جست‌وجوی استاد و پژوهشگر

Hao Shenمشاهده پروفایل

استاد

• Stochastic Partial Differential Equations
• Quantum Field Theory
• Statistical Mechanics
• +۵ مورد دیگر

Hao Shen is a Professor in the Department of Mathematics at the University of Wisconsin-Madison. His academic work focuses on stochastic partial differential equations and their connections with quantum field theory, statistical mechanics, and geometric flows. Dr. Shen's educational background includes a PhD from Princeton University in 2013 under the supervision of Weinan E. He completed postdoctoral training at the University of Warwick with Martin Hairer (2014-2015) and served as a Ritt Assistant Professor at Columbia University with Ivan Corwin (2015-2018) before joining UW-Madison. Hao Shen's research interests span multiple areas of mathematical physics and probability theory: Stochastic partial differential equations (SPDEs) Quantum field theory, particularly Yang-Mills theory and gauge theories Statistical mechanics and interacting particle systems Geometric flows, including Ricci flow Stochastic quantization methods Renormalization theory for singular SPDEs His recent publications demonstrate a strong focus on the mathematical foundations of quantum field theories through stochastic methods, with particular attention to Yang-Mills theory in various dimensions and the O(N) sigma model. Shen's work often bridges rigorous mathematical analysis with physical applications, employing advanced techniques from regularity structures, paracontrolled distributions, and stochastic analysis. Simons Fellow in Mathematics (2024-2025) NSF CAREER DMS-2044415 (2021-2026) NSF DMS-1954091 (2020-2023) NSF DMS-1712684 / DMS-1909525 (2017-2020) As an academic leader, Dr. Shen serves on the editorial boards of prestigious journals including Annals of Probability, Stochastics and Partial Differential Equations: Analysis and Computations, and Annales de l'Institut Henri Poincaré. He actively contributes to the mathematical community through organizing conferences and summer schools, such as the 2024 MSRI/SLMath Summer Graduate School on "Stochastic quantization" and the upcoming 2025 SLMath semester program "Recent Trends in Stochastic Partial Differential Equations." Dr. Shen's teaching portfolio includes advanced courses in probability theory, stochastic analysis, and specialized topics in stochastic partial differential equations. His commitment to education is evident in his development of graduate-level courses that bridge theoretical mathematics with applications in physics.

Ulrich F Keyserمشاهده پروفایل

استاد

• Applied Physics
• Biophysics
• Nanotechnology
• +۳ مورد دیگر

Professor Ulrich F Keyser is a faculty member at the University of Cambridge, affiliated with the Cavendish Laboratory and the Physics of Medicine department. His research focuses on applied physics in biological and soft systems, utilizing advanced techniques such as nanopore sensing, microfluidics, and DNA nanostructures. Keyser's work bridges biophysics, nanotechnology, and molecular biology, with applications in biomedical engineering and biomimetic systems. Research interests include: Single-molecule sensing and manipulation Nanopore-based diagnostics and RNA/DNA analysis Synthetic cell models and membrane biophysics DNA/RNA nanostructures and programmable sensors Ion transport and molecular dynamics in confined systems Biophysics of protein oligomers and nucleic acid modifications His recent publications highlight advancements in nanopore detection of RNA mutations, DNA structural studies, and programmable biosensing technologies. Keyser's laboratory employs interdisciplinary approaches combining physics, chemistry, and biology to address fundamental and applied questions in biomedical and soft matter systems.

• Conformal Field Theory
• Quantum Field Theory
• Renormalization Group
• +۴ مورد دیگر

Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.

• Dark Matter
• Particle Astrophysics
• Cosmology
• +۲ مورد دیگر

Pearl Sandick is a Professor in the Department of Physics and Astronomy and Interim Dean in the College of Science at the University of Utah. She has previously served as Associate Chair of the Department of Physics and Astronomy and Associate Dean for Faculty and Research in the College of Science. Her academic journey at the University of Utah began in 2011 as an Assistant Professor, progressing to Associate Professor in 2017, and achieving the rank of Professor in 2022. Her educational background includes: BA in Mathematics from New York University (2003) PhD in Physics from the University of Minnesota (2008) Sandick is a theoretical particle physicist whose research focuses on physics beyond the Standard Model, with particular emphasis on dark matter. Her work spans theoretical modeling, connections to astrophysical observations, and implications for experimental detection. She investigates various dark matter candidates and their potential signatures in current and future experiments, including collider searches, direct detection experiments, and indirect detection through astrophysical observations. Her research also extends to connections between particle physics and cosmology, including early universe phenomena and implications for cosmic structure formation. She has developed computational tools like MADHAT for dark matter analysis and has made significant contributions to understanding how stellar evolution can constrain axion physics. Her scholarly contributions have been recognized with several prestigious awards: University of Utah Early Career Teaching Award (2016) University of Utah Distinguished Mentor Award Linda K. Amos Award for Distinguished Service to Women University of Utah Presidential Scholar Sandick has been actively involved in mentoring graduate students, as evidenced by her teaching of PhD thesis research and Master's research courses. She has secured significant research funding from the National Science Foundation and other agencies to support her work on dark matter, dark energy, and new physics. Her grant portfolio includes projects on theoretical particle physics, connections to astrophysical observations, and studies on graduate education reform following a departmental tragedy. She is an active member of the American Physical Society, having served as Chair of the regional Four Corners Section in 2021-2022, demonstrating her commitment to the broader physics community and leadership in her field.

Soroosh Sorooshianمشاهده پروفایل

استاد

• Hydrometeorology
• Water Resources Systems
• Climate Studies
• +۳ مورد دیگر

Soroosh Sorooshian is a Professor at the Samueli School of Engineering , University of California, Irvine, with joint appointments in Civil and Environmental Engineering and Earth System Science . He serves as Founding Director of the Center for Hydrometeorology and Remote Sensing (CHRS) and holds the Samueli Endowed Chair in Engineering . His expertise spans hydrometeorology, climate-water interactions, remote sensing applications, and water resource management in arid regions. Education : Ph.D. in Engineering (1978), Engineer Degree in Systems Engineering (1977), M.S. in Operations Research (1973), B.S. in Mechanical Engineering (1971). Leadership & Affiliations : Member of US National Academy of Engineering , International Academy of Astronautics , and multiple scientific bodies (AAAS, AGU, AMS, IWRA). Former advisor to NASA, NOAA, and UNESCO initiatives. Recent research focuses on machine learning integration for hydrological modeling , satellite precipitation product development , and climate change impact assessments . Key trends include deep learning for bias correction , multi-sensor precipitation fusion , and atmospheric river hydrology in California. Awards include the AGU Horton Medal , NASA Distinguished Public Service Medal , and Prince Sultan Bin Abdulaziz International Water Prize . He consults on urban flooding and surface hydrology challenges. Scientific Honors : Chinese Academy of Sciences Einstein Professorship (2014) UNESCO Great Man-Made River Water Prize (2007) AMS Walter Orr Roberts Lecturer (2009) Multiple Distinguished Educator Awards Advisory Roles : Served on committees for NASA, DOE, and World Climate Research Programme's Hydrology Commission.

• Ocean Renewable Energy
• Robotics
• Biomechanics
• +۲ مورد دیگر

Dr. Boyin Ding is an Associate Professor at the University of Adelaide , serving as Academic Director at Haide College and researcher in the Mechanical Engineering department within the Faculty of Sciences, Engineering and Technology. He leads the Wave Energy Research initiative established in 2014, while also contributing to Robotics and Biomechanics through his work with the Flinders Medical Device Research Institute. Research Areas: Ocean Wave Energy Harvesting Control Systems for Renewable Energy 6DOF Robotic Testing Spine Biomechanics Transnational Education Programs Key Collaborations: Australia-China Joint Research Centre for Offshore Wind & Wave Energy Acoustics, Vibration and Control Research Group Scientific Awards: Australian Endeavour Fellowship Malcolm Kinnaird Engineering Excellence Award (2012) His recent publications focus on hybrid offshore energy systems, nonlinear hydrodynamics in wave energy converters, and biomechanical testing technologies. He has developed control algorithms for floating offshore wind-wave systems and pioneered 6DOF robotic platforms for medical applications. As an eligible PhD supervisor, he actively collaborates with global industries and academic institutions.

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

Le-Nam Tranمشاهده پروفایل

پژوهشگر

• Wireless Communications
• 5G/6G Network Optimization
• Signal Processing
• +۴ مورد دیگر

Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation

Roberto Zaninoمشاهده پروفایل

استاد

• Computational fluid dynamics
• Nuclear fusion
• Concentrated solar power
• +۵ مورد دیگر

Roberto Zanino is a Full Professor of Nuclear Engineering at the Department of Energy (DENERG) of the Polytechnic of Turin, Italy. He serves as Advisor to the Rector for relations with European and international university networks and for the UniTe project, Undergraduate Research Opportunities Coordinator, and Project management functions of PoliToArgentina. He is also Scientific Advisor for the Partnership Agreement with NEWCLEO. Dr. Zanino earned his Laurea cum laude in Nuclear Engineering from Politecnico di Torino in 1984 and his Ph.D. in Energetics in 1989. His academic progression includes Assistant Professor (1990-91), Associate Professor (1992-2000), and Professor (2001-present). He previously served as Director of Alta Scuola Politecnica (2007-2010) and Head of the Graduate Program in Energetics (2011-present). His research spans computational fluid dynamics, concentrated solar power, controlled thermonuclear fusion, Generation IV nuclear fission reactors, and plasma physics. His work focuses on thermal-hydraulic analysis of liquid metal systems, superconducting magnet design for fusion applications, and concentrated solar power optimization. His recent publications demonstrate strong expertise in coupling computational tools for nuclear applications, particularly in CFD-system code integration for liquid metal systems and fusion magnet analysis. Dr. Zanino has received recognition as an IEEE Senior Member (2012) and has supervised numerous doctoral students working on topics including thermal-hydraulic analysis of heavy liquid metal systems, superconducting magnet simulation for fusion applications, and concentrated solar power modeling. He has extensive international experience, having worked at Max-Planck-Institut für Plasmaphysik, Massachusetts Institute of Technology, and University of Illinois at Chicago. He is actively involved in major fusion projects including ITER, DTT (Divertor Tokamak Test facility), and EUROfusion. His teaching portfolio includes Computational Heat and Mass Transfer, Nuclear Fusion Reactor Engineering, Solar Thermal Technologies, and Computational Thermal Fluid Dynamics at both master's and doctoral levels.

Kenneth Burchمشاهده پروفایل

استاد

• Condensed Matter Physics
• Materials Science
• Topological Insulators
• +۴ مورد دیگر

Kenneth Burch is the John H. Rourke Professor of Physics and Chairperson of the Department of Physics at Boston College. He holds a Ph.D. from the University of California at San Diego, with prior degrees from UC Santa Cruz. His research focuses on spectroscopic investigations of novel solids, interfaces, and nano-materials, particularly in topological insulators, unconventional superconductors, and 2D atomic crystals. His work explores phenomena such as spin/valleytronics, thermoelectrics, and magnetic proximity effects. Education: B.S., University of California at Santa Cruz M.S., University of California at San Diego Ph.D., University of California at San Diego Research Interests: His studies emphasize the interplay between electronic structure, magnetism, and light-matter interactions in quantum materials. Key areas include: 2D van der Waals heterostructures Magnetic and topological phase transitions Ultrafast carrier dynamics in photodoped systems Design of novel materials for energy and sensing applications Recent Work Trends: Recent publications highlight advancements in layered magnetism (e.g., Cr2Te3 ferromagnetism), correlated insulators (La2O3Mn2Se2), and ultrafast dynamics in Mott insulators. His work often integrates optical spectroscopy, nanofabrication, and theoretical modeling to uncover emergent phenomena. Labs & Collaborations: His research group focuses on experimental condensed matter physics, with access to advanced facilities for nanoscale material synthesis and characterization. Collaborative projects include developing graphene-based sensors for environmental monitoring and studying topological phases in kagome lattices.

Cam Ha Tranمشاهده پروفایل

استادیار

• Neuroscience
• Cardiovascular Physiology
• Neurovascular Coupling
• +۴ مورد دیگر

Cam Ha Tran is an Assistant Professor in the Department of Physiology and Cell Biology at the University of Nevada, Reno, affiliated with the Institute of Neuroscience. Her research focuses on neurovascular unit interactions, particularly how blood flow regulation impacts brain function under health and disease conditions such as stroke and dementia. She employs advanced techniques like two-photon imaging, optogenetics, and electrophysiology to study astrocyte-endothelial communication and vascular reactivity. Education: PhD in Cardiovascular and Respiratory Sciences from the Cumming School of Medicine, University of Calgary (Canada); Master of Biomedical Technology and Bachelor of Science from the University of Alberta (Canada). Research emphasizes understanding how astrocytes and endothelial cells coordinate to maintain cerebral blood flow, with implications for neurological disorders. Her recent work explores TRPA1 channels in neurovascular coupling, astrocyte dysfunction in Alzheimer’s, and seizure-induced vascular changes. Techniques include in vivo imaging and chemogenetic approaches to dissect cellular mechanisms. Key contributions include uncovering astrocyte roles in functional hyperemia and identifying therapeutic targets for cerebrovascular diseases. Her lab’s findings bridge basic science and clinical applications, aiming to improve diagnostics and treatments for stroke and neurodegenerative conditions.

• Optical Solitons
• Quantum Optics
• Nonlinear Chromatic Dispersion
• +۳ مورد دیگر

Dr. Anjan Biswas is a Professor in the Department of Math & Physics at the College of Arts & Sciences. His research focuses on analytical and computational approaches to quantum optics and nonlinear wave propagation. Education: Ph.D. in Mathematics from the University of New Mexico. Metrics: Erdos Number 4, Hirsch Index 111. Research interests center on optical solitons in fibers with linear and nonlinear chromatic dispersion, Lie symmetry analysis for quiescent solitons, and stochastic modeling in photonic systems. His work explores fractional temporal evolution, polarization-mode dispersion, and Kudryashov’s power-law structures. Recent publications highlight advancements in cubic-quartic soliton modeling, dispersion triplet systems, and noise-resilient optical communication frameworks. Studies span Boussinesq-type models, magneto-optic waveguides, and metamaterial applications. Key methodologies include Adomian decomposition, Sardar sub-equation techniques, and Lie symmetry analysis. Applications range from quantum fluid dynamics to internet bottleneck suppression via exotic self-phase modulation structures.

Annabelle Bohrdtمشاهده پروفایل

استاد

• Theoretical Physics
• Quantum Many-Body Systems
• Quantum Simulation
• +۳ مورد دیگر

Annabelle Bohrdt is a Professor at the University of Regensburg's Faculty of Physics, affiliated with the Institute of Theoretical Physics. Her research focuses on strongly interacting quantum many-body systems, combining numerical methods, quantum simulation experiments, and machine learning techniques like neural networks. She explores topics such as the Fermi-Hubbard model, t-J model, and stripe formation in doped antiferromagnets. Her work bridges theoretical models and experiments, leveraging quantum gas microscopy and interpretable machine learning tools. Notable contributions include studies on pairing mechanisms in bilayer antiferromagnetic Mott insulators and the role of fluctuations in quantum many-body systems. Bohrdt actively collaborates with experimental groups to validate theoretical predictions. Teaching includes specialized courses on numerical methods for quantum many-body systems and machine learning applications in physics. She advises graduate and undergraduate students on projects in quantum matter and computational physics.

Robert Seiringerمشاهده پروفایل

استاد

• Quantum many-body systems
• Mathematical physics
• Quantum statistical mechanics
• +۵ مورد دیگر

Robert Seiringer is a Professor at the Institute of Science and Technology Austria (ISTA). His academic career spans institutions including McGill University (Associate Professor, 2011–2013) and Princeton University (Assistant Professor, 2003–2010; Postdoc, 2001–2003). His research focuses on quantum many-body systems , mathematical physics , and quantum statistical mechanics , with significant contributions to Bose-Einstein condensation, superfluidity, and polaron models. Research Trends : His recent work explores superconductivity (BCS theory), quantum gases (Bose-Einstein condensation, Landau-Pekar equations), and mathematical tools for non-perturbative analysis. Key subfields include stability of many-body systems, spectral theory, and Density Functional Theory. Awards : Fellow, American Mathematical Society Corresponding Member, Austrian Academy of Sciences ERC Advanced Grant Henri Poincaré Prize (IAMP) Alfred P. Sloan Fellow Erwin Schrödinger Fellow Collaborations : Leads a research group at ISTA with postdocs (e.g., Davide Desio, Lorenzo Pigozzi) and collaborators (e.g., C. Hainzl, B. Schlein).

Vagelis Papalexakisمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Vagelis Papalexakis is an Associate Professor and Ross Family Chair in the Computer Science & Engineering Department at the University of California, Riverside. His research focuses on data science, machine learning, and tensor methods, with applications in multi-aspect/multi-modal data analysis. He holds a Ph.D. from Carnegie Mellon University and a Diploma/M.Sc. from the Technical University of Crete. Affiliations: Ross Family Chair, Bourns College of Engineering, UCR Education: Ph.D. in Computer Science, Carnegie Mellon University M.Sc./Diploma in Electronic & Computer Engineering, Technical University of Crete His work emphasizes interpretable insights from complex datasets, including tensor-based defenses against adversarial attacks, graph representation learning, and scalable algorithms for high-dimensional data. Notable awards include the NSF CAREER Award (2021), IEEE DSAA Next Generation Award (2021), and ICDM Tao Li Award (2022). Grants include NSF funding for railway safety (CISE MSI: RPEP CPS), USDOT transportation research, and NVIDIA GPU grants. He leads projects in AI ethics, misinformation detection, and gravitational wave analysis. His lab collaborates with industry (e.g., Cisco, Instacart) and national labs (e.g., Lawrence Livermore).