جستجوی استاد راهنمای دانشگاهی

Yee Whye Tehمشاهده پروفایل

استاد

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +۴ مورد دیگر

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

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.

• Functional Analysis
• Artificial Intelligence
• Foundations of Computational Mathematics
• +۱۵ مورد دیگر

Professor Anders C. Hansen is a mathematician at the University of Cambridge and University of Oslo, leading the Applied Functional and Harmonic Analysis group. His work bridges functional analysis, artificial intelligence, and computational mathematics, focusing on the Solvability Complexity Index (SCI) hierarchy and stability issues in deep learning. He has held prestigious fellowships, including a Royal Society University Research Fellowship and Peterhouse Bye-Fellowship. Educated at the University of Cambridge, UC Berkeley, and the Norwegian University of Science and Technology Developed groundbreaking theories in compressed sensing and deep learning, revealing algorithmic instability paradoxes Organized workshops on computational mathematics and AI interpretability His research explores the SCI hierarchy , exposing computational barriers in AI, quantum mechanics, and inverse problems. Key projects include Smale’s 18th problem and analyzing neural network stability. His work has transformed understanding of compressed sensing, particularly in medical imaging. Recent scientific awards include the Whitehead Prize (2019), IMA Prize (2018), and Leverhulme Prize (2017). Collaborations span institutions like Caltech, MIT, and the University of Vienna. As an educator, he teaches NST Part IA Mathematical Methods , Part II Numerical Analysis , and a Part III course on Compressed Sensing . His group has mentored 17 PhD and postdoctoral researchers since 2012.

Simo Särkkäمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +۷ مورد دیگر

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

• Combinatorics
• Operator Theory
• Graph Theory
• +۲ مورد دیگر

Sergio Bermudo Navarrete is a Professor at the Department of Economics, Quantitative Methods and Economic History, Universidad Pablo de Olavide. His research spans combinatorics, graph theory, and operator theory. Education: PhD in Mathematics (2003, University of Seville) with thesis on functional models of operators in Hilbert spaces. Research Interests: Focus on graph theory, domination problems, topological indices, and operator theory. Publication Trends: Recent work includes vertex-degree-based indices for oriented graphs, domination parameters in product graphs, and differential analysis of line graphs. Keywords span Computer Science , Mathematical Chemistry , and Operations Research . Collaborations: Frequent co-authors: José María Sigarreta Almira, José Manuel Rodríguez García, Juan Alberto Rodríguez-Velazquez. Contact: Email sbernav@upo.es .

Ali Mostafazadehمشاهده پروفایل

استاد

• Mathematical Physics
• Quantum Mechanics
• Scattering Theory
• +۳ مورد دیگر

Ali Mostafazadeh is a Professor at the Department of Mathematics, College of Sciences, Koç University. His research spans Mathematical Physics, focusing on Quantum Mechanics, Scattering Theory, and PT-Symmetry. He has made significant contributions to understanding non-Hermitian Hamiltonians, electromagnetic wave propagation, and geometric scattering phenomena. Education: PhD in Physics (1994) from The University of Texas, BA in Physics and Mathematics (1989) from Boğaziçi University His work explores the intersection of mathematics and physics, particularly through spectral singularities, transfer matrix methods, and nonlinear optical systems. Recent publications highlight advancements in broadband directional invisibility, exact Born approximations, and time-dependent Hilbert spaces in quantum systems. 2011 Outstanding Success Award 2007 TÜBİTAK Science Award 2006 Werner-von-Siemens Excellence Award 2001 TÜBA Outstanding Young Scientists Award 2001 Parlar Foundation Research Incentive Award

Paul Larsonمشاهده پروفایل

استاد

• Set Theory
• Topology
• Model Theory
• +۳ مورد دیگر

Paul Larson is a Professor of Mathematics at Miami University. His research focuses on set theory, topology, and model theory, with particular expertise in forcing axioms, descriptive set theory, and infinitary logic. He holds a Ph.D. in Mathematics from the University of California, Berkeley. His work bridges foundational mathematical logic with applications in topology and combinatorics. Key contributions include studies on canonical models under fragments of the Axiom of Choice, polar forcings, and cardinal characteristics. Larson has collaborated extensively with leading researchers such as Saharon Shelah and Jindřich Zapletal. His publications span prestigious journals like the Annals of Pure and Applied Logic and Transactions of the American Mathematical Society. Beyond research, he contributes to the academic community through editorial work and expository writings on historical developments in determinacy theory. Education: Ph.D., Mathematics, University of California, Berkeley Research interests emphasize foundational questions in set theory with applications to topology and model theory. His recent work explores advanced forcing techniques, square principles in Pmax extensions, and combinatorial properties of cardinal invariants. Publications reflect interdisciplinary engagement, including crystal structure prediction in high-pressure chemistry and operator theory in functional analysis. Despite an extensive publication record, no specific scientific awards are documented here. His advising and grant activities remain unspecified in the provided texts. Collaborations span international institutions, reflecting his role as a central figure in contemporary set theory research.

Frank Pollmannمشاهده پروفایل

استاد

• Condensed Matter Theory
• Quantum Information Theory
• Correlated Electrons
• +۶ مورد دیگر

Prof. Dr. Frank Pollmann is a Full Professor (W3) at the Department of Physics PH-I, Technical University of Munich (TUM), leading the Chair of Theoretical Solid-State Physics since 2022. His research focuses on condensed matter theory and quantum information concepts , particularly in systems of correlated electrons and quantum many-body dynamics . PhD: Max Planck Institute for the Physics of Complex Systems / TU Ilmenau (2006) Postdoc: UC Berkeley (2008-2010) Group Leader: MPIPKS Dresden (2011-2016) Associate Professor: TUM (2017-2022) His work spans topological phases , frustrated spin systems , and non-equilibrium quantum dynamics , utilizing tensor network methods and quantum information theory to study phenomena like many-body localization and Hilbert space fragmentation . His publications demonstrate trends in quantum scar states , Kardar-Parisi-Zhang hydrodynamics , and quantum transport anomalies . Scientific Awards : ERC Consolidator Grant (2017) Walter Schottky Prize (2015) Otto-Hahn Medal (2007) He teaches courses including Advanced Methods in Quantum Many-Body Theory , Solid State Theory , and Topology in Condensed Matter , while leading the Pollmann Group under the TUM School of Natural Sciences.

Ivan Dokmanicمشاهده پروفایل

استادیار

• Machine Learning
• Signal Processing
• Inverse Problems
• +۱۱ مورد دیگر

Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .

• Quantum Optics
• Photonics
• Quantum Information Science
• +۷ مورد دیگر

Klaus Mølmer is a Professor at the Niels Bohr Institute, University of Copenhagen, specializing in Quantum Optics and Photonics. His research spans quantum information, entanglement, and cavity QED, leveraging machine learning and Grover's algorithm for quantum state engineering. His recent work focuses on spin squeezing, Rydberg atom interactions, and mechanical resonator cooling. A leader in quantum simulation and superradiance, he collaborates on cavity-mediated emission and quantum network design. The 15 most recent articles highlight advancements in quantum state manipulation, entanglement protocols, and robust differential phase sensing. These studies bridge theoretical frameworks with experimental applications in cavity QED, Rydberg arrays, and zero-photon detection.

Ana Caraianiمشاهده پروفایل

استاد

• Number Theory
• Arithmetic Geometry
• Langlands Program
• +۳ مورد دیگر

Ana Caraiani is a Royal Society University Research Fellow and Professor in the Department of Mathematics at Imperial College London, specializing in Number Theory and Arithmetic Geometry. She is a member of the Number Theory group, focusing on the Langlands program, Shimura varieties, and p-adic Galois representations. Her work bridges arithmetic geometry and representation theory, with contributions to modularity lifting theorems, cohomology of Shimura varieties, and local-global compatibility in the Langlands program. Education: She earned a Ph.D. in Mathematics from Harvard University in 2012. She held positions as a Veblen Research Instructor (2013–2015) and Veblen Fellow (2015–2016) at the Institute for Advanced Study's School of Mathematics. Research Interests: Her research emphasizes the classical and p-adic Langlands programs, Shimura varieties, arithmetic geometry, and moduli stacks of Galois representations. Specific topics include vanishing theorems for cohomology, modularity of elliptic curves over CM fields, and applications of perfectoid spaces. Key Contributions: Caraiani has advanced the proof of modularity of elliptic curves over imaginary quadratic fields, established vanishing theorems for Shimura varieties with torsion coefficients, and contributed to the potential automorphy of Galois representations over CM fields. Her work links geometric approaches to arithmetic conjectures, such as the Sato-Tate and Ramanujan conjectures. Awards and Recognition: Royal Society University Research Fellowship (202?), Veblen Research Instructor/Fellowships (2013–2016), and contributions to major collaborative projects like the Potential Automorphy over CM Fields paper in the Annals of Mathematics.

Valter Morettiمشاهده پروفایل

استاد

• Algebraic Quantum Field Theory
• Black Holes
• Functional Analysis
• +۱۰ مورد دیگر

Valter Moretti is a Full Professor in the Department of Mathematics at the University of Trento. His academic career spans roles from Research Fellow to Full Professor, focusing on Mathematical Physics and Quantum Field Theory (QFT) in curved spacetime. He earned an MSc in Physics from Genova University and a PhD in Theoretical Physics from Trento University. Research Interests : Algebraic QFT, General Relativity, Quantum Mechanics, Operator Algebras, and Spectral Theory. His work bridges mathematical rigor with physical applications, particularly in quantum localization, entanglement, and curved spacetime phenomena. Publications : Authored 15+ recent papers on topics like quantum particle localization, entanglement certification, and QFT on curved backgrounds. Collaborated on a 2022 patent for generating entangled photon states. Awards : Holds a patent for a quantum-certified random number generator (2022). Supervision : Advised 8 PhD students, including N. Pinamonti, L. Franceschini, and C. van de Ven. Coordinated national and international research projects (e.g., H2020-MSCA-COFUND-2015). Labs & Collaborations : Affiliated with INFN, TIFPA-INFN, and Q@TN (Quantum@Trento). Organized conferences like Quantum Physics and Geometry (2014) and Quantum Machine Learning (2023). Teaching : Lectures on Analytical Mechanics, Quantum Relativistic Theories, and Special Relativity. Authored textbooks on Spectral Theory and Quantum Mechanics.

• Inverse Problems
• Applied Harmonic Analysis
• Machine Learning
• +۳ مورد دیگر

Rima Alaifari is an Assistant Professor for Applied Mathematics at ETH Zürich, specializing in inverse problems, applied harmonic analysis, and scientific machine learning. She is an associated member of the ETH AI Center and will assume a full professorship at RWTH Aachen University in 2025. Her work focuses on stability analysis, regularization, and operator learning, with applications in phase retrieval and robustness of neural networks. Alaifari holds a Ph.D. in Mathematics from Vrije Universiteit Brussel (2014), where she studied under Prof. Ingrid Daubechies and Prof. Michel Defrise. She completed her M.Sc. in Applied and Industrial Mathematics at Johannes Kepler University, Linz (2010). Her academic career includes postdoctoral fellowships at ETH Zurich (2014–2016) and a Marie Curie-funded position (2016). Her research interests span inverse problems, phase retrieval, stability in machine learning, and operator learning. Notable projects include SNF-funded work on phase retrieval (2019) and collaborations on adversarial robustness in medical imaging (e.g., CT reconstruction). She has mentored PhD students Tandri Gauksson and Matthias Wellershoff, and postdocs Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner). Teaching includes courses on inverse problems, time-frequency analysis, and robustness of deep neural networks. She actively participates in international conferences, delivering plenary talks at venues like the International Conference on Computational Harmonic Analysis (2022) and ICERM (2023). Her work bridges mathematical foundations and practical applications, emphasizing stability and robustness in computational methods.

Sean Carrollمشاهده پروفایل

استاد

• Cosmology
• Quantum Mechanics
• General Relativity
• +۴ مورد دیگر

Sean Carroll serves as the Homewood Professor of Natural Philosophy at Johns Hopkins University and holds External Faculty status at the Santa Fe Institute. His research bridges cosmology, quantum mechanics, and philosophy, focusing on foundational questions about spacetime emergence, quantum interpretation, and complexity across cosmic scales. Carroll earned his Ph.D. from Harvard University in 1993. His academic trajectory reflects deep engagement with theoretical physics and philosophical inquiry, culminating in his current named professorship at Johns Hopkins. Carroll's research centers on the intersection of physics and philosophy, with significant contributions to quantum foundations, cosmology, and the nature of emergence. He is a leading proponent of the many-worlds interpretation of quantum mechanics and has pioneered work on the thermodynamic arrow of time, quantum decoherence, and the fine-tuning of initial cosmic conditions. His recent investigations explore discretized quantum systems, holographic principles in gravity, and the philosophical implications of quantum gravity. Analysis of his 2022-2025 publications reveals a pronounced shift toward computational approaches in quantum gravity, with increasing emphasis on finite-dimensional Hilbert spaces and GPU-accelerated modeling. His work consistently integrates quantum information theory with cosmological questions, particularly examining how spacetime geometry emerges from quantum entanglement and how complexity evolves in closed systems. Carroll's scientific recognition includes: National Science Foundation Fellowship NASA Fellowship Sloan Research Fellowship Packard Fellowship Fellow of the American Physical Society American Institute of Physics Award Fellow of the Royal Society Guggenheim Fellowship Fellow of the American Association for the Advancement of Science His research has been sustained through major fellowships from NSF, NASA, Sloan, and Packard foundations, enabling interdisciplinary collaborations across physics and philosophy. Carroll actively mentors graduate students at Johns Hopkins and contributes to public discourse through his popular science books (including the Biggest Ideas in the Universe series) and the weekly Mindscape podcast. As Fractal Faculty at the Santa Fe Institute, Carroll participates in cross-disciplinary research on complex systems, exploring how emergent phenomena arise from fundamental physical laws. His work bridges theoretical physics with broader questions about complexity in biological, cognitive, and social systems.

Xu Jinchaoمشاهده پروفایل

استاد

• Multigrid methods
• Machine learning
• Finite element methods
• +۵ مورد دیگر

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.