جست‌وجوی استاد و پژوهشگر

• AI-driven Medical Imaging
• Biomedical Data Analysis
• Data Quality Transfer
• +۷ مورد دیگر

Dr. Guang Yang is an Associate Professor in the Department of Bioengineering and Imperial-X at Imperial College London's Faculty of Engineering. His research focuses on AI-driven techniques for imaging and biomedical data analysis, particularly in translational healthcare applications such as fast imaging, federated learning, and generative AI. Current projects address challenges in cardiovascular disease, lung disease, and oncology. He leads the AYL Lab, emphasizing innovation, collaboration, and ethical AI practices. Dr. Yang holds editorial roles across journals including npj Digital Medicine and Medical Image Analysis . He is a UKRI Future Leaders Fellow and has secured substantial funding (e.g., £1.7M UKRI Fellowship, £8.7M ERC H2020 grant). The lab includes 1 PI, 7 PDRAs, 14 PhD students, and 10+ MRes/MSc students, fostering a culture of professional growth and inclusivity. His research interests span AI ethics, federated learning, explainable AI, and medical imaging applications. Recent work includes analyzing AI autophagy risks, drug discovery via machine learning, and non-invasive imaging techniques. Key collaborations involve industry partners and institutions like the Francis Crick Institute. Teaching includes modules on trustworthy AI in medical imaging and image processing. Awards include IEEE Senior Member and SPIE Life Member statuses. The lab prioritizes mental health and work-life balance, promoting open communication and career development for members.

Samuel Hubermanمشاهده پروفایل

استادیار

• Thermal Transport
• Condensed Matter Physics
• Molecular Dynamics
• +۱ مورد دیگر

Samuel Huberman is an Assistant Professor in Chemical Engineering at McGill University, leading research in thermal transport phenomena, condensed matter physics, and computational methods. His lab investigates phonon hydrodynamics, nanoscale heat transfer, and materials for energy applications using density functional theory, molecular dynamics, and ultrafast spectroscopy techniques. Research focuses on: Phonon transport in 2D materials and nanostructures Hydrodynamic heat transport modeling beyond Fourier's law Experimental techniques like frequency-domain photoreflectance Solid-state electrolytes for battery technologies Recent publications demonstrate innovations in thermal transport theory and characterization methods applied to semiconductors, fluorides, and alloy systems.

Jacob Steinhardtمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• AI Safety
• +۷ مورد دیگر

Jacob Steinhardt is an Assistant Professor in the Department of Statistics at UC Berkeley, where he is also part of BAIR (Berkeley Artificial Intelligence Research) and CLIMB. His research focuses on ensuring machine learning systems are understood by and aligned with humans, addressing critical challenges in AI safety and reliability. Dr. Steinhardt's research centers on three main directions: Robustness - developing models resilient to distributional shifts, adversaries, and model mis-specification; Reward specification and reward hacking - creating methods to infer complex value functions from data and prevent degenerate policies; and Scalable alignment - designing ML systems that conform to interpretable abstractions despite their large scale. His work rethinks both theoretical and empirical paradigms of machine learning to address these critical challenges to AI safety. An analysis of his recent publications reveals a consistent focus on understanding the internal mechanisms of neural networks, particularly large language models, to improve their alignment with human values. His research spans interpretability techniques, safety mechanisms, and evaluation frameworks, with significant contributions to understanding reward hacking, distributional shift, and model transparency. His work often combines theoretical insights with empirical validation through novel experimental frameworks. Dr. Steinhardt actively mentors a diverse group of PhD students including Ruiqi Zhong (co-advised with Dan Klein), Meena Jagadeesan (co-advised with Mike Jordan), Erik Jones (co-advised with Anca Dragan), and several others. His former students have gone on to positions at leading organizations including OpenAI, Genentech, and the Center for AI Safety. He is the Founder & CEO of Transluce, a non-profit research lab building open, scalable technology for understanding frontier AI systems. Through this initiative and his academic work, he contributes significantly to the growing field of AI safety research, bridging theoretical foundations with practical applications to make machine learning systems more reliable and beneficial.

Eric Mazumdarمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Game Theory
• Economics
• +۵ مورد دیگر

Eric Mazumdar is an Assistant Professor of Computing and Mathematical Sciences and Economics at the California Institute of Technology (Caltech), holding dual appointments in the Division of Engineering and Applied Science and the Humanities and Social Sciences division. His research bridges machine learning and economics with applications in societal-scale systems. His educational background includes a B.S. in Electrical Engineering and Computer Science from MIT (2015) and a Ph.D. in Electrical Engineering and Computer Science from UC Berkeley (2021), where he was co-advised by Michael Jordan and Shankar Sastry. Mazumdar's research focuses on theoretical foundations of learning algorithms in uncertain, dynamic environments interacting with strategic agents . Key areas include algorithmic game theory, reinforcement learning in strategic settings, risk-aware decision-making, and behavioral economics. His work addresses challenges in intelligent infrastructure, online markets, e-commerce, and healthcare delivery through rigorous mathematical frameworks. His publication trends reveal a strong focus on zero-sum stochastic games , distributionally robust reinforcement learning , and strategic classification , with increasing integration of optimization theory and game-theoretic principles. Recent work emphasizes sample efficiency, risk aversion, and equilibrium computation in multi-agent systems. NSF Career Award for studying strategic interactions in Societal-Scale Systems Research Fellowship for Learning in Games from the Simons Institute Mazumdar advises multiple postdocs (Zaiwei Chen, Laixi Shi, Kishan Panaganti) and graduate students (Lauren Conger, Tinashe Handina, Yizhou Zhang), with research funded by NSF, DARPA, and Amazon. His group explores applications in intelligent infrastructure and healthcare systems through collaborations with Caltech's Linde Institute and Center for Social Information Sciences (CSIS).

Daniel Barczykمشاهده پروفایل

دانشیار

• Macroeconomics
• Public Finance
• Family Economics
• +۱ مورد دیگر

Daniel Barczyk is an Associate Professor of Economics at McGill University, specializing in Macroeconomics, Public Finance, and Family Economics. He received his Ph.D. from New York University and holds undergraduate and master's degrees from the University of Toronto. Dr. Barczyk's research examines intergenerational transfers, long-term care policies, and lifecycle savings behavior. His work combines dynamic macroeconomic modeling with empirical analysis to study household decision-making across generations. Current projects investigate housing wealth as old-age insurance mechanisms and family risk-sharing arrangements in different cultural contexts. Dr. Barczyk's publications consistently focus on modeling family dynamics in macroeconomic frameworks, with recent expansion into Asian economies. His methodological approach integrates quantitative theory with policy evaluation. Awards & Grants: Best Paper Award in Economics (LBS 2010) SSHRC Insight Grant (2018-2021) FQRSC Young Scholar Research Grant Professional Service: MA Program Director at McGill Co-organizer of CIREQ Macro Conferences Referee for leading economics journals

Ammar Hakimمشاهده پروفایل

مدرس

• Computational Plasma Physics
• Fusion Energy
• Space Plasmas
• +۶ مورد دیگر

Ammar Hakim is a Lecturer and Principal Research Physicist at Princeton University's Computational Sciences Department. He holds a Ph.D. from the University of Washington (2006) and specializes in computational plasma physics, fusion energy, and high-energy astrophysics. His research focuses on tokamak turbulence, machine learning for PDEs, and electromagnetic simulations using advanced numerical methods like discontinuous Galerkin schemes. Roles: Deputy Head of Computational Sciences, Instructor for AST 560 (Computational Plasma Physics) Key Expertise: Plasma turbulence, kinetic processes, fluid dynamics, and high-performance computing Tools: Gkeyll simulation framework, discontinuous Galerkin methods, gyrokinetic theory Research interests include plasma-material interactions, multi-fluid moment models, and hybrid kinetic-fluid approaches. His recent work addresses challenges in fusion edge plasmas, relativistic astrophysical systems, and formal verification of PDE solvers.

Marc Alexander Schweitzerمشاهده پروفایل

استاد

• Numerical Methods
• Computational Mechanics
• Meshfree Methods
• +۴ مورد دیگر

Marc Alexander Schweitzer is a Professor of Mathematics at the University of Bonn and affiliated with Fraunhofer SCAI. His work focuses on numerical methods and computational mechanics, particularly meshfree methods, peridynamics, and algebraic multigrid techniques. He contributes to advancing computational tools for engineering and materials science applications. Research interests include partition of unity methods, multiscale modeling, fracture mechanics, and efficient linear solvers. His work integrates mathematical theory with practical computational challenges, such as adaptive algorithms and parallel computing. Publications highlight contributions to peridynamic fracture models, scalable solvers for material design, and visualization of fracture progression in particle-based systems. Collaborative efforts with Fraunhofer SCAI emphasize industrial applications and algorithmic innovation.

Padma Raghavanمشاهده پروفایل

استاد

• High-Performance Computing
• Supercomputing
• Algorithm Design
• +۳ مورد دیگر

Padma Raghavan is the Vice Provost for Research and Innovation, Chief Research Officer, and Distinguished Professor of Computer Science at Vanderbilt University. She leads strategic research initiatives, including partnerships like Ancora Innovation and Army Pathfinder. Her academic role focuses on high-performance computing, sparse data algorithms, and energy-efficient systems. She has authored over 100 publications and advised nearly 50 students. Education: Ph.D. and M.S. in Computer Science from Pennsylvania State University; B.Tech. from Indian Institute of Technology. Pre-Vanderbilt roles included Associate Vice President for Research and founding Director of Penn State's Institute for Computational and Data Sciences. Research emphasizes enhancing parallel computing performance through sparse data algorithms, fault tolerance, and energy efficiency. Key contributions include resilient scheduling heuristics and sparse matrix solutions. Awards include NSF CAREER Award, IEEE and AAAS Fellowships. Leadership roles include advising the National Science Foundation and Oak Ridge National Laboratory. Current advisory boards include the President’s Committee on the National Medal of Science. Labs/Teams: FOUNDER of Penn State’s Institute for Computational and Data Sciences. Active in interdisciplinary computational science initiatives.

Joost-Pieter Katoenمشاهده پروفایل

استاد

• Formal Methods
• Probabilistic Programming
• Model Checking
• +۴ مورد دیگر

Joost-Pieter Katoen is a Professor at RWTH Aachen University's Department of Computer Science, part of the School of Computer Science. He leads the MOVES group and focuses on formal methods, probabilistic systems, and program verification. His research includes probabilistic programming, Markov models, Bayesian networks, and stochastic systems. He has co-authored over 200 publications in top venues like AAAI, CAV, and CONCUR, and developed tools like AMBER and SAFEST for probabilistic analysis. Notably, his student Kevin Batz received the ETAPS Dissertation Award in 2025 for work on deductive verification of probabilistic programs. He serves as an editor for Formal Methods and has advised PhD students such as Kevin Batz and Bahare Salmani Barzoki. His work bridges theoretical foundations with practical tools for analyzing complex systems.

• Fractal Geometry
• Mathematical Analysis
• Geometric Measure Theory
• +۶ مورد دیگر

Prof Kenneth Falconer is the Regius Professor of Mathematics at the University of St Andrews, leading the School of Mathematics and Statistics. He specializes in Fractal Geometry and Mathematical Analysis, with contributions to self-affine sets, box-counting dimensions, and fractal stochastic processes. His academic journey includes a PhD from the University of Cambridge and roles at the University of Bristol before joining St Andrews in 1993. He has authored five books, including the seminal Fractal Geometry: Mathematical Foundations and Applications . His research spans over 130 publications, focusing on fractal geometry's theoretical and applied aspects, including Falconer's distance problem and affinity dimension. He has supervised 19 PhD students, many of whom pursue academic careers. Awards include the Shephard Prize (2020), CBE (2024), and Fellowship in the Royal Society of Edinburgh. Teaching responsibilities include courses on Fractal Geometry, Functional Analysis, and Analysis for postgraduates via the Scottish Mathematics and Statistics Training Centre. He has held administrative roles like Head of School and Postgraduate ProDean. Beyond academia, Falconer is an avid long-distance walker, completing 37 LDWA 100-mile events and climbing all Munros and Corbetts in Scotland.

Jonathan Fraserمشاهده پروفایل

استاد

• Fractal Geometry
• Analysis
• Geometric Measure Theory
• +۳ مورد دیگر

Professor Jonathan Fraser is a faculty member in the Department of Pure Mathematics at the University of St Andrews, leading the Analysis Research Group. His primary affiliation is within the School of Mathematics and Statistics, where he holds a professorial position. Fraser's research focuses on fractal geometry, analysis, and their intersections with geometric measure theory, Fourier analysis, and dynamical systems. His educational background includes a PhD from the University of St Andrews (2013), with a thesis on self-affine fractals. He has supervised multiple PhD students, including Ana de Orellana, Tianyi Feng, Yunlong Xu, and Lijian Yang. Fraser's research interests span fractal projections, self-affine sets, Fourier analysis of fractals, and Kakeya-type problems. He has contributed to understanding the dimension theory of fractals via Assouad spectra and intermediate dimensions, with applications to conformal dynamics and additive combinatorics. His awards include the 2024 Fellowship of the Royal Society of Edinburgh, the 2023 Sir Edmund Whittaker Memorial Prize, and the 2022 RSE/Sir Thomas Makdougall Brisbane Medal. He is an editor for the Proceedings of the Royal Society of Edinburgh and Journal of Fractal Geometry . Fraser's work often bridges pure mathematics and stochastic processes, with recent focus on Fourier analytic techniques and dimension interpolation. He leads research projects funded by the Leverhulme Trust and EPSRC, exploring topics like fractal projections and conformal geometry. In addition to academic leadership, Fraser collaborates widely, organizing seminars such as the One World Fractals series. He is actively mentoring new researchers and has upcoming initiatives for PhD and postdoctoral positions in fractal geometry.

James Davenportمشاهده پروفایل

استاد

• Computer Algebra
• Symbolic Computation
• Cryptography
• +۵ مورد دیگر

James Davenport is the Hebron and Medlock Professor of Information Technology at the University of Bath's Department of Computer Science. He holds affiliations with institutions such as the International Centre for Higher Education Management (ICHEM), the Institute of Coding, and the UKRI CDT in Accountable, Responsible, and Transparent AI. His research focuses on computer algebra, cryptography, symbolic computation, and computational geometry, with notable contributions to cylindrical algebraic decomposition and quantifier elimination. He has led or collaborated on projects funded by EPSRC, EU Horizon 2020, and other bodies, addressing challenges in algorithmic complexity, cybersecurity education, and digital skills development. His research interests span computer science education, cybersecurity, and formal methods, with a strong emphasis on advancing symbolic computation techniques. Recent work includes improving cylindrical algebraic decomposition through machine learning and exploring frameworks for authentic assessment in computing education. He actively contributes to initiatives like the Institute of Coding to address the UK’s digital skills gap. Professor Davenport has been involved in 12 research projects, including the DEWCAD project targeting computational geometry challenges. His publications emphasize algorithmic innovation, educational frameworks, and interdisciplinary applications. While no specific awards are listed, his extensive contributions to the field underscore his academic impact. He supervises doctoral students and collaborates with global researchers, contributing to both theoretical advancements and practical applications in education and technology. His work bridges academic research and industry needs, particularly in cybersecurity and computational efficiency.

Robert J. Webberمشاهده پروفایل

استادیار

• Mathematics of Information
• Data Science
• Scientific Computing
• +۵ مورد دیگر

Robert J. Webber is an Assistant Professor in the Department of Mathematics at the University of California, San Diego (UCSD). He holds a Ph.D. in Mathematics from New York University (2021) and previously served as a postdoctoral scholar at the California Institute of Technology (Caltech) from 2021 to 2024, hosted by Joel A. Tropp. Research Focus: Webber specializes in randomized numerical methods for data science and scientific computing, including interdisciplinary applications in chemistry, astronomy, and geophysics. His work emphasizes randomized algorithms for matrix approximation, extreme-scale linear algebra, and rare event sampling. Notable projects include the randomly pivoted Cholesky method for kernel matrices and randomly sparsified Richardson iteration for large-scale linear systems. Teaching: He teaches probability and data science courses at the upper-undergraduate and graduate levels at UCSD. His collaborative projects include studying Mercury’s orbital instability and extreme weather patterns using rare event sampling techniques. Publications: Over 30 peer-reviewed articles since 2019, with notable contributions to journals like Communications on Pure & Applied Mathematics and SIAM Journal on Scientific Computing . His work has been featured in Forbes , SIAM News , and the California Business Journal .

Olivier Sèteمشاهده پروفایل

پژوهشگر

• Numerical Mathematics
• Computational Complex Analysis
• Approximation Theory
• +۳ مورد دیگر

Olivier Sète is a postdoctoral researcher in mathematics at Universität Ulm. His work focuses on numerical mathematics, computational complex analysis, and their applications in mathematics and sciences. He has held teaching roles at institutions including TU Berlin and Universität Greifswald, covering courses such as Numerical Linear Algebra, Analysis, and Partial Differential Equations. His research interests include approximation theory, numerical methods for the Helmholtz equation, conformal mapping, potential theory, and harmonic mappings in astrophysics. Notable contributions include the AAA algorithm for rational approximation, which earned the Frontiers of Science Award in 2023. His research spans topics like lemniscatic domains, polynomial pre-images, and stochastic Galerkin methods. He has developed software tools like the Transport of Images Toolbox for computing zeros of harmonic mappings. His work bridges theoretical mathematics with computational applications, addressing challenges in complex analysis and numerical methods. Awards include the Frontiers of Science Award for his work on the AAA algorithm. He has collaborated extensively with researchers such as Yuji Nakatsukasa and Lloyd N. Trefethen. His academic background includes a PhD from TU Berlin (2016) and a Diplom from TU Berlin (2009).

Sam Wolffمشاهده پروفایل

استادیار

• Single-cell technologies
• Stem cell biology
• Cell cycle dynamics
• +۷ مورد دیگر

Sam Wolff, PhD, is an Assistant Professor in the Department of Genetics at the UNC School of Medicine. His research focuses on single-cell technologies to study stem cell biology, cell cycle dynamics, and the molecular mechanisms governing cell fate decisions. He utilizes CRISPR, live-cell imaging, and iterative immunofluorescence (4i) techniques to investigate pluripotency regulation in human embryonic stem cells (hESCs). His work includes understanding transcriptional heterogeneity during directed differentiation of hESCs into definitive endoderm, leveraging single-cell RNA sequencing and computational tools. Dr. Wolff received a UNC Computational Medicine Program Pilot Award (2022) for collaborative research with Dr. Phil Spanheimer. His lab explores cell cycle proteins' roles in breast cancer resistance and stem cell differentiation, combining proteomic and transcriptomic data for comprehensive insights. He has published extensively in journals such as *Science*, *Molecular Systems Biology*, and *Cell Systems*. Recent mentees include Lauryn Bailey in the PRISM program. Education and Training: While specific educational details are not explicitly stated in the provided text, his research trajectory suggests advanced training in genetics, molecular biology, and biomedical sciences. His work integrates computational approaches with wet-lab techniques, indicating interdisciplinary expertise. Research Interests: His lab's projects include: (1) Single-cell analysis of OCT4 inheritance and its role in stem cell fate; (2) Development of the Cell Cycle Browser tool for modeling cell-cycle progression; (3) Investigation of breast cancer cell resistance to palbociclib using 4i imaging; (4) Improving directed differentiation protocols for definitive endoderm via RNA velocity analysis. These projects highlight a focus on translational research in regenerative medicine and cancer biology. Grants and Awards: In addition to the 2022 pilot award, his research has been supported by institutional funding at UNC, as evidenced by publications and lab activities. Labs/Teams: The Wolff Lab collaborates with the UNC Computational Medicine Program and maintains active partnerships within the Department of Genetics. They host visiting scholars and trainees through programs like PRISM and the Wake Tech Community College START Program.