جستجوی استاد راهنمای دانشگاهی

• Condensed Matter Physics
• Superconductivity
• 2D Materials
• +۲ مورد دیگر

Eryin Wang is a Research Fellow at the Center for Free-Electron Laser Science (CFEL), part of the Max Planck Institute for the Structure and Dynamics of Matter in Hamburg, Germany, where he contributes to the Quantum Condensed Matter Dynamics Group within the Condensed Matter Dynamics Department. His research centers on condensed matter physics with emphases on photo-induced superconductivity, 2D materials, and ultrafast dynamics. He investigates light-driven quantum phenomena in systems like K 3 C 60 and van der Waals heterostructures (graphene/h-BN, transition metal dichalcogenides), employing angle-resolved photoemission spectroscopy (ARPES) and ultrafast laser techniques to probe non-equilibrium states. His work reveals critical insights into metastable superconducting phases and electronic structure modifications under optical excitation. Analysis of recent publications shows a cohesive trajectory exploring light-matter interactions to induce and characterize novel quantum states. His K 3 C 60 studies demonstrate ultrafast critical currents and nonlinear transport in photo-induced superconductors, while 2D materials research elucidates stacking-dependent electronic properties and plasmaron effects. This integration of ultrafast electrical measurements with spectroscopic methods defines his experimental approach. His scientific recognition includes: Humboldt Fellowship No information is available regarding academic advising or research grants from provided sources. Dr. Wang operates within CFEL's Quantum Condensed Matter Dynamics Group, utilizing advanced free-electron laser and ultrafast facilities to investigate quantum material dynamics at femtosecond timescales.

Martin Wainwrightمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Martin Wainwright is the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at MIT, affiliated with the Laboratory for Information and Decision Systems and the Statistics and Data Science Center. He joined MIT in 2022 from UC Berkeley, where he held the Howard Friesen Chair. He earned his B.Math from the University of Waterloo and his PhD from MIT in Electrical Engineering and Computer Science. His research focuses on statistics, machine learning, information theory, and optimization. Wainwright has received numerous awards, including the COPSS Presidents’ Award, Blackwell Award, and Sloan Fellowship. He co-authored influential books on graphical models, sparse statistical modeling, and high-dimensional statistics. His work spans theoretical foundations and applications in reinforcement learning, statistical inference, and algorithmic design. He advises students in interdisciplinary areas and leads research initiatives at the MIT Institute for Data, Systems, and Society (IDSS), fostering collaborations across engineering, statistics, and data science.

• Statistical Physics
• Condensed Matter Physics
• Biological Physics
• +۶ مورد دیگر

Jack Lidmar is an Associate Professor in the Department of Physics at KTH Royal Institute of Technology. His research focuses on statistical physics, condensed matter physics, and biological physics, with emphasis on fluctuation phenomena in superconductors, phase transitions, quantum phase transitions, disordered systems, and the development of efficient Monte Carlo simulation methods. He has contributed to studies of superconducting nanostructures, spin glasses, and biological systems such as virus structural mechanics. Lidmar teaches several courses at KTH, including Computational Physics , Statistical Physics , and Quantum Information , and oversees multiple degree projects. His work integrates theoretical modeling with advanced simulation techniques, such as the Accelerated Weight Histogram (AWH) method, to explore rare events and free energy landscapes in materials and biological systems. Recent research highlights include investigations of superconducting nanostrips, phase transitions in two-dimensional materials, and the application of simulation methods to predict material properties like skin permeability. His interdisciplinary approach bridges condensed matter physics with computational biology and engineering applications.

Robert Bamlerمشاهده پروفایل

استاد

• Deep Generative Models
• Machine Learning
• Probabilistic Inference
• +۳ مورد دیگر

Robert Bamler is a Professor of Data Science and Machine Learning at the University of Tübingen, Germany , and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and the Tübingen AI Center . Current affiliation: University of Tübingen (since November 2020) Prior roles: Postdoctoral Scholar at UC Irvine (with Stephan Mandt), Machine Learning Researcher at Disney Research (Pittsburgh/Los Angeles) Education: PhD in Theoretical Statistical and Quantum Physics from University of Cologne (2016), advised by Achim Rosch Research Focus : Algorithm development for deep generative models (including large language models) Resource-efficient inference and model compression Probabilistic machine learning applications in natural sciences Theoretical foundations in statistics, information theory, and physics Scientific Contributions : German Telekom Foundation PhD scholarship awardee Co-founder of the BamlerLab reading group on probabilistic modeling and compression Key Collaborations : Stephan Mandt (UC Irvine) Bernhard Schölkopf (MPI-IS) Cluster of Excellence "Machine Learning" (Tübingen) Teaching : Lecturer for "Data Compression With and Without Deep Probabilistic Models" (2021-2025) Organizer of weekly reading groups on function-space inference and compression algorithms

• Reinforcement Learning
• Learning in Games
• Applied Probability
• +۲ مورد دیگر

Zaiwei Chen is an Assistant Professor in the Edwardson School of Industrial Engineering at Purdue University. His research focuses on developing theoretical foundations for sequential decision-making under uncertainty, particularly in reinforcement learning, multi-agent systems, and optimization. He leads projects on data-efficient algorithms with applications in control and game theory. He holds a Ph.D. in Machine Learning and dual M.S. degrees in Mathematics and Operations Research from Georgia Institute of Technology, and a B.S. in Electrical Engineering from Zhejiang University. His research bridges applied probability, machine learning, and optimization to create robust algorithms for complex systems. Recent work emphasizes convergence guarantees in multi-agent reinforcement learning and scalable solutions for high-dimensional problems. Chen's publications demonstrate a consistent focus on theoretical guarantees for reinforcement learning algorithms, particularly in stochastic and game-theoretic settings. Key trends include Lyapunov stability frameworks, finite-sample analysis, and methods to overcome computational bottlenecks like the "curse of dimensionality." Awards include: Solberg Academic Excellence Scholarship PIMCO Postdoctoral Fellowship in Data Science (2022) Sigma Xi Best Ph.D. Thesis Award Runner-up for SIGMETRICS Doctoral Dissertation Award He actively recruits PhD students to work on reinforcement learning theory and maintains collaborations with Caltech and Georgia Tech. Future work includes scalable multi-agent learning frameworks and real-world applications in industrial systems.

Ruqi Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Probabilistic Methods
• +۵ مورد دیگر

Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Previously, she was a postdoctoral fellow at the Institute for Foundations of Machine Learning, UT Austin (2021-2022), and earned her PhD in Statistics from Cornell University (2016-2021). Her research focuses on scalable probabilistic methods for machine learning, including alignment of foundation models, uncertainty quantification, and Bayesian deep learning. She has received awards such as the Ross-Lynn Research Scholar Fund and ICML Best Reviewer recognition. Education: PhD in Statistics, Cornell University (2021) MS in Computer Science, Cornell University (2021) Bachelor of Science in Mathematics, Renmin University of China (2012-2016) Research Interests: Trustworthy AI: Safety of LLMs/VLMs, alignment mechanisms Probabilistic Inference: Bayesian methods, MCMC, variational inference Generative Models: Diffusion models, energy-based models Uncertainty Estimation: Calibration, out-of-distribution detection Notable Contributions: Developed the Discrete Langevin Sampler for high-dimensional discrete spaces Pioneered Low-Precision SGLD for efficient Bayesian neural networks Designed DP-Fast MH for privacy-preserving Bayesian inference Teaching: CS57800 - Statistical Machine Learning (2022-Present) CS37300 - Data Mining and Machine Learning (2024) CS59200 - Probabilistic Machine Learning (2022)

Michael Epsteinمشاهده پروفایل

استاد

• Statistical Genetics
• Genetic Epidemiology
• Biostatistics
• +۳ مورد دیگر

Michael Epstein is a Professor in the Department of Biostatistics and Bioinformatics at Emory University. His research focuses on developing statistical methods for human gene mapping of complex traits, with applications to diseases such as Alzheimer’s, breast cancer, epilepsy, and schizophrenia. He holds a Ph.D. and M.S. from the University of Michigan and a B.S. from Duke University. Epstein’s work bridges methodological innovation and applied genetics, addressing challenges in genetic epidemiology. His applied research targets neuropsychiatric disorders and congenital anomalies, leveraging techniques like transcriptome-wide association studies (TWAS), eQTL analysis, and polygenic risk scoring. He teaches BIOS 770 (Statistical Genetics) and collaborates on large consortia initiatives (e.g., BCAC/OCAC). Key areas of expertise include statistical modeling of gene-by-environment interactions, rare variant analysis in family trios, and multi-ancestry genomic studies. His methodological contributions include frameworks like CADET, SCAMPI, and TIGAR for analyzing complex genetic datasets.

Qing Zhouمشاهده پروفایل

استاد

• Causal inference and graphical models
• High-dimensional statistics
• Monte Carlo methods
• +۵ مورد دیگر

Qing Zhou is Professor and Chair of Statistics & Data Science at the University of California, Los Angeles, and a faculty member in the Interdepartmental Program in Bioinformatics. His methodological work integrates causal inference, high-dimensional statistics, Monte-Carlo computation and statistical bioinformatics, with applications ranging from gene-regulatory networks to financial risk models. Education & Affiliations: Professor & Chair, Department of Statistics and Data Science, UCLA Faculty, UCLA Interdepartmental Program in Bioinformatics Ph.D. training in Statistics (implied by faculty rank and publication record) Research Interests: Zhou develops theory and algorithms for learning directed acyclic graphs (DAGs) to uncover causal relations from large-scale data. His group designs penalized likelihood, divide-and-conquer and federated strategies for structure learning of Bayesian networks, accompanied by R packages and scalable software. In high-dimensional inference he introduced “estimator augmentation” to deliver honest confidence sets for lasso-type estimators, and recently proposed projection-and-shrinkage techniques that merge sparse regularization with Stein estimation. Monte-Carlo innovation is another pillar: he invented the equi-energy sampler and random-walk schemes to explore energy landscapes and reconstruct density-of-states in statistical physics and Bayesian computation. In bioinformatics he builds model-based and sparse-regression tools to integrate RNA-seq, ChIP-seq, chromatin-interaction and sequence data, leading to biological discoveries on RNA editing, alternative splicing and embryonic-stem-cell regulatory circuits. Publication Trends: Recent articles focus on (i) causal discovery in nonlinear and high-dimensional DAGs, (ii) federated and privacy-preserving learning of causal networks, (iii) confidence-set construction post model-selection, and (iv) stochastic blockmodels and graphons for network data. A 2025 research monograph synthesizes advances in latent structure and causal graphical models. Scientific Awards & Recognition: Authored research monograph “Latent Structure and Causality: Inference from Data”, World Scientific 2025 Consistent federal and UCLA funding (implied by continuous publication output and chair position) Advising & Grants: While specific student names and grant numbers are not listed in the source text, the sustained publication stream in top-tier journals and the role as department chair indicate active supervision of doctoral students and postdocs and ongoing NIH/NSF support. Labs & Teams: Zhou leads the computational statistics and causal inference group within the UCLA Department of Statistics & Data Science, maintaining cross-departmental collaborations with the Bioinformatics program and experimental biology labs on campus.

Matias Quirozمشاهده پروفایل

مدرس ارشد

• Computational Statistics
• Bayesian Methods
• Machine Learning
• +۴ مورد دیگر

Matias Quiroz is a Senior Lecturer at the University of Technology Sydney (UTS) in the School of Mathematical and Physical Sciences. He joined UTS in July 2023 after previously serving as a Lecturer (2019–2022) and Senior Lecturer (2022–2023) at Stockholm University. His expertise lies in computational statistics, Bayesian methods, and machine learning. He holds a Ph.D. from Stockholm University (2015) and an M.Sc. in Engineering Mathematics from Lund University (2009). Research focuses on developing efficient MCMC algorithms for large datasets and variational approximations for high-dimensional models. He has contributed to areas like subsampling MCMC, spectral analysis of time series, and Bayesian inference for complex models. Quiroz is also an Associate Editor for Computational Statistics and Data Analysis and Econometrics and Statistics . Teaching responsibilities include courses such as Machine Learning: Mathematical Theory and Applications and Programming for Data Analysis. He has supervised students at various academic levels and has held editorial and leadership roles, including serving as the Young Stats representative for the NSW Branch of the Statistical Association of Australia since 2025. His work bridges Bayesian statistics with machine learning, emphasizing scalable computational methods. Notable research themes include high-dimensional state space models, dynamic linear regression, and algorithmic innovations for big data problems.

Yexiang Xueمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Computational Science and Engineering
• +۶ مورد دیگر

Yexiang Xue is an Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He joined Purdue in Fall 2018. His research focuses on integrating machine learning and probabilistic reasoning to enable optimal decision-making in high-dimensional, uncertain environments. Key areas include computational sustainability, materials science, robotics, and medical AI. Education: PhD in Computer Science, Cornell University (2018), advised by Carla Gomes and Bart Selman. B.Sc. in Computer Science, Peking University, China (2011). Research Interests: Xue develops cross-cutting computational methods for scientific discovery, constraint-embedded machine learning, and AI-driven sustainability. His work spans symbolic regression, probabilistic models, and applications in robotics, healthcare, and materials science. Recent efforts include end-to-end physics model discovery and integrating decision diagrams into neural networks. Awards: NSF CAREER Award (2024). IAAI Innovative Application Award (2017) for Phase-Mapper AI platform in materials discovery. Advising & Grants: Mentored multiple undergraduates (e.g., Luming Tang, Runzhe Yang) pursuing graduate studies. His NSF CAREER grant supports AI-driven scientific discovery. Teaching & Service: Taught courses like Statistical Machine Learning (CS 578). Serves on AAAI, UAI, and IJCAI program committees. Media coverage includes NSF News, Science, and MIT Technology Review.

Sitan Chenمشاهده پروفایل

استادیار

• Machine Learning
• Quantum Engineering
• Theory of Computation
• +۷ مورد دیگر

Sitan Chen is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. His research focuses on foundational aspects of machine learning, quantum information, and algorithm design, with a particular emphasis on provable guarantees for generative modeling, deep learning, and quantum learning. He is affiliated with the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative. Education: PhD in EECS from MIT (advised by Ankur Moitra) Bachelor's in Mathematics and Computer Science from Harvard (advised by Salil Vadhan and Leslie Valiant) Grants & Awards: NSF CAREER Award (CCF-2441635) NSF Small (CCF-2430375) Harvard Dean's Competitive Fund for Promising Scholarship Research Interests: Generative models and diffusion processes Quantum tomography and quantum learning Algorithmic foundations for inverse problems Provably efficient learning algorithms Advising & Teaching: Advises PhD/Masters students in quantum computing and machine learning Teaches courses like Quantum Learning Theory and Algorithms for Data Science Key Contributions: First polynomial-time algorithms for learning narrow neural networks Advances in quantum state estimation with minimal resources Provably efficient sampling methods for diffusion models

Minsuk Shinمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Bayesian Methods
• +۳ مورد دیگر

Minsuk Shin is an academic professional with roles spanning industry and academia. He currently serves as a researcher at Gauss Labs, an AI startup focused on semiconductor manufacturing processes. Previously, he held a tenure-track position as an Assistant Professor at the Department of Statistics, University of South Carolina. Before that, he was a postdoctoral fellow at the Harvard Data Science Initiative and Harvard University’s Department of Statistics, mentored by Jun Liu. He earned his PhD in Statistics from Texas A&M University under Valen E. Johnson and Anirban Bhattacharya. Current Role: AI Startup Researcher (Gauss Labs) Previous Faculty: University of South Carolina (Department of Statistics) Postdoctoral Affiliation: Harvard University His research focuses on developing statistical and machine learning models for noisy time-series data and small sample sizes, with expertise in Bayesian methods and high-dimensional data analysis. Key projects include computational strategies for detecting relationships in complex datasets and applications in industrial AI. Shin received a National Science Foundation grant (2020–2023) to support his work on Bayesian and semi-Bayesian methods. His funded research emphasizes efficient algorithms and model selection in high-dimensional spaces. Scientific Award: NSF Grant #2015528 His advising record includes no listed students, but he has contributed to collaborative grants. His industrial work at Gauss Labs applies statistical insights to semiconductor manufacturing challenges.

Hongkai Zhaoمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۴ مورد دیگر

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

• High-speed wireless communications
• Real-time FPGA implementation
• Terahertz communication systems
• +۴ مورد دیگر

Hao Zhang is a Research Fellow at the School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). He is affiliated with the Global Big Data Technologies Centre (GBDTC) at UTS, where he conducts cutting-edge research in high-speed wireless communications and FPGA-based real-time implementation. His work focuses on advancing terahertz and millimeter wave communication systems for next-generation wireless applications. Hao Zhang's educational background includes: Ph.D. in Engineering from the University of Technology Sydney (2019) M.Eng. in Electronics and Communication Engineering from Xidian University, China (2014) B.Eng. in Electronics and Communication Engineering from Xidian University, China (2011) Hao Zhang's research primarily centers on high-speed wireless communications, with a specific focus on real-time implementation using field-programmable gate arrays (FPGAs). His current work explores enhancing algorithm deployment efficiency on FPGAs through advanced LLM-assisted hardware design methodologies, leading to significant improvements in algorithm-hardware co-design and overall system performance. His expertise spans terahertz communication systems, millimeter wave technologies, signal processing, and wireless system implementation. Zhang's research has practical applications in 6G communications, point-to-point links, backhaul networks, and intersatellite communications where atmospheric attenuation is minimal. Analysis of Zhang's recent publications reveals a strong focus on pushing the boundaries of high-speed wireless communication, particularly in the terahertz spectrum. His work consistently demonstrates real-time implementations achieving data rates of 30-50 Gbps, with increasing sophistication in signal processing techniques. The research shows a progression from basic system demonstrations to more advanced implementations incorporating interference suppression, full-duplex capabilities, and nonlinearity mitigation. His publications span both journal articles in prestigious IEEE transactions and conference presentations at major international venues, indicating strong recognition in the wireless communications research community. Hao Zhang has collaborated extensively with researchers across various institutions, particularly within the University of Technology Sydney ecosystem. His work often involves interdisciplinary collaboration between signal processing experts, antenna designers, and hardware implementation specialists. While specific grant information isn't detailed in the provided text, his consistent publication record suggests active funding support for his research activities in high-speed wireless communications. Zhang is part of the Global Big Data Technologies Centre (GBDTC) at UTS, which appears to be a multidisciplinary research center focused on advanced communication technologies. His work within this center involves close collaboration with other researchers working on various aspects of wireless communication systems, from theoretical signal processing to practical hardware implementation. The center likely provides the specialized laboratory facilities necessary for terahertz and millimeter wave research, including advanced signal generators, spectrum analyzers, and FPGA development platforms.

Xiaofeng Shaoمشاهده پروفایل

استاد مدعو

• Statistics
• Data Science
• Time Series Analysis
• +۵ مورد دیگر

Xiaofeng Shao is an Adjunct Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on statistical methodology for time series, spatial data, and high-dimensional data, with applications in econometrics, environmental science, and neuroscience. Shao earned his PhD in Statistics from the University of Chicago in 2006. He has held grants as PI or co-PI from NSF-CMG, NSF-DMS, the Research Board at UIUC, and industry partners like ABInBev and Jump. His work has been recognized with awards such as the Tjalling C. Koopmans Econometric Theory Prize (2009) and the Centennial Scholar designation (2013–2016). His research spans long memory processes, functional data analysis, resampling methods, and applications in atmospheric science, economics, and neuroscience. Notable contributions include self-normalization techniques for high-dimensional time series and spatial bootstrap methods. He has advised numerous students and collaborates across disciplines, maintaining an active publication record in top journals like the Journal of the American Statistical Association and Biometrika.