جست‌وجوی استاد و پژوهشگر

Kyle A Caudleمشاهده پروفایل

استاد

• Forecasting
• Time Series Analysis
• Multilinear Algebra
• +۳ مورد دیگر

Kyle A. Caudle is a Professor of Mathematics at the South Dakota School of Mines and Technology, holding a Ph.D. from George Mason University, an M.S. from Salve Regina University, and a B.A. from Western State College. His research spans forecasting, tensor analysis, and anomaly detection, with applications in engineering and data science. Caudle develops computational tools for time series forecasting, tensor decomposition, and graph representation learning. He created software packages like Flow Field, rTensor2, and LTAR, published on CRAN. His interdisciplinary projects include collaborations with NIST and Naval Surface Warfare Centers on surface ship maintenance and anomaly detection. Publications emphasize multilinear algebra, temporal forecasting, and machine learning. Recent work advances tensor factorization for high-dimensional data, hierarchical graph networks, and deep generative models. Awards include the 2015 Peter Holmes Prize for innovative statistics teaching and accreditation as a Professional Statistician (ASA, 2013). He mentors graduate students and co-developed the Ph.D. in Data Science.

Yuejie Chiمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Signal Processing
• +۴ مورد دیگر

Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with affiliations in the Machine Learning Department and CyLab. She holds a Ph.D. from Princeton University and a B.Eng. (Hon.) from Tsinghua University. Her research focuses on theoretical foundations of data science, machine learning, signal processing, and inverse problems, with applications in sensing, imaging, and AI systems. Her work emphasizes interdisciplinary approaches at the intersection of statistics, optimization, and sensing. Notable achievements include contributions to high-dimensional structured signal processing, for which she received the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award (2019) and the PECASE (2019). She is an IEEE Fellow (2023) and has been recognized as a Goldsmith Lecturer (2021) and Distinguished Lecturer (2022). Research interests include generative AI, reinforcement learning, nonconvex optimization, federated learning, and resource-efficient algorithms. Her group collaborates on projects such as data-driven materials research and agile waveform design for communication networks. She has advised numerous PhD students, many of whom have pursued academic and industry roles in leading institutions. Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024), NSF/ONR/AFOSR young investigator awards, and leadership roles in top conferences like NeurIPS, ICML, and IEEE workshops.

Kai Hebelerمشاهده پروفایل

مدرس

• Many-body theory
• Nuclear astrophysics
• Density functional theory
• +۱ مورد دیگر

Dr. Kai Hebeler is a Lecturer in the Theory Center of the Institute of Nuclear Physics at the Technical University of Darmstadt, Germany. His research focuses on many-body theory, nuclear astrophysics, and density functional theory. He holds a doctoral degree from TU Darmstadt and has conducted postdoctoral and research roles at institutions including ECT* (Trento), CEA (Saclay), and TRIUMF (Vancouver). His work emphasizes nuclear interactions and their applications to extreme astrophysical environments like neutron stars. Research interests include advanced many-body methods, nuclear structure calculations, and the interplay between nuclear theory and astrophysical phenomena. Hebeler’s contributions have advanced the understanding of dense matter properties and neutron star equations of state.

• Numerical Analysis
• Geometric Integration
• Quantum Dynamics
• +۵ مورد دیگر

Prof. Christian Lubich is a Professor in the Mathematical Institute at the University of Tübingen, Germany. His research focuses on numerical analysis, geometric integration, quantum dynamics, and partial differential equations. He has authored influential books such as *Geometric Numerical Integration* (with E. Hairer and G. Wanner) and *From Quantum to Classical Molecular Dynamics*. His work emphasizes stability, accuracy, and computational efficiency in numerical methods for high-dimensional and oscillatory systems. Lubich's contributions span dynamical low-rank approximation, oscillatory Hamiltonian systems, and wave propagation in dispersive media. He has developed novel algorithms for tensor networks and matrix differential equations, addressing challenges in quantum many-body dynamics and stiff evolution problems. His research bridges numerical analysis with applications in physics, chemistry, and engineering. Key areas of exploration include: (1) structure-preserving integrators for long-time simulations, (2) error analysis of finite difference methods in semi-classical regimes, and (3) parallel algorithms for tensor-based computations. His work on pseudospectra and eigenvalue optimization has advanced stability analysis in linear dynamical systems. Lubich collaborates extensively on boundary element methods, finite element algorithms for evolving surfaces, and variational discretization techniques. His publications reflect a commitment to both theoretical rigor and practical computational solutions for complex physical systems.

Weihong Guoمشاهده پروفایل

استاد

• Mathematical Image Analysis
• Inverse Problems
• Scientific Computing
• +۳ مورد دیگر

Professor Weihong Guo holds the position of Professor and Chair in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on Mathematical Image Analysis, Inverse Problems, Scientific Computing, and Computer Vision, with significant contributions to medical imaging and computational mathematics. His work emphasizes developing advanced algorithms for image segmentation, denoising, and reconstruction, often integrating deep learning with traditional variational methods. Notable applications include lung nodule detection, MRI muscle segmentation, and quantum tensor decomposition in biomedical contexts. Guo’s research also extends to graph signal processing, dynamic network analysis, and sparse recovery techniques. He has published extensively in top-tier journals and conferences, demonstrating a strong interdisciplinary approach bridging applied mathematics, computer science, and biomedical engineering.

Jan Draismaمشاهده پروفایل

استاد

• Algebraic Geometry
• Tensor Analysis
• Combinatorics
• +۳ مورد دیگر

Prof. Jan Draisma is a Professor of Mathematics at the University of Bern, serving as Program Director for the MSc in Mathematics and a Member of the Institute Management at the Mathematisches Institut (MAI). His research focuses on algebraic geometry, tensor analysis, and their applications, with particular emphasis on polynomial representations, tensor rank, and geometric invariant theory. He has made significant contributions to understanding asymptotic tensor ranks, topological Noetherianity, and combinatorial algebraic structures. His work bridges pure mathematics with computational methods, often addressing foundational problems in algebra and geometry. Recent research trends include exploring singular loci in tensor varieties, the interplay between algebraic density properties and affine manifolds, and advancing techniques in polynomial functors. His articles highlight interdisciplinary approaches, combining geometric, combinatorial, and computational perspectives. Prof. Draisma’s interests span theoretical advancements and practical applications, such as genome reconstruction from biological data and the development of efficient algorithms for tensor analysis. His contributions reflect a commitment to advancing both fundamental mathematical theory and its real-world relevance.

Tim Kuniskyمشاهده پروفایل

استادیار

• Computational Complexity of Statistical Problems
• Spectral Algorithms
• Random Matrix Theory
• +۴ مورد دیگر

Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.

Eric Chiمشاهده پروفایل

دانشیار

• Statistical Learning
• Numerical Optimization
• Biological Science Applications
• +۳ مورد دیگر

Eric Chi is an Associate Professor in the Department of Statistics at Rice University, also affiliated with the Ken Kennedy Institute. He holds a B.A. in Physics from Rice University, an M.S. in Electrical Engineering from UC Berkeley, and a Ph.D. in Statistics from Rice University. His research focuses on statistical learning and numerical optimization, applied to large-scale data in biological and engineering domains. He has been recognized with awards including the NSF CAREER Award (2018) and the LeRoy and Elva Martin Teaching Excellence Award (2020). Chi's academic journey includes postdoctoral roles at UCLA and Rice, and prior faculty experience at North Carolina State University (2015-2021). He serves as an Associate Editor for the Journal of Computational and Graphical Statistics since 2016 and on the Editorial Board of Statistical Methods in Medical Research since 2011. His work emphasizes algorithmic development for complex data structures, including tensor decomposition, matrix completion, and convex clustering. Recent research trends include robust low-rank tensor decomposition, Bayesian inference via proximal MCMC, and convex-nonconvex regularization strategies. His publications span statistical computing, machine learning, and interdisciplinary applications. Chi advises current PhD students at Rice and has mentored multiple alumni now in academia and industry roles.

Arvind Krishna Saibabaمشاهده پروفایل

دانشیار

• Inverse Problems
• Numerical Linear Algebra
• Medical Imaging
• +۳ مورد دیگر

Arvind Krishna Saibaba is an Associate Professor in the Department of Mathematics at North Carolina State University, within the College of Sciences. He holds a PhD in Computational and Mathematical Engineering from Stanford University (2013). His research focuses on inverse problems, numerical linear algebra, and their applications in medical imaging and geosciences. He is particularly known for developing efficient algorithms for large-scale Bayesian inverse problems and randomized numerical methods. Dr. Saibaba’s work bridges theoretical advancements with practical applications, including parametric kernel approximations, tensor train decompositions, and hybrid projection methods. His recent publications (2023–2025) address cutting-edge topics such as edge-preserving regularization, Monte Carlo diagonal estimation, and non-Gaussian randomized low-rank approximations. He leads research in computational frameworks for dynamic inverse problems and has contributed to geophysical modeling and medical imaging techniques. Grants: Collaborative Research: Randomized Algorithms For Dynamic and Hierarchical Bayesian Inverse Problems RTG: Randomized Numerical Analysis ATD: Collaborative Research: Computationally Efficient Algorithms for Detecting Anomalous Atmospheric Emissions He is affiliated with the Faculty Research Group in Numerical Analysis and Scientific Computing. While no specific awards are listed here, his prolific publication record and grant activity reflect his impactful contributions to computational mathematics.

• Exploration Geophysics
• Artificial Intelligence
• Compressed Sensing
• +۳ مورد دیگر

Jianwei Ma is a Professor at the School of Mathematics, Harbin Institute of Technology, leading the Center of Geophysics and the Institute of Artificial Intelligence. His expertise spans exploration geophysics, artificial intelligence, and compressed sensing. Ma holds a Ph.D. in Solid Mechanics from Tsinghua University (2002) and a BS in Engineering Mechanics from Dalian University of Technology (1998). His research focuses on improving seismic exploration through mathematical methods, including sparse transforms, compressed sensing, and deep learning applications. Notable contributions include methodologies for seismic noise reduction, data interpolation, and full waveform inversion. Ma has held visiting positions at institutions like UCLA, UC Los Angeles, and the University of Texas at Austin. He has received prestigious awards such as the NSFC Distinguished Young Scholar (2016) and World's Top 2% Scientists (2020-2024). Ma actively contributes to professional societies, including SEG and IEEE, and has organized international workshops on mathematical geophysics and AI-driven geoscience. His team includes faculty members and over 30 graduate students, reflecting his commitment to training future researchers in geophysics and AI. Collaborations with global leaders in geophysics and mathematics, such as Prof. Stanley Osher (UCLA) and Prof. Gerlind Plonka (University of Goettingen), highlight his interdisciplinary approach. Key publications include advancements in deep learning for geophysical inversion and sparse representation techniques for seismic data processing.

Joyce A. Chewمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Geometric Deep Learning
• Low-Rank Matrix/Tensor Approximation
• +۴ مورد دیگر

Joyce A. Chew is an Assistant Professor of Mathematics & Statistics at Calvin University. She holds a PhD in Mathematics from UCLA (2025) and dual bachelor’s degrees in Mathematics and Chemistry from Calvin University (2020). Her research focuses on geometric deep learning, low-rank matrix/tensor approximation, and mitigating bias in machine learning. She has contributed to manifold-based data analysis, scattering transforms, and non-negative matrix factorization techniques. Her work bridges mathematics, data science, and applications in single-cell genomics and chemometrics. Chew has received prestigious awards including the NSF Graduate Research Fellowship and the UCLA Raymond Redheffer Prize. She actively mentors undergraduate researchers and participates in initiatives like UCLA’s Collaborative Undergraduate Research Experience. Education : PhD in Mathematics, UCLA, 2025 (Thesis: Topics in Geometric Deep Learning and Learning on Manifolds) C.Phil. & MA in Mathematics, UCLA, 2023 & 2022 B.S. (Honors) Mathematics & B.A. Chemistry, Calvin University, 2020 Research Interests : Her work emphasizes geometric structures in data science, including manifold learning, neural network architectures for non-Euclidean data, and algorithmic fairness. She develops methods for analyzing high-dimensional datasets (e.g., point clouds) using diffusion maps and scattering transforms. Recent projects address bias in word embeddings and robust uncertainty quantification in spectroscopic analysis. Awards : Pacific Journal of Mathematics Dissertation Award (2025) SIAM Student Travel Award (2024) UCLA Raymond Redheffer Prize (2022) NSF Graduate Research Fellowship (2020) Mentoring & Grants : Chew leads UCLA/Los Angeles Pierce College Collaborative Undergraduate Research Experience (2023-2024) and directs the UCLA CAM REU (2021-2022). She has advised projects in geometric deep learning and chemometric modeling. Labs/Teams : Collaborates with groups at UCLA (Deanna Needell’s lab), Stanford (Smita Krishnaswamy), and institutions globally on manifold-based data science initiatives.

Yuxin Chenمشاهده پروفایل

استاد

• Machine Learning
• High-Dimensional Estimation
• Nonconvex Optimization
• +۹ مورد دیگر

Yuxin Chen is a Professor at the Wharton School, University of Pennsylvania , appointed in both the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . He previously served as an Assistant Professor at Princeton University (2017–2021) and a Postdoctoral Scholar at Stanford University (2015–2017). Education: Ph.D., Electrical Engineering, Stanford University, 2015 M.S., Statistics, Stanford University, 2013 M.S., Electrical and Computer Engineering, UT Austin, 2010 B.S., Microelectronics, Tsinghua University, 2008 His research spans high-dimensional estimation , machine learning theory , nonconvex optimization , and statistical signal processing , with applications in medical imaging and computational biology . Recent work focuses on diffusion models , large language models (LLMs) , and reinforcement learning (RL) . His 15 most recent publications emphasize diffusion models (generalization, sampling efficiency, scientific applications), reinforcement learning (sample complexity, convergence theory), and nonconvex optimization (implicit regularization, statistical guarantees). Scientific Awards: Alfred P. Sloan Research Fellowship, 2022 Princeton SEAS Junior Faculty Award, 2021 Princeton Graduate Mentoring Award, 2020 ICCM Best Paper Award (Gold Medal), 2020 ARO Young Investigator Program (YIP) Award, 2020 AFOSR YIP Award, 2019 SIAM Activity Group on Imaging Science Best Paper Prize IEEE Transactions on Power Electronics Prize Paper Award (first place) He mentors PhD students and leads interdisciplinary research projects funded by agencies like the Army Research Office and Air Force Office of Scientific Research . His team develops frameworks such as LOCO Edit for controllable diffusion model editing and MagNet for power magnetics modeling .

Ivan Markovskyمشاهده پروفایل

دانشیار

• Structured Low-Rank Approximation
• System Identification
• Data-Driven Control
• +۷ مورد دیگر

Ivan Markovsky is an Associate Professor at the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel. He previously held positions at the School of Electronics and Computer Science (ECS) at the University of Southampton. His research focuses on structured low-rank approximation, system identification, and data-driven control, with applications in signal processing and control systems. Education: PhD in Electrical Engineering from Katholieke Universiteit Leuven (2005). Research Interests: Specializes in low-rank approximation methods, system identification, and their applications in control theory and signal processing. Develops numerical optimization algorithms for structured matrices and tensors, with emphasis on practical engineering problems. His work bridges theoretical mathematics and applied control systems engineering. Awards: Received an ERC Starting Grant (2011) for research on structured low-rank approximation. Grants & Projects: ERC Starting Grant (2011-2016) - Structured Low-Rank Approximation FWOAL827 (2017-2020) - Block-oriented nonlinear identification using Volterra series FWOAL758 (2015-2018) - Decoupling multivariate polynomials in nonlinear system identification Labs/Teams: Leads research groups focused on system identification, matrix approximation, and data-driven control methodologies. Collaborates internationally on projects involving tensor approximation and nonlinear system modeling.

Changxiao Caiمشاهده پروفایل

استادیار

• Mathematical Data Science
• Diffusion Models
• Reinforcement Learning
• +۲ مورد دیگر

Changxiao Cai is an Assistant Professor at the University of Michigan in the Department of Industrial and Operations Engineering and an affiliate faculty member in Electrical and Computer Engineering. He holds a Ph.D. in Electrical Engineering from Princeton University (2021) and a B.E. in Electronic Engineering from Tsinghua University (2016). Previously, he was a postdoctoral researcher at the University of Pennsylvania under T. Tony Cai and Hongzhe Li. His research focuses on mathematical data science , with recent work emphasizing diffusion models , reinforcement learning , high-dimensional statistics , and nonconvex optimization . His publications span theoretical analysis of diffusion models, convergence guarantees in reinforcement learning, and statistical methods for tensor completion, demonstrating interdisciplinary applications in machine learning, optimization, and information theory. Contact: cxcai@umich.edu

Vijay Bhattiproluمشاهده پروفایل

استادیار

• Approximation Algorithms
• Hardness of Approximation
• Polynomial Maximization over Convex Sets
• +۴ مورد دیگر

Vijay Bhattiprolu serves as an Assistant Professor in the Department of Combinatorics & Optimization at the University of Waterloo, where his research bridges theoretical computer science and advanced mathematical disciplines. His work focuses on fundamental questions in computational complexity and optimization theory, with significant implications for algorithm design and analysis. Dr. Bhattiprolu's academic journey includes a Ph.D. from Carnegie Mellon University (2019) under Venkat Guruswami, a postdoctoral fellowship at Princeton University and the Institute for Advanced Study (2019-2022) as part of the Simons Collaboration on Algorithms and Geometry, and undergraduate studies at the University of Illinois at Urbana-Champaign (2014) with research mentorship from Sariel Har-Peled and Mahesh Viswanathan. Ph.D. in Computer Science, Carnegie Mellon University (2014-2019) Postdoc, Princeton University / Institute for Advanced Study (2019-2022) B.Sc., University of Illinois at Urbana-Champaign (2011-2014) His research program centers on approximation algorithms, hardness of approximation, and their deep connections to functional analysis and convex geometry. Key themes include polynomial maximization over convex sets, spectral theory, asymptotic convex geometry, and sum of squares methods, with applications to fundamental optimization problems. This interdisciplinary approach leverages mathematical tools to establish tight bounds on computational efficiency and develop novel algorithmic frameworks. Dr. Bhattiprolu's publication record reveals consistent contributions to theoretical foundations of optimization, particularly in matrix norm approximation, quadratic form maximization, and geometric methods in algorithm design. His work frequently appears in premier venues including STOC, FOCS, and SICOMP, demonstrating sustained impact on understanding computational limits and advancing approximation techniques. He currently mentors three graduate students: Yang Xiao (Ph.D., co-advised with Chaitanya Swamy), Jacob Skitsko (Ph.D., co-advised with Kostya Pashkovich), and Martin Liu (Masters). His teaching portfolio includes advanced courses in approximation algorithms (CO759) and core optimization theory (CO250, CO370), reflecting his commitment to training the next generation of theoretical computer scientists.