جستجوی استاد راهنمای دانشگاهی

Vasant Honavarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Causal Inference
• +۶ مورد دیگر

Dr. Vasant Honavar is a Professor of Computer Science and Informatics at Pennsylvania State University, holding the Edward Frymoyer Endowed Chair. He serves as Director of the Center for Artificial Intelligence Foundations and Scientific Applications and Associate Director of the Institute for Computational and Data Sciences. His expertise spans artificial intelligence, machine learning, causal inference, and bioinformatics. Honavar has led over $60M in research grants and mentored 36 PhD students, 30 MS students, and numerous undergraduates. He is a Fellow of the AAAS and recipient of NSF Director’s Awards. Education: Ph.D., Computer Science and Cognitive Science, University of Wisconsin–Madison (1990) M.S., Computer Science, University of Wisconsin–Madison (1989) M.S., Electrical and Computer Engineering, Drexel University (1984) B.E., Electronics Engineering, Bangalore University (1982) Research Interests: His work focuses on machine learning, causal inference, knowledge representation, health informatics, and algorithmic fairness. Notable contributions include scalable algorithms for big data analytics and predictive modeling, as well as computational infrastructure for interdisciplinary science. Awards: Fellow, AAAS (2018) ACM Distinguished Member NSF Director’s Award for Superior Accomplishment (2013) Edward Frymoyer Endowed Chair (2013) Leadership: Honavar co-founded the Penn State Center for Artificial Intelligence and led the NIH-funded Biomedical Data Sciences Ph.D. program. He is a Co-PI of the North East Big Data Innovation Hub and serves on editorial boards of journals like IEEE/ACM Transactions on Computational Biology and Bioinformatics. Lab & Teams: Directs the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics, fostering collaborations across computer science, life sciences, and health sciences.

Rayan Saabمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Applied and Computational Harmonic Analysis
• +۵ مورد دیگر

Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.

Tobias Müllerمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Graphs
• +۵ مورد دیگر

Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.

• Branching processes
• Random trees
• Reinforcement processes
• +۹ مورد دیگر

Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.

Daniel Hugمشاهده پروفایل

استاد

• Probability
• Geometry
• (Convex) geometric analysis
• +۳ مورد دیگر

Prof. Dr. Daniel Hug is a Professor at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and the Institute of Stochastics . His research focuses on Probability , Geometry , Convex geometric analysis , Stochastic geometry , and Educational mathematics . He contributes to the DFG Priority Program Random Geometric Systems and previously participated in the DFG research unit Geometry and Physics of Spatial Random Systems . Prof. Hug has co-authored two influential monographs: Poisson Hyperplane Tessellations (Springer Monographs in Mathematics) and Lectures on Convex Geometry (Springer Graduate Texts in Mathematics, 2020). His work spans theoretical advancements in convex and stochastic geometry , including integral geometry, random mosaics, and tensor valuations, alongside applied studies in digital microstructures and Minkowski tensors . His publications from 2025–2024 emphasize hyperbolic space , Boolean models , and Minkowski tensor estimators , reflecting ongoing collaborations with researchers like M. Klatt, C. Thäle, and R. Schneider. Prof. Hug leads courses such as Stochastic Geometry and seminars on Random graphs and tessellations , and is actively involved in working groups like AG Stochastische Geometrie and AG Stochastik .

Tatiana Smirnova-Nagnibedaمشاهده پروفایل

دانشیار

• Combinatorial Group Theory
• Geometric Group Theory
• Asymptotic Group Theory
• +۵ مورد دیگر

Tatiana Smirnova-Nagnibeda is an Associate Professor in the Mathematics Section at the University of Geneva, where she obtained her PhD before holding positions at ETH Zurich and KTH Stockholm. She returned to UNIGE where she has established herself as a leading researcher in geometric and combinatorial group theory. Her research focuses on combinatorial, asymptotic and geometric group theory, as well as probabilities on groups and graphs. She has made significant contributions to the understanding of branch groups, self-similar groups, Schreier graphs, and spectral properties of group actions. Her work often bridges algebra, probability, and geometry, revealing deep connections between these areas through the study of Thompson's groups, Grigorchuk's group, and other important group constructions. Her recent publications demonstrate a consistent focus on subgroup structure in various classes of groups, spectral properties of Schreier and Cayley graphs, and connections to dynamical systems. She frequently collaborates with researchers from around the world, particularly with Rostislav Grigorchuk, and has mentored numerous doctoral students who have gone on to successful academic careers. Managing Editor for Groups, Geometry, and Dynamics Editor for L'Enseignement Mathématique Organizer of GAGTA conferences (2022, 2024) Organizer of specialized workshops on high-dimensional expanders (2015, 2016) She leads an active research group comprising postdoctoral fellows and doctoral students working on various aspects of group theory and its applications. Her teaching includes advanced courses on graph theory, random walks on groups, spectral theory of graphs, and amenability at the University of Geneva.

Marie-Colette van Lieshoutمشاهده پروفایل

استاد

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +۶ مورد دیگر

Marie-Colette van Lieshout is a Professor of Spatial Stochastics at the Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, and a Scientific Staff Member in the Stochastics group at Centrum Wiskunde & Informatica (CWI), Amsterdam. She has been active in research since 1997 and is a leading expert in stochastic geometry, spatial statistics, and image analysis. Her educational and professional background includes positions at the University of Warwick and the Free University Amsterdam. She is currently engaged in advanced research on point processes, random fields, and tessellation models, with applications in seismic hazard, fire risk, and machine learning. Her research interests include: Stochastic Geometry Spatial Statistics Image Analysis Point Process Modeling Seismic Risk Assessment Machine Learning for Spatial Data Her recent publications (2023–2025) focus on spatial intensity estimation, marked point processes, and data-driven risk modeling, showing a strong integration of classical spatial statistics with modern computational and machine learning techniques. Key themes include adaptive kernel smoothing, infill asymptotics, and applications in environmental and public safety domains. She has received significant recognition, including: Elected Fellow, International Statistical Institute (ISI) She has been awarded multiple research grants from NWO and other agencies, including the KLEIN grant for fire risk management and the DeepNL grant for seismicity prediction in Groningen. She has supervised or collaborated with researchers such as C. Lu, Z. Baki, and R. Markwitz. She is also active in academic service, serving on editorial boards (e.g., Methodology and Computing in Applied Probability), advisory boards (InHolland University), and councils of learned societies (Bernoulli Society, KWG). She leads and participates in research clusters such as STAR and contributes to outreach and education through courses and public lectures on earthquake modeling and spatial statistics.

Linan Chenمشاهده پروفایل

دانشیار

• Probability Theory
• Analysis
• Geometry
• +۴ مورد دیگر

Linan Chen is an Associate Professor in the Department of Mathematics and Statistics at McGill University since 2014, following a postdoctoral position at the same institution (2011–2014). He holds a Ph.D. from MIT (2011, supervised by Daniel Stroock) and a B.Sc. from Tsinghua University (2006). His research focuses on probability theory and its intersections with analysis and geometry, including partial differential equations, functional analysis, Gaussian measures, and random geometry. He is affiliated with the Probability Lab of the Centre de Recherches Mathématiques (CRM) and the CNRS-Unite Mixte Internationale (CNRS-UMI) since 2014. Chen teaches advanced probability courses such as Honours Probability (Math 356) and Advanced Probability Theory I/II (Math 587/589), alongside special topics courses like Topics in Geometry and Topology (Math 599). He has advised students including Leila Sloman, Reinhold Willcox, Ulysse Blau, and Olivier Nadeau-Chamard through independent study programs. His research explores cutting-edge topics in probability, including Gaussian free fields, degenerate diffusion equations, and asymptotic properties of geometric stochastic structures. Recent work addresses high-dimensional phenomena, stochastic processes in geometry, and applications in mathematical physics. Chen’s contributions span theoretical advancements and methodological innovations, with publications in journals such as the Journal of Theoretical Probability, Annales Henri Poincaré, and SIAM Journal on Mathematical Analysis.

Jonathan Pakianathanمشاهده پروفایل

استاد

• Algebraic Topology
• Cohomology of Groups
• Lie Algebras
• +۳ مورد دیگر

Jonathan Pakianathan is a Professor of Mathematics at the University of Rochester's School of Arts & Sciences, Department of Mathematics. He holds a PhD from Princeton University (1997) and a BS in Mathematics and Physics from Caltech (1992). His research focuses on algebraic topology, cohomology of groups and Lie algebras, geometric combinatorics, and finite fields. He has held leadership roles, including Director of Graduate Studies (2014–2020) and Director of Undergraduate Studies (2003–2010). Notable awards include the Goergen Award for Excellence in Teaching (2014) and the Sloan Foundation Doctoral Dissertation Fellowship (1996). His research explores applications of topology and algebra in discrete geometry, often collaborating with Alex Iosevich and students. Recent work addresses topics like Fuglede's conjecture over finite fields, geometric configurations, and probabilistic methods in combinatorics. His articles frequently intersect harmonic analysis, group theory, and number theory, reflecting interdisciplinary strengths. Education : PhD, Princeton University, 1997 BS in Mathematics and Physics, Caltech, 1992 Awards : Goergen Award for Excellence in Undergraduate Teaching, 2014 Sloan Foundation Doctoral Dissertation Fellowship, 1996 H. J. Ryser Scholarship, 1991 Grants include an NSA Mathematical Sciences Grant (2016–2017, $110,000 total) with A. Iosevich. He advises numerous PhD students, many of whom now hold academic or research positions. His teaching spans undergraduate to graduate courses, including algebra, topology, and financial mathematics. Research groups and collaborations extend to geometric combinatorics, algebraic topology, and applications in physics and data science. Ongoing projects explore topological methods in discrete geometry and probabilistic structures over finite fields.

Gilles Bonnetمشاهده پروفایل

استادیار

• Probability Theory
• Convex Geometry
• Stochastic Geometry
• +۳ مورد دیگر

Gilles Bonnet is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence within the University of Groningen , Netherlands. He is also affiliated with the Groningen Cognitive Systems and Materials Center (CogniGron) . His academic journey includes a PhD from University of Osnabrück (2016) under Prof. Matthias Reitzner, followed by a postdoc at Ruhr University Bochum (2016-2021) . Research Interests: His work bridges Probability Theory and Convex Geometry , focusing on high-dimensional stochastic structures. Key areas include random polytopes , Poisson hyperplane tessellations , and geometric inequalities . He has explored phase transitions in random polytopes and combinatorial diameter bounds. Scientific Contributions: Co-organized the Workshop On Randomness and Discrete Structures (2025) and the Spring School and Workshop on Polytopes (2019). His 2016 paper on Poisson tessellation earned a best poster award at the 18th Stochastic Geometry workshop. Awards: Best poster award (2016) Teaching: Delivers courses on Probability and Measure , Random Geometry , and Stochastic Processes at the University of Groningen and Ruhr University Bochum.

• High-dimensional probability theory
• Data Science
• Machine Learning
• +۵ مورد دیگر

Sjoerd Dirksen is a Professor of Mathematics for Data Sciences at Utrecht University since May 2025, having previously served as an Associate Professor for Applied Mathematics (2019-2025) and Junior Professor at RWTH Aachen University (2014-2019). He is affiliated with the Mathematical Institute within the Faculty of Science at Utrecht University, where his office is located in the Hans Freudenthal Building. His research interests focus on high-dimensional probability theory and its applications in data science, machine learning, and signal processing. Specifically, he investigates randomized data dimension reduction methods using structured random matrices, theory for deep learning including random neural networks, high-dimensional covariance estimation for wireless communication systems, and statistical postprocessing of weather forecasts in collaboration with the Royal Netherlands Meteorological Institute (KNMI). Previously, he worked on compressed sensing, sharp estimates for stochastic processes in Banach spaces, and noncommutative analysis. Analysis of his recent publications (2018-2024) reveals a strong focus on quantization effects in high-dimensional data processing, particularly one-bit compressed sensing and covariance estimation under coarse quantization. His work bridges theoretical mathematics with practical applications in signal processing, wireless communications, and meteorological forecasting, demonstrating a consistent trajectory from foundational mathematical research to applied data science problems. Dirksen's academic career shows progression from postdoctoral work at the Hausdorff Center for Mathematics in Bonn to independent research positions. His publication record demonstrates significant contributions to the mathematics of data science, with papers appearing in top journals across mathematics, statistics, and signal processing. His research combines deep theoretical insights with practical applications, particularly in the areas of dimensionality reduction and high-dimensional statistics.

• Mathematics of Data Science
• Stochastic Geometry
• Convex Geometry
• +۶ مورد دیگر

Eliza O'Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University. Her research focuses on the intersections of stochastic geometry , convex geometry , high-dimensional probability , and statistical learning theory . Her work explores: Nonconvex and convex regularizers in inverse problems Random tessellations and their machine learning applications Spectrahedral regression for convex function approximation Determinantal point processes for modeling repulsive interactions High-dimensional random convex sets and their asymptotic geometry Her research is supported by the National Science Foundation . Recent publications investigate gradient-based dimension reduction, oblique decision trees, and geometric properties of regularizers. She has received her PhD from the University of Texas at Austin and was a postdoctoral scholar at Caltech.

Jan Ratajمشاهده پروفایل

استاد

• Integral Geometry
• Geometric Measure Theory
• Stochastic Geometry
• +۲ مورد دیگر

Jan Rataj is a Professor at the Faculty of Mathematics and Physics , Charles University , specializing in Integral Geometry , Geometric Measure Theory , and Stochastic Geometry . He has been affiliated with Charles University since 1991 and has held visiting positions at universities in Jena, Karlsruhe, Ulm, and Aarhus. Research interests focus on geometric probability, convex geometry, and measure theory Teaches courses on Measure and Integration Theory , Geometry , and advanced topics in geometric measure theory Scientific contributions include coauthoring 2 monographs and ~50 research papers. His work explores: Random measurable sets and perimeter analysis Stochastic modeling of microstructures Geometric functionals and invariants Applications to spatial statistics and probability theory He supervises PhD students and leads the Seminar of Stochastic Geometry , which examines topics like: Max-stable random fields Convex body reconstruction Point process analysis

• Applied probability theory
• Markov chain Monte Carlo methods
• Spatial statistics
• +۴ مورد دیگر

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research focuses on advanced statistical methodologies with applications across various scientific domains. His educational background includes extensive training in mathematical sciences, though specific degree details aren't provided in the current materials. His research interests span: Applied probability theory Markov chain Monte Carlo methods (MCMC) Spatial statistics Stochastic geometry Stochastic simulation Point process modeling Professor Møller's recent publication record shows consistent productivity with 239 research outputs including journal articles, reports, and book chapters. His work demonstrates strong focus on spatial point processes, Bayesian inference methods, and applications of stochastic geometry. The research trends indicate increasing sophistication in modeling complex spatial patterns and developing computational methods for statistical inference. His scientific contributions have been supported by numerous research projects, with 28 projects documented including the current "Peculiar Distribution Functions and Interesting Stochastic Processes" (2022-2026). His work has generated significant scholarly impact with citations across multiple disciplines. Professor Møller has supervised 8 PhD students and maintains active collaborations across international research networks. His current projects suggest continued research activity in developing novel statistical methodologies for complex spatial data analysis with applications in materials science, neuroscience, and environmental statistics.

Markus Heydenreichمشاهده پروفایل

استاد

• Probability Theory
• Statistical Mechanics
• Random Graphs
• +۷ مورد دیگر

Markus Heydenreich is a Professor at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences and Technology, University of Augsburg. His research focuses on probability theory, particularly discrete random objects in statistical mechanics, mean-field behavior, lace expansion, and spatial random graph models. 2025: Workshop Bayrischzell 2024: Workshop Frauenchiemsee His recent publications highlight critical phenomena in random connection models, percolation, and spatial networks. He contributes to editorial boards and DFG-funded projects, including Random Geometric Systems and Mathematics of Many-Body Quantum Systems . Teaching includes courses on stochastic processes and seminars on probability and forensics. Key research themes: phase transitions, high-dimensional random walks, and mathematical physics applications. Collaborators include Remco van der Hofstad, Christian Hirsch, and Kilian Matzke. The team at Augsburg comprises doctoral researchers and postdocs.