جستجوی استاد راهنمای دانشگاهی

Werner Wendelinمشاهده پروفایل

استاد

• Schramm-Loewner Evolution
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Wendelin Werner is a renowned mathematician and currently the Rouse Ball Professor of Mathematics at the University of Cambridge (since 2023). Previously, he held professorships at ETH Zürich (2013–2023) and the University of Paris-Sud in Orsay (1997–2013). His research focuses on probability theory, mathematical physics, and complex analysis, particularly Schramm-Loewner evolution and conformal invariance. He has received prestigious awards, including the Fields Medal (2006), SIAM George Pólya Prize (2006), and Fermat Prize (2001). His work bridges stochastic processes and geometric structures, with implications for statistical mechanics and critical phenomena. Werner studied at the École Normale Supérieure (1987–1991) and earned his PhD from Université Paris VI under J. F. Le Gall (1993). Early career positions included postdoctoral research at Cambridge (1993–1995) and roles at CNRS (1991–1993, 1995–1997). His academic contributions span stochastic geometry, critical systems, and random curves, with seminal papers on loop measures, percolation, and SLE. He has delivered major lectures globally, including at the International Congress of Mathematicians (IMC 2006) and the College de France's Cours Peccot. Werner’s honors include membership in the French Academy of Sciences (2008), the Berlin-Brandenburg Academy (BBAW), and the German National Academy Leopoldina. He is also an honorary fellow of Gonville and Caius College, Cambridge (2009). His research has advanced understanding of universal scaling limits in two-dimensional models, earning recognition as a leader in probability and mathematical physics.

Stanislav Smirnovمشاهده پروفایل

استاد

• Mathematical Physics
• Statistical Physics
• Probability Theory
• +۴ مورد دیگر

Stanislav Smirnov is a Professor of Mathematics at the University of Geneva, where he has been a faculty member since 2003. He also holds a part-time position at the Chebyshev Laboratory of St. Petersburg State University. As Director of the National Centre of Competence in Research SwissMAP since 2014, he leads a major research initiative funded by the Swiss National Science Foundation. His education includes a mathematics degree from St. Petersburg State University (1992) and a PhD from the California Institute of Technology (1996). Prior to joining Geneva, he held positions at Yale University, the Institute for Advanced Study in Princeton, the Max Planck Institute in Bonn, and the Royal Institute of Technology in Stockholm. Professor Smirnov's research focuses on the mathematical foundations of statistical physics, with particular emphasis on conformal invariance in two-dimensional lattice models. His work has revolutionized our understanding of critical phenomena through rigorous mathematical approaches, establishing deep connections between probability theory, complex analysis, and statistical mechanics. He develops novel discrete complex analysis techniques to prove continuum limits of lattice models at criticality. His publication record shows consistent advancement in understanding random planar geometry, percolation theory, and the Ising model, with increasing attention to Liouville quantum gravity in recent years. His work bridges deep mathematical analysis with physical insights, often providing rigorous proofs for phenomena previously understood only through physical intuition. Fields Medal (2010) for the proof of conformal invariance of percolation and the planar Ising model in statistical physics As an advisor, Professor Smirnov has mentored doctoral students including Ekin Arikök and Nikolai Bobenko. He leads the ANALYSE, PHYSIQUE MATHÉMATIQUE ET PROBABILITÉS research group at the University of Geneva. His research is supported by major grants including the NCCR SwissMAP, European Research Council's AG COMPASP, and Swiss National Science Foundation grants 200020_143434 and 200020_141329. Professor Smirnov has organized numerous influential conferences, including the "Conformal maps from probability to physics" workshop at Monte Verità (2010) and multiple "Random Planar Geometry" meetings in Les Diablerets, fostering collaboration between mathematicians and physicists working on critical phenomena and random geometry.

• Nanotechnology
• Materials Science
• Photovoltaics
• +۴ مورد دیگر

Professor Thomas Bein is affiliated with the Department of Chemistry at Ludwig-Maximilians-Universität München (LMU) , where he leads the Functional Nanosystems research group. His work focuses on synthesizing and characterizing nanostructured materials with applications in energy, catalysis, and biomedical delivery. Mesoporous nanoparticles for drug delivery Semiconductor nano-morphologies for photovoltaics Photoelectrochemical water splitting Metal-organic frameworks (MOFs) Electroactive networks His research emphasizes atomic-scale control of material architectures using self-assembly, hydrogen bonding, and covalent interactions, enabling precise tuning of electronic, optical, and catalytic properties. A review of his recent publications reveals cutting-edge investigations into covalent organic frameworks (COFs), perovskite-inspired solar materials, and functional nanoparticle systems. Key trends include optimizing energy conversion efficiency, enhancing stability in optoelectronic devices, and exploring bio-compatible nanocarriers for targeted therapies. Professor Bein’s group actively contributes to interdisciplinary projects at the intersection of chemistry, physics, and biomedical engineering, with ongoing collaborations in solar energy, sustainable materials, and nanomedicine.

Gianluca Iaccarinoمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +۶ مورد دیگر

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

• Semiconductor Physics
• Gallium Nitride (GaN) Electronics
• Two-Dimensional Materials
• +۶ مورد دیگر

Tomas Palacios is a Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT) , where he directs the Center for Graphene Devices and 2D Systems and leads the Microsystems Technology Laboratories (MTL). His research focuses on pushing the boundaries of microelectronics through novel semiconductor materials and device architectures, including Gallium Nitride (GaN) and 2D materials like graphene and molybdenum disulfide (MoS2). Professor, MIT Electrical Engineering and Computer Science Director, MIT Center for Graphene Devices and 2D Systems Clarence J. LeBel Professor, MIT Faculty Director, Northeast Microelectronics Internship Program (NMIP) Research Interests span multiple cutting-edge domains: High-frequency electronics (>300 GHz) for 6G and quantum applications High-voltage power devices (600V–10kV) for energy conversion Post-silicon logic devices using 2D materials High-temperature electronics (e.g., Venus rover applications) Distributed neural networks on large-area 2D materials Graphene-based biosensors and chemical detection systems Scientific Contributions include: Double recipient of the IEEE George Smith Award for groundbreaking GaN transistor work Co-invented first MoS2 electronic circuits Developed world’s first Wi-Fi-to-electricity conversion antenna Led MIT’s Microsystems Technology Laboratories since 2021 Advising Philosophy emphasizes cross-layer expertise, with students gaining experience from materials synthesis to system-level prototyping. His lab has incubated startups like Vertical Horizons , focused on GaN power devices for AI and EVs.

Smirnov Stanislavمشاهده پروفایل

استاد

• Mathematical Physics
• Probability
• Complex Analysis
• +۳ مورد دیگر

Stanislav Smirnov is a Professor at the University of Geneva's Department of Mathematics since 2003 and Head of the Chebyshev Laboratory at St. Petersburg State University since 2010. He holds a Ph.D. from the California Institute of Technology (1996) and a B.Sc./M.Sc. from St. Petersburg State University (1992). His research focuses on mathematical physics, probability, complex analysis, and dynamical systems, with groundbreaking contributions to the understanding of critical phenomena in statistical physics. Key achievements include receiving the Fields Medal (2010), the highest honor in mathematics, for his work on percolation and the Ising model. He has organized major international conferences, such as the 'St. Petersburg School in Probability & Statistical Physics' (2012) and the '2D Statistical Physics Workshop' (2013). His academic career includes positions at Yale University, the Max-Planck Institute, and KTH Royal Institute of Technology. Education: Ph.D., Mathematics, California Institute of Technology (1996) B.Sc./M.Sc., Mathematics (with honors), St. Petersburg State University (1992) Awards: Fields Medal (2010) European Research Council Advanced Grants (2008, 2013) Rollo Davidson Prize (2002) Salem Prize (2001) Smirnov’s research bridges probability theory and complex analysis, with notable results on conformal invariance in two-dimensional models. His work has advanced understanding of critical exponents, percolation thresholds, and the universality of phase transitions. He advises doctoral students and collaborates internationally on projects funded by Swiss and European grants. He co-organized over 20 conferences worldwide, including plenary talks at the International Congress of Mathematicians (2006, 2010) and the World Congress in Probability and Statistics (2012). His lab fosters interdisciplinary research, linking mathematics to physics and computer science.

Jiatao Guمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• 3D Generation
• +۳ مورد دیگر

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.

Andrea Coladangeloمشاهده پروفایل

استادیار

• Quantum Computation
• Quantum Cryptography
• Quantum Learning Theory
• +۳ مورد دیگر

Andrea Coladangelo is an Assistant Professor at the Allen School of Computer Science & Engineering , University of Washington , co-leading the Quantum group and contributing to the Theory and Crypto groups. He coordinates the NSF-funded Quantum@UW REU program , fostering undergraduate research in quantum information. Previously, he was a postdoctoral researcher at UC Berkeley and the Simons Institute , advised by Umesh Vazirani , following a PhD in Computer Science at Caltech under Thomas Vidick . His academic journey began with a B.A. in Mathematics from Oxford and a Master in Mathematics from Cambridge . He co-founded qBraid , a platform for quantum computing education. Research Interests : Andrea explores the intersection of quantum computation and cryptography , focusing on foundational questions in entanglement , quantum correlations , and quantum learning theory . His work investigates quantum pseudorandomness , device-independent security , quantum algorithms , and quantum copy-protection , often leveraging computational assumptions to bridge quantum information theory with cryptographic applications. Scientific Awards : 2025 Google Research Scholar Program Award in Quantum Computing 2023 CSE Undergraduate Teaching Award for his course on quantum computation 2019 Best Student Paper Award at QIP Teaching & Outreach : Andrea designed and taught CSE 434: Intro to Quantum Computation (Spring 2023, 2024, 2025), CSE 534: Quantum Information and Computation (Autumn 2023), and CSE 599C: Quantum Learning Theory (Winter 2025). He also delivered lectures at the 22nd Bellairs Crypto Workshop (2024) and led a quantum programming tutorial using qBraid . Labs & Teams : As co-leader of the Quantum group at the Allen School, he collaborates with researchers in Theory and Crypto , advancing quantum computing through interdisciplinary projects and educational initiatives.

Daniel Dominic Kaplan Sleatorمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Theoretical Computer Science
• +۵ مورد دیگر

Daniel Dominic Kaplan Sleator is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. He maintains an office in the Gates-Hillman Center (7205 Gates-Hillman) and teaches various courses in algorithms and theoretical computer science. Professor Sleator's research spans several areas of theoretical computer science and algorithms. His primary interests include: Algorithms and Data Structures Amortized Analysis and Competitive Analysis Persistent and Self-Adjusting Data Structures Computational Geometry and Combinatorial Optimization Combinatorial Game Theory and Mathematical Games Music Analysis using Computational Methods His extensive publication record shows a consistent focus on efficient data structures and algorithms. Over the years, his work has evolved from foundational data structures like splay trees and skew heaps to applications in diverse areas such as music analysis and combinatorial games. A notable trend in his work is the development of self-adjusting data structures that achieve excellent amortized performance without maintaining explicit structural constraints. His papers on splay trees, skew heaps, and persistent data structures have become classics in the field. Professor Sleator has made significant contributions across multiple domains of computer science. His work on competitive algorithms for paging and list update problems has been particularly influential, establishing fundamental results in online algorithms. His research extends beyond traditional computer science into interdisciplinary areas like computational music theory, demonstrating the broad applicability of algorithmic thinking. He teaches a variety of courses including Algorithms 15-451/651, Competition Programming 15-295, and specialized topics like mathematical games.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Chris G. Van de Walleمشاهده پروفایل

استاد

• Computational Materials Science
• Semiconductors
• Nitride Materials
• +۷ مورد دیگر

Chris G. Van de Walle is the Herbert Kroemer Distinguished Professor in the Department of Materials at the University of California, Santa Barbara's College of Engineering. As a member of the National Academy of Engineering and fellow of multiple prestigious scientific societies including the American Physical Society and Materials Research Society, he leads the Computational Materials Group which is part of UCSB's strong computational science cluster within the Materials Department. His research interests focus on novel electronic materials, particularly wide-band-gap semiconductors (III-V nitrides, II-VI compounds), transparent conductors, complex oxides, loss mechanisms in light emitters, two-dimensional conductors, quantum information science, and the physics and chemistry of hydrogen interactions with solids. His group uses first-principles computational techniques to study atomic and electronic structure of crystalline, polycrystalline and amorphous materials, interfaces, surfaces, defects, and heterojunctions. Analysis of his recent publications reveals a strong focus on semiconductor materials for quantum technologies, with particular emphasis on GaN, Ga 2 O 3 , and related compounds. His work combines computational materials science with applications in optoelectronics, quantum information, and energy technologies, demonstrating consistent innovation in understanding defects and their role in material properties. Major Awards and Recognitions: Aneesur Rahman Prize for Computational Physics (APS) Materials Theory Award (MRS) Vannevar Bush Faculty Fellowship (DoD) John Bardeen Award (TMS) Medard W. Welch Award (AVS) Highly Cited Researcher (Clarivate Analytics) Professor Van de Walle has mentored numerous successful researchers, including Dr. Fangzhou Zhao (Corbett Prize winner) and Dr. Mark Turiansky (APS Nicholas Metropolis Award recipient). His group maintains strong connections with the UCSB Quantum Foundry and the Solid State Lighting and Energy Electronics Center, demonstrating collaborative research efforts across multiple disciplines. The group actively investigates defects for quantum information science, loss mechanisms in light emitters, nitride semiconductors, halide perovskites, oxides, and hydrogen interactions with materials.

Qianwen Wangمشاهده پروفایل

استادیار

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +۵ مورد دیگر

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

Jan von Delftمشاهده پروفایل

استاد

• Correlated electron systems
• Quantum impurity models
• Quantum criticality
• +۷ مورد دیگر

Jan von Delft is a Professor (chair) at Ludwig-Maximilians-University (LMU) Munich, working in the Faculty of Physics within the Chair of Theoretical Solid State Physics. His research group consists of postdocs, PhD students, and master's students working on various aspects of strongly correlated electron systems, with physical space located at Theresienstr. 37 (Room A420) in Munich. von Delft's research focuses on correlated electron and spin systems, with particular interest in dynamical and transport properties, quantum impurity models, Hund metals, unconventional superconductors, quantum magnets, and quantum criticality. His methodological expertise includes many-body field theory, parquet formalism (FRG), DMFT, and tensor networks (NRG, DMRG, PEPS, XTRG, etc.). His work bridges theoretical concepts with computational approaches to understand complex quantum phenomena in condensed matter systems. He has developed a distinctive emphasis on real-frequency calculations and numerical methods for studying quantum critical phenomena. Analysis of von Delft's recent publications reveals a strong focus on developing and applying advanced computational methods to study strongly correlated electron systems. His group has made significant contributions to numerical renormalization group techniques, tensor network methods, and the parquet formalism for calculating real-frequency correlation functions. His research shows increasing sophistication in handling quantum criticality, particularly in heavy-fermion systems, and exploring unconventional superconductivity mechanisms. Notably, his group has developed specialized computational libraries like KeldyshQFT to make these advanced methods more accessible to the broader physics community. von Delft actively mentors a substantial research group consisting of one postdoc (Markus Scheb), eleven PhD students (Anxiang Ge, Sasha Kovalska, Mathias Pelz, Marc Ritter, Nepomuk Ritz, Changkai Zhang, Markus Frankenbacher, Felipe Picoli, Simone Fodera, Ming Huang), and two master's students (Ester Pages, Gianluca Grosso). His detailed Style Guide for scientific communication demonstrates his commitment to high-quality research presentation. The group appears well-funded with ongoing research activities spanning theoretical development, computational implementation, and physical interpretation of complex quantum phenomena.

Brent Pymمشاهده پروفایل

دانشیار

• Mathematical Physics
• Algebraic Geometry
• Noncommutative Geometry
• +۳ مورد دیگر

Brent Pym is an Associate Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on the intersection of differential, algebraic, and noncommutative geometry, with a particular emphasis on Poisson varieties and deformation quantization. He has held academic positions at the University of Edinburgh, University of Oxford, and was a Postdoctoral Fellow at McGill and the University of Toronto. Education: BScE in Engineering Physics, Queen's University (2007) MSc in Mathematics, University of Toronto (2008) PhD in Mathematics, University of Toronto (2013) Research Interests: Pym studies Poisson structures, their quantizations, and connections to mathematical physics. His work involves classical/derived algebraic geometry, D-modules, moduli spaces, the Stokes phenomenon, and multiple zeta values. Recent projects include holonomic Poisson manifolds, log symplectic structures, and software for symbolic calculations in deformation quantization. Awards: Lichnerowicz Prize (2018) Advising & Grants: Pym has openings for graduate students (admission 2026) and undergraduate projects (2026–27). He develops the Star Products software package for symbolic calculations in Poisson brackets and quantization. His work is supported by research collaborations and institutional grants. Labs & Teams: Pym collaborates with researchers in geometry and mathematical physics, contributing to projects in noncommutative algebra and geometric quantization. His software tools enhance symbolic computation in these fields.

• Causal Inference
• Representation Learning
• Generative Models
• +۵ مورد دیگر

Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.