جست‌وجوی استاد و پژوهشگر

• Computer Graphics
• 3D Reconstruction
• Deep Learning
• +۵ مورد دیگر

Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.

Clifford Cheungمشاهده پروفایل

استاد

• Theoretical Physics
• Quantum Gravity
• Scattering Amplitudes
• +۵ مورد دیگر

Clifford Cheung is a Professor of Theoretical Physics at the California Institute of Technology (Caltech). He is affiliated with the Department of Theoretical Physics within the Division of Physics, Mathematics and Astronomy. His research focuses on fundamental questions in quantum gravity, scattering amplitudes, string theory, and effective field theories, with particular emphasis on bootstrap principles, black hole dynamics, and gravitational wave physics. Cheung's work bridges high-energy physics, astrophysics, and mathematical physics. He has pioneered methods to reconstruct scattering amplitudes using symmetry principles and positivity bounds, contributing significantly to our understanding of string theory's uniqueness and the interplay between gravitational systems and gauge theories. His research also explores the implications of effective field theories for extreme mass ratio binaries and gravitational wave phenomena. Key themes in his work include the development of novel symmetry-based approaches to quantum gravity, the application of bootstrap methods to constrain fundamental theories, and the exploration of connections between scattering amplitudes and black hole thermodynamics. His findings have advanced our knowledge of gravitational interactions, string universality, and the foundational structure of spacetime. Cheung has been featured in Caltech news for his contributions to theoretical physics, including insights into the 'miracle and beauty of physics' and his work on the Sloan Research Fellowships. His research outputs consistently address cutting-edge topics in theoretical physics, with a focus on unifying principles and rigorous mathematical frameworks.

Jason P. Millerمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Schramm-Loewner Evolutions
• +۷ مورد دیگر

Jason P. Miller is a Professor in the Statistics Laboratory at the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, and a Fellow of Trinity College, Cambridge. He previously held the Poincaré Chair at IHP in the 2015-2016 academic year and was a post-doctoral researcher at MIT and Microsoft Research. Miller's research focuses on probability theory, particularly stochastic interface models, random surfaces, Schramm-Loewner evolutions (SLE), Liouville quantum gravity, and random planar maps. His work bridges mathematical physics and probability, exploring deep connections between random geometry, conformal field theory, and statistical mechanics. He has made fundamental contributions to understanding the relationship between Liouville quantum gravity and the Brownian map, and has extensively studied the properties of Schramm-Loewner evolutions in various contexts. Miller's publication record shows a consistent focus on the intersection of probability theory and mathematical physics, with a particular emphasis on scaling limits of discrete models to continuum objects. His work often involves collaborations with prominent researchers like Scott Sheffield and Ewain Gwynne, and demonstrates a progression from foundational work on SLE and the Gaussian free field to more recent breakthroughs in Liouville quantum gravity and its connections to random planar maps. Scientific Awards Rollo Davidson Prize, 2015 Poincaré Chair, 2015-2016 academic year Whitehead Prize, 2016 Clay Research Award, 2017 ICM invited speaker (probability and statistics), 2018 Doeblin Prize, 2018 Eisenbud Prize, 2023 Fermat Prize, 2023 Miller has supervised numerous PhD students (though specific names aren't listed in the provided text) and has been involved in significant research grants supporting his work in random geometry and probability theory. His editorial service includes positions on the boards of Probability Theory and Related Fields and Bernoulli journals. While specific laboratory details aren't provided, Miller's research appears to be theoretical in nature, focusing on mathematical analysis of random geometric structures. His work has significant implications for theoretical physics, particularly in understanding quantum gravity and critical phenomena in statistical mechanics.

Peng Liمشاهده پروفایل

استاد

• Integrated Circuits and Systems
• Brain-Inspired Computing
• Electronic Design Automation
• +۶ مورد دیگر

Peng Li is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on integrated circuits, brain-inspired computing, electronic design automation, and hardware machine learning systems. He holds Fellow status in the Institute of Electrical and Electronics Engineers (IEEE). His work emphasizes neuromorphic engineering, spiking neural networks, and the intersection of machine learning with analog circuit design. Education includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University, an MS in Systems Engineering from Xi'an Jiaotang University, and a BS in Information Science and Engineering from the same institution. His research has been recognized with prestigious awards including the ICCAD Ten-Year Retrospective Most Influential Paper Award and multiple Design Automation Conference Best Paper Awards. Key research trends in his articles include advancements in spiking neural networks (SNNs), hardware accelerators for neuromorphic computing, Bayesian optimization for analog circuit design, and robustness in machine learning systems. He explores topics like adversarial robustness, energy-efficient architectures, and data-efficient prediction techniques. His work bridges theoretical machine learning models with practical hardware implementations, particularly in 3D integration and systolic array acceleration. Notable contributions include pioneering hybrid approaches combining formal verification with machine learning for analog circuits (HFMV framework), and innovations in neuromorphic processors such as the 3D Liquid State Machine architecture. His research also addresses challenges in semiconductor manufacturing, including wafer map pattern recognition and failure detection through semi-supervised learning and contrastive methods. Awards highlight his impactful contributions to both design automation and neural computing. His grants and collaborations likely span industry partnerships in semiconductor technology and neuromorphic computing. He leads a lab focused on next-generation hardware-software co-design for intelligent systems, emphasizing energy efficiency and scalability.

Kyle Luhمشاهده پروفایل

استادیار

• Probability
• Random Matrix Theory
• Randomized Algorithms
• +۲ مورد دیگر

Kyle Luh is an Assistant Professor at the University of Colorado Boulder in the Department of Mathematics, part of the College of Arts and Sciences. His research focuses on probability, random matrix theory, and randomized algorithms. Education: Ph.D. in Mathematics from Yale University (2017) His recent work explores eigenvalue gaps in random matrices, controllability of non-Hermitian systems, and applications to sparse reconstruction. Publications span topics like circular law for block band matrices, Littlewood–Offord inequalities, and stability analysis in quantum walks. Key trends in his research include spectral analysis of random graphs, robustness in learning algorithms, and combinatorial aspects of matrix theory. His articles highlight intersections between pure probability and applied computational methods.

Ivana Nikoloskaمشاهده پروفایل

استادیار

• Machine Learning
• Quantum Computing
• Communication Engineering
• +۷ مورد دیگر

Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.

Gourab Rayمشاهده پروفایل

دانشیار

• Probability Theory
• Statistical Physics
• Stochastic Processes
• +۳ مورد دیگر

Gourab Ray is an Associate Professor in the Department of Mathematics and Statistics at the University of Victoria, Faculty of Science. He holds a PhD from the University of British Columbia, Vancouver. His research focuses on the intersection of probability theory, geometry, and mathematical physics, particularly large-scale patterns in stochastic models inspired by physics. Key areas include random planar maps, random walks, lattice spin models, dimer models, Gaussian free field properties, and Liouville quantum gravity. Recent work emphasizes establishing Gaussian free field-like behaviors in dimer models across various graphs and surfaces. He teaches courses such as MATH 236: Introduction to Real Analysis and MATH 555: Topics in Probability. His publications span leading journals including Inventiones Mathematicae , Annals of Probability , and Probability Theory and Related Fields . Notable contributions include studies on unimodular hyperbolic triangulations, half-planar map classifications, and conformal invariance in dimer models. No specific awards are listed for Dr. Ray, though his work has been recognized in peer-reviewed venues. He actively contributes to academic service, including roles on graduate committees and research collaborations. His research group engages with theoretical and applied aspects of probability theory, often bridging discrete and continuous mathematical frameworks.

Jonathan Weareمشاهده پروفایل

استاد

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +۲ مورد دیگر

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Gregory F. Lawlerمشاهده پروفایل

استاد

• Probability Theory
• Random Walks
• Schramm-Loewner Evolution
• +۳ مورد دیگر

Gregory F. Lawler is the George Wells Beadle Distinguished Service Professor in the Department of Mathematics at the University of Chicago. He also maintains appointments in the Department of Statistics and has affiliations with Computational and Applied Mathematics and Financial Mathematics programs. His academic background includes: B.A. (1976) from the University of Virginia Ph.D. (1979) from Princeton University under Edward Nelson Professor Lawler is a leading researcher in probability theory with a focus on conformally invariant processes , particularly the Schramm-Loewner Evolution (SLE) and various forms of random walks. His work bridges pure mathematics and statistical physics, establishing rigorous connections between discrete models and their continuous scaling limits. He has made fundamental contributions to understanding loop-erased random walks, Brownian motion, and the geometric properties of random curves, with applications in statistical mechanics for understanding two-dimensional critical phenomena. Analysis of his recent publications reveals a sustained focus on the mathematical foundations of SLE, with particular attention to natural parametrization, loop measures, convergence questions, and the relationship between discrete models and their continuous limits. His work consistently combines complex analysis with probabilistic methods to establish rigorous results about random curves. Professor Lawler is actively involved in mentoring through the University of Chicago's REU program and has developed extensive educational materials for students at all levels. His notes on probability theory and stochastic calculus are widely used resources within the mathematics community. He has authored several influential books including "Random Walk and the Heat Equation," "Conformally Invariant Processes in the Plane," and "Random Walk: A Modern Introduction" (with Vlada Limic), which have become standard references in probability theory.

Vincent Vargasمشاهده پروفایل

دانشیار

• Probability
• Mathematical Physics
• Statistical Mechanics
• +۳ مورد دیگر

Vincent Vargas is a French mathematician and Associate Professor at the University of Geneva, where he joined in 2021 after holding a research position at CNRS. He completed his PhD in mathematics at Paris-Diderot University under the supervision of Francis Comets. His primary research interests include: Probability Mathematical Physics Statistical Mechanics Quantum Field Theory Gaussian Multiplicative Chaos Liouville Quantum Gravity Vargas has made significant contributions to the rigorous probabilistic construction of Liouville field theory and the proof of the DOZZ formula, work that was featured in Quanta Magazine. His research bridges mathematics and theoretical physics through probabilistic methods applied to quantum gravity. Analysis of his recent publications reveals a strong focus on mathematical structures underlying conformal field theory, with particular attention to Liouville quantum gravity across various geometries and the connections between probability and quantum physics. His notable scientific achievements have been recognized with prestigious awards: Marc Yor Prize (2019) George Pólya Prize (2022) Vincent Vargas has mentored several PhD students including Romain Allez, Yichao Huang, Guillaume Rémy, and Tunan Zhu. He has been actively involved in the academic community through organizing conferences and workshops, including a trimester at the Institut Henri Poincaré in 2015 and a conference on 'Probability and quantum field theory' in 2019. His professional activities extend to industry applications through his previous consultancy with Capital Fund Management (2007-2013) and his current role on the board of their research foundation.

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.

Tim Rogersمشاهده پروفایل

استاد

• Stochastic Processes
• Random Matrix Theory
• Collective Behavior
• +۴ مورد دیگر

Professor Tim Rogers is affiliated with the University of Bath as a faculty member in the Department of Mathematical Sciences . He is actively involved in research spanning complex systems, network theory, and stochastic processes. PhD in Random Matrix Theory from King's College London (2010) His research focuses on emergent behavior in random systems , including: Collective Behavior : Crowd dynamics, lane formation, and noise-enhanced synchronization Epidemics & Networks : Spread prediction, node risk assessment, and misinformation impacts Ecology & Evolution : Trait emergence, species boundaries, and demographic noise effects Random Matrix Theory : Spectral analysis and applications to complex systems Publication trends reflect interdisciplinary work bridging Physics, Biology, and Mathematics , with a focus on network structures , stochastic modeling , and emergence phenomena . Scientific awards include: 2015 : Editor's Choice for Europhys. Lett. 109, 28005 2016 : Highlight of Journal of Physics A 2017 : Editor's Suggestion for Phys. Rev. E 92, 032708 He has supervised numerous PhD students and postdocs on projects related to stochastic dynamics , network modeling , and mathematical biology , with ongoing grants from agencies like EPSRC and The Leverhulme Trust .

Oguz Durumericمشاهده پروفایل

دانشیار

• Differential Geometry
• Medical Image Analysis
• Geometric Topology
• +۵ مورد دیگر

Oguz Durumeric is an Associate Professor in the Department of Mathematics at the University of Iowa, part of the College of Liberal Arts and Sciences. His research focuses on differential geometry, medical image analysis, and geometric topology. He earned his PhD from SUNY Stony Brook and has contributed to interdisciplinary applications of geometry in medical imaging, particularly in lung biomechanics and radiation therapy planning. Education: PhD in Mathematics from SUNY Stony Brook. Research Interests: Dr. Durumeric’s work bridges pure and applied mathematics. In differential geometry, he explores knot energies and curvature properties. In medical imaging, he develops advanced registration techniques for 4DCT and MRI data to study lung ventilation patterns and improve cancer treatment accuracy. His geometric topology research includes ideal knot structures and conformal transformation analysis. Recent Research Trends: His articles highlight innovations in medical image registration (e.g., lung motion artifact correction, out-of-phase ventilation detection) and geometric models for biomedical applications. He also addresses foundational challenges like shape collapse in large-deformation registration. Grants & Collaborations: While specific grants are not listed, his work implies collaboration with medical imaging labs and oncology teams. No formal advisees are documented here. Labs/Teams: Affiliated with the University of Iowa Mathematics Department’s research groups in geometry and applied mathematics. His website provides further details on ongoing projects.

Christian Freundمشاهده پروفایل

استاد

• Protein Biochemistry
• Immunology
• Structural Biology
• +۳ مورد دیگر

Christian Freund is Professor of Protein Biochemistry at the Institute for Chemistry & Biochemistry, Freie Universität Berlin, holding this W2 professorship since 2011. He serves as Coordinator of the FU Berlin-UCSF Collaborative Initiative and Founding Member/Vice-chair of the DFG Collaborative Research Centre SFB/TRR 186 on Molecular Switches in Cellular Signal Transmission, leading interdisciplinary research across Berlin and Heidelberg institutions. His academic foundation includes Chemistry studies at Heinrich-Heine-Universität Düsseldorf (1983-1986) and Ludwig-Maximilians-Universität München (1986-1989), followed by a PhD in Structural Biology at the Max-Planck-Institute of Biochemistry (1994) and Habilitation in Biochemistry at Freie Universität Berlin (2005). Freund's research integrates structural biology, biophysics, and immunology to investigate molecular mechanisms of antigen presentation and cellular signaling. His work centers on MHC class II dynamics, protein conformational switches, and nanoscale organization of signaling complexes, employing NMR spectroscopy, quantitative proteomics, and molecular engineering to dissect immune recognition pathways and neuronal signaling mechanisms. Analysis of his 2010-2019 publications reveals consistent focus on MHC-mediated antigen presentation (60% of works), with significant contributions to understanding peptide exchange dynamics and HLA-DM editing functions. Secondary research streams explore synaptic protein networks (25%) and T cell signaling machinery (15%), demonstrating methodological breadth across structural biology, proteomics, and cell biological approaches. His scientific recognition includes: Biofuture award from the German Ministry of Education and Research (1999) Swiss National Funds Post-doctoral Scholarship (1997) Innovationswettbewerb Medizintechnik grant (2009) As research group leader at Leibniz-Institute of Molecular Pharmacology (2000-2011) and current FU Berlin professor, Freund has secured major collaborative funding through DFG SFB/TRR 186 and the UCSF partnership. His mentorship spans postdoctoral fellows at Harvard/Dana-Farber and Leibniz-Institute, with current supervision of graduate students in the Berlin biochemistry program. Freund directs a research group within FU Berlin's Institute for Chemistry & Biochemistry, operating as core component of SFB/TRR 186. His laboratory maintains active collaborations with UCSF's QBI (Nevan Krogan) and Heidelberg-based structural biology teams, utilizing advanced NMR, cryo-EM, and single-molecule imaging facilities across the Berlin-Heidelberg research alliance.

Hugo Duminil-Copinمشاهده پروفایل

استاد

• Probability Theory
• Statistical Mechanics
• Mathematical Physics
• +۳ مورد دیگر

Hugo Duminil-Copin is a renowned mathematician holding half-time appointments as Professor at the Institut des Hautes Études Scientifiques (IHÉS) and the Université de Genève . He has been recognized with prestigious awards, including the Fields Medal (2022) and the New Horizons Prize in Mathematics (2017) . His research focuses on probability theory and statistical mechanics, particularly phase transitions, percolation, and critical phenomena. Education: He earned his Ph.D. in Mathematics from the University of Geneva in 2012. Key academic milestones include a Full Professorship at IHÉS from 2015 and prior roles as Assistant and Associate Professor at Geneva. Research: Duminil-Copin explores foundational questions in statistical physics, such as the rigorous analysis of lattice models and phase transitions in dimensions three and four. His work bridges probability, combinatorics, and mathematical physics, with notable contributions to percolation theory and the Ising model. Awards: Beyond the Fields Medal, he has received the EMS Prize (2016), the Loeve Prize (2017), and an ERC Consolidator Grant (CRIBLAM, 2018–2023). Grants & Leadership: He led the ERC-funded CRIBLAM project, investigating random-cluster models and related topics. His collaborations span global institutions, emphasizing interdisciplinary approaches to complex systems.