, and autonomous navigation. He also investigates robotic systems for tasks like object catching and fluid dynamics modeling, applying uncertainty-aware methods for robust decision-making. Articles Trends : Recent publications highlight advancements in event camera processing (e.g., EV-TTC, EqNIO), equivariant architectures for shape and motion analysis (SE(3)-Equivariant Networks), and multi-agent robotics with distributed perception systems. Collaborations with institutions such as ETH Zurich and Boston University reinforce his contributions to scalable and geometrically grounded AI solutions. Advising & Grants : He advises over 30 PhD students and postdocs, many now faculty or industry leaders. His grants include NSF awards (NCS-FO 2124355, CPS 2038873), ARL DCIST, and ONR funding, focusing on robotics, vision, and AI. His work often bridges theoretical insights (e.g., matrix permanent for localization) with practical robotic systems like the Tiercel drone. Labs & Teams : Affiliated with the GRASP Laboratory at Penn, a premier robotics & vision research center. Collaborates widely, including with the Robotics Lab at ETH Zurich and researchers at the University of Coimbra. His group develops open-source tools like EV-FlowNet and EventGAN for event-based vision.

Val B. Tannenمشاهده پروفایل

استاد

• Databases
• Logic and Computation
• Data Provenance
• +۳ مورد دیگر

Val B. Tannen is a Professor in the Department of Computer and Information Science at the University of Pennsylvania. He specializes in database systems, logic and computation, and data provenance. His research focuses on query languages, incremental computation, and semantic integration. Tannen has also contributed to foundational work in provenance analysis, probabilistic databases, and distributed stream processing systems. He is a Fellow of the ACM and a Member of Academia Europaea. He teaches courses such as CIS 1600 (Spring 2023) and has authored over 140 publications. His work frequently explores technical challenges in maintaining data integrity, optimizing query processing, and enabling privacy-aware systems through provenance tracking. Key contributions include the DBSP framework for stream processing, semiring-based provenance semantics, and systems like ORCHESTRA for collaborative data sharing. His research integrates theoretical computer science with practical database engineering, addressing both foundational problems and real-world applications in data management.

Sam Chowمشاهده پروفایل

دانشیار

• Diophantine Equations
• Diophantine Approximation
• Additive Combinatorics
• +۲ مورد دیگر

Sam Chow is Associate Professor in Number Theory at the University of Warwick, specializing in diophantine approximation, additive combinatorics, and analytic number theory. His research solves problems at the intersection of diophantine equations, Ramsey theory, and geometric measure theory, with recent breakthroughs on Duffin-Schaeffer conjecture counterexamples and Littlewood's conjecture. Chow organizes the 2025 DECANT conference celebrating Trevor Wooley's 60th birthday and edits for the Bulletin and Journal of the London Mathematical Society. His work develops new combinatorial and analytical methods for classical problems in number theory, frequently involving fractal sets, prime numbers, and polynomial expansions over finite fields.