جستجوی استاد راهنمای دانشگاهی

Sourav Sarkarمشاهده پروفایل

دانشیار

• Probability Theory
• KPZ Universality Class
• Random Growth Models
• +۱۰ مورد دیگر

Sourav Sarkar is a University Associate Professor in Probability at the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge (since July 2024) and a Fellow of Trinity Hall. Previously, he held positions as an Assistant Professor at DPMMS (2021–2024), Postdoctoral Fellow at the University of Toronto (2019–2021), and completed his Ph.D. in Statistics at UC Berkeley (2019) under Prof. Alan Hammond. His education includes a BSc (2013) and MSc (2015) in Statistics from the Indian Statistical Institute, Kolkata, advised by Prof. Parthanil Roy. Research Interests : Probability theory with a focus on KPZ universality class, random growth models (e.g., last passage percolation, exclusion processes), Coulomb gas, and percolation theory. His work bridges stochastic processes, mathematical physics, and statistical mechanics. Teaching : Teaches advanced courses in Probability and Stochastic Calculus at the University of Cambridge, including Part II courses on Applied Probability and Probability & Measure. Previously taught at UC Berkeley, where he received the Outstanding Graduate Student Instructor Award (2019). Publications : Focuses on KPZ fixed point, geodesic properties, and phase transitions in interacting particle systems. Recent work includes studies on the directed landscape, stable random fields, and competitive erosion dynamics. Awards : Recognized for exceptional teaching at Berkeley and sustained contributions to probability theory research.

Perla Sousiمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Random Walks
• +۴ مورد دیگر

Perla Sousi is a Professor of Probability at the University of Cambridge's Statistics Laboratory, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS). She is also a Fellow of Emmanuel College. Her research focuses on Probability Theory, Stochastic Processes, and their applications, including Random Walks, Brownian Motion, Mixing Times of Markov Chains, Percolation Theory, and Dynamical Systems. Notably, she explores phase transitions in stochastic models, cutoff phenomena in Markov chains, and the interplay between geometry and probability. Her work often involves collaboration with leading researchers in the field, addressing questions in both theoretical and applied stochastic processes. She has taught courses such as Probability IA, Percolation and Random Walks on Graphs, Advanced Probability, and Applied Probability. Her research has been published in top-tier journals like Annals of Probability , Probability Theory and Related Fields , and Communications in Mathematical Physics . Key contributions include studies on mixing times in dynamic environments, phase transitions in random walks, and capacity analysis in high-dimensional settings. Her articles highlight advancements in understanding stochastic systems' behavior, with a focus on cutting-edge topics like dynamical percolation, branching processes, and cutoff phenomena in complex networks. She actively contributes to both foundational theory and applications in stochastic modeling.

Lionel Levineمشاهده پروفایل

استاد

• Applied Mathematics
• Combinatorics and Discrete Geometry
• Probability and Statistics
• +۴ مورد دیگر

Lionel Levine is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His academic research focuses on abelian networks, interacting particle systems, and the emergence of complex patterns from simple rules. He has held prestigious fellowships, including the Simons Fellowship and Sloan Research Fellowship, and has been honored with an endowed professorship. Levine's work bridges probability theory, combinatorics, and statistical physics, with notable contributions to the study of sandpile models and internal diffusion-limited aggregation (IDLA). Education: Ph.D. in Mathematics (2007), University of California, Berkeley. Research Interests: Applied Mathematics, Combinatorics, Probability, Abelian Networks, Sandpile Models, and their intersections with computer science and statistical physics. His research explores how local rules generate large-scale structures, such as in abelian networks and sandpile models. Awards and Honors: Simons Fellowship, Sloan Research Fellowship, Endowed Professorship in the College of Arts and Sciences. Teaching: Courses include Probability Theory (MATH 6710/6720), Topics in Probability: Math for AI Safety (MATH 7710), and undergraduate mathematics courses like Strategy, Cooperation, and Conflict (MATH 1340). Grants and Funding: Supported by the National Science Foundation (NSF), Simons Foundation, Sloan Foundation, and Institute for Advanced Study. Collaborations: Collaborates with prominent researchers such as Yuval Peres, Cris Moore, and Jim Propp. His work has been published in leading journals like the Annals of Probability and Duke Mathematical Journal. Future Work: Continues investigating AI safety, causal models, and multi-agent learning, including research on mathematical frameworks for transformer circuits and hidden incentives in AI systems.

• Medical Imaging
• Nuclear Medicine
• Radiation Dosimetry
• +۳ مورد دیگر

Hunor Kertész is a Research Fellow at the University of Sydney's Image X Institute, affiliated with the Discipline of Medical Imaging Sciences in the School of Health Sciences. His research focuses on advancing medical imaging technologies, particularly in PET (Positron Emission Tomography) and CT (Computed Tomography) systems, with an emphasis on improving image quality, reducing radiation exposure, and developing novel imaging tools. His work spans several key areas: optimizing positron range correction in PET/CT for cardiac and oncology applications, exploring low-dose imaging techniques for pediatric patients, and creating open-source software tools like Dvgardener for medical image processing. He has contributed to the development of anthropomorphic phantoms for clinical simulations and portable CT systems for lung cancer screening. Recent publications highlight advancements in PET/MRI dose reduction for epilepsy patients, high-resolution total-body PET/CT through positron range correction, and the validation of Rubidium-82 myocardial imaging methods. His research often intersects with radiation dosimetry, image reconstruction algorithms, and 3D-printed anatomical models for quality assurance. While not explicitly mentioned, his contributions likely involve collaborations with clinical teams to translate imaging innovations into practical diagnostic solutions.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +۳ مورد دیگر

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

Alexandra BELIBOUمشاهده پروفایل

مدرس

• Music Education
• Music Therapy
• Music Technology
• +۳ مورد دیگر

Alexandra BELIBOU is a PhD Lecturer at the Department of Music Performance and Pedagogy, Faculty of Music, Transilvania University of Brașov. Her work bridges musicology with modern interdisciplinary approaches, focusing on music education technology, therapeutic applications, and 20th-century compositional analysis. She holds a prominent position in advancing digital pedagogy through software integration. Research Interests: Music therapy methodologies emphasizing pluralistic approaches in clinical settings AI applications in music composition and education Analysis of 20th-century composers like Schoenberg, Reich, and Pärt Cultural studies of folk music traditions (e.g., Irish dance music, Romanian psalmic liturgy) Publications Trends: Recent work explores pandemic-era musician well-being, AI-assisted composition tools, and the intersection of music with medicine/physics. Earlier research delves deeply into psalmic text settings across centuries, minimalist techniques, and numerical symbolism in atonal music. Her teaching and research emphasize bridging traditional musicology with emerging technologies, as seen in proposals for software-based creativity workshops and interdisciplinary curricula.

Carl D'Apolito-Dworkinمشاهده پروفایل

مدرس

• Architectural Design
• Computational Design
• Building Technology
• +۳ مورد دیگر

Carl D'Apolito-Dworkin serves as a Design Critic in Architecture at Harvard University's Graduate School of Design and is an associate partner/project architect at Preston Scott Cohen, Inc. His professional practice has produced significant public buildings including the Temple Beth Shalom Synagogue (2024), Anhui Province Museum of Science and Technology (2023), and Taubman College of Architecture at the University of Michigan (2016). Education: M. Arch from Harvard Graduate School of Design (AIA Henry Adams Medal recipient) B.A. summa cum laude from Yale University (Louis Sudler Prize for the Arts) His research centers on translating between physical matter and mathematical models to transform architectural construction processes. He investigates dual modes of material behavior, fabrication systems, and environmental conditions to manage indeterminacy in building projects through constraint modeling and geometric innovation. Specific research foci include facade component standardization across fabrication systems, airflow simulation, developable approximation of complex forms, and social geometries governing site-lines, acoustics, accessibility, and ritual practices within architectural spaces. Scientific Awards: AIA Henry Adams Medal Louis Sudler Prize for the Arts While no formal student advising relationships are documented, his teaching portfolio includes core architecture studios and specialized courses such as Architectural Representation II and Digital Media: Environmental Geometries at Harvard GSD, along with part-time lecturing roles at Penn and Northeastern. At Preston Scott Cohen, Inc., he leads multidisciplinary project teams in delivering complex public buildings, integrating computational design with construction innovation through collaborations with engineers, fabricators, and environmental consultants.

Stefano Gogiosoمشاهده پروفایل

دانشگاهی

• Quantum Computing
• Quantum Theory
• Quantum Software
• +۳ مورد دیگر

Stefano Gogioso is a Departmental Lecturer at the University of Oxford , specializing in quantum theory and quantum software. He holds a DPhil in Computer Science from Oxford (2013–2017) and advanced degrees from Cambridge (MASt, BA) and the University of Genova (MSc, BSc). As a Fellow at Kellogg College and co-founder of Hashberg Ltd , he develops quantum programming tools and focuses on quantum causal structures, quantum field theory, and natural language processing applications. His research bridges foundational quantum theory with practical applications, including near-term quantum computing and educational outreach through visual methods like Quantum in Pictures . Research Interests: Quantum foundations, quantum software, categorical quantum mechanics, quantum field theory, and quantum causality. His work emphasizes pictorial formalisms and compositional methods, with contributions to indefinite causality, quantum cellular automata, and quantum natural language processing (QNLP). Key Contributions: Published over 25 papers, including works on causal polytopes, categorical Feynman diagrams, and QNLP pipelines. Co-developed Hashberg 's quantum programming tools and serves as a mentor for AI initiatives at CDL-Oxford. His thesis introduced dynamics in categorical quantum mechanics, addressing symmetry and quantum clocks. Teaching: Teaches quantum computing courses for MSc/MFoCS students, professionals, and continued education. Courses include Quantum Software , Quantum Computing for Software Engineers , and bespoke corporate training. Labs/Teams: Part of the Oxford Quantum Group and involved in collaborative projects with industry and academia. Advising: Supervised students like Nicola Pinzani (causal orders) and Maria Stasinou (quantum field theory). Grants/Awards: Not explicitly listed, but recognized for contributions to quantum foundations and education.

• Ergodic Theory
• Combinatorics
• Ergodic Ramsey Theory
• +۳ مورد دیگر

Vitaly Bergelson is a Professor of Mathematics and Physical Sciences at The Ohio State University, affiliated with the Department of Mathematics within the College of Arts and Sciences. His work focuses on Ergodic Theory , Combinatorics , Ergodic Ramsey Theory , Polynomial Szemerédi Theorems , and Number Theory . He holds a PhD from the Hebrew University of Jerusalem (1984). His research explores the interplay between ergodic systems, combinatorial structures, and number-theoretic phenomena. Notable contributions include polynomial extensions of Szemerédi's theorem and foundational work on ergodic Ramsey theory. Recent articles address topics like multiplicative functions, Diophantine approximations, and quasirandom group structures. His work often bridges abstract mathematical frameworks with concrete combinatorial problems, yielding applications in additive combinatorics and symbolic dynamics. Publications highlight advancements in recurrence properties, uniform distribution, and the structure of algebraic dynamical systems. Collaborations with scholars like A. Leibman, N. Hindman, and J. Moreira underscore his interdisciplinary approach. His research is disseminated through top-tier journals such as Inventiones Mathematicae and Journal d'Analyse Mathématique .

Selma Yildirimمشاهده پروفایل

استاد آموزشی

• Mathematical Analysis
• Partial Differential Equations
• Spectral Theory
• +۱ مورد دیگر

Selma Yildirim is an Associate Instructional Professor at the University of Chicago's Department of Mathematics. Her research primarily focuses on mathematical analysis, partial differential equations, spectral theory, and mathematical physics, with an emphasis on eigenvalue problems. She has taught a wide range of courses including Calculus, Mathematical Methods in Physical Sciences, Linear Algebra, and Numerical Analysis. Her pedagogical approach incorporates blended synchronous teaching formats and educational technology like GeoGebra and Python. Her publications consistently explore eigenvalue estimation techniques for operators such as the fractional Laplacian and Klein-Gordon operators, with applications to quantum mechanics and fluid dynamics. She has also developed educational resources on metacognition and data science.

Youssef M. Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Data Assimilation
• Computational Science and Engineering
• +۵ مورد دیگر

Youssef M. Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering (CCSE). He is affiliated with the MIT Schwarzman College of Computing, the Statistics and Data Science Center, and the Aerospace Computational Design Laboratory. His research focuses on computational science and engineering, with an emphasis on uncertainty quantification, Bayesian modeling, data assimilation, and machine learning applied to physical systems. He holds a Ph.D. in Mechanical Engineering from MIT (2004), preceded by S.M. (1999) and S.B. (1997) degrees in Aeronautics and Astronautics from the same institution. Marzouk’s work bridges computational mathematics, statistical inference, and fluid dynamics, addressing challenges in energy systems and environmental modeling. He has received numerous awards, including the 2018 AIAA Associate Fellowship and the 2012 MIT Class of 1942 Career Development Chair. His teaching spans computational mathematics, fluid dynamics, and uncertainty quantification. Key collaborations involve the MIT CCSE and external institutions, with funding from DOE and NSF. He advises students on topics like stochastic modeling and inverse problems, and his research lab explores advanced computational methods for high-dimensional systems.

Victor H. Mollمشاهده پروفایل

استاد

• Classical Analysis
• Symbolic Computation
• Special Functions
• +۳ مورد دیگر

Victor H. Moll is a Professor of Mathematics at Tulane University, New Orleans, Louisiana. He holds a Ph.D. and M.S. in Mathematics from New York University (1984/1982) and a B.S. in Mathematics from Universidad Santa Maria (1978). His research focuses on Classical Analysis, Symbolic Computation, Special Functions, and Number Theory. He has held visiting positions at Universidad Santa Maria (Chile) and the Courant Institute (NYU), and served as a postdoctoral researcher at Temple University. Moll has contributed extensively to integral evaluations, particularly through the Gradshteyn and Ryzhik series and the method of brackets for definite integrals. His work bridges pure mathematics with applications in physics and combinatorics. His research spans asymptotic analysis of polynomials, valuation theory of number sequences, and symbolic computation techniques. Notable contributions include studies on Jacobi polynomials, Catalan numbers, and hypergeometric inequalities. Moll has collaborated on projects involving Feynman diagrams and modular-type transformations, showcasing interdisciplinary reach. His academic journey includes roles from Assistant Professor (1986–1992) to full Professor (2001–present) at Tulane. While no specific grants or awards are listed, his prolific publication record (over 200 articles from 2015–2025) highlights sustained academic engagement. His work often explores connections between classical mathematical analysis and modern computational methods.

• Mathematical Analysis
• Partial Differential Equations
• Dynamical Systems
• +۳ مورد دیگر

Sebastian Throm is an Assistant Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on mathematical analysis of partial differential equations, dynamical systems, and modeling of complex networks. His work explores the relaxation properties of Sobolev spaces, spectral gap analysis for dissipative Boltzmann equations with Maxwell interactions, and stability of self-similar solutions in inelastic kinetic models. He also investigates space-fractional Swift–Hohenberg equations for pattern formation and nonlinear dynamics. Recent publications highlight his contributions to spectral gap theory for Boltzmann equations, stability analysis of self-similar profiles in 1D inelastic collisions, and amplitude equations for fractional pattern-forming systems. These studies span mathematical physics, nonlinear analysis, and applied mathematics.

Subhabrata Senمشاهده پروفایل

استادیار

• Applied Probability
• Statistics of Networks
• Signal Detection
• +۵ مورد دیگر

Subhabrata Sen is an Assistant Professor of Statistics at Harvard University, located in Science Center 713, Cambridge. His research focuses on Applied Probability, Statistics of Networks, Signal Detection, and Machine Learning. He holds a PhD from Stanford University (2017), advised by Amir Dembo and Andrea Montanari, and prior degrees from the Indian Statistical Institute, Kolkata. His work bridges statistical theory, high-dimensional data analysis, and applications in networks and physics-inspired methods. Key contributions include foundational studies on spin glasses, community detection, and causal inference in complex systems. His research often employs mean-field techniques and explores universality principles in estimation problems. Selected awards and recognition are not explicitly mentioned in the provided text. His advising and grants include postdoctoral mentoring at Microsoft Research and MIT (2017-19). He collaborates on projects involving spectral methods, random matrix theory, and multi-layer network analysis. Labs/teams: Active in Harvard's Statistics Department research groups focused on statistical theory and network science. Maintains an academic website with preprints and resources.

• Inverse Problems
• Nonlinear Acoustics
• Medical Imaging
• +۵ مورد دیگر

Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.