جست‌وجوی استاد و پژوهشگر

Anindya Bhadraمشاهده پروفایل

استاد

• Bayesian methods for high-dimensional and complex data
• Computational statistics
• Applications of statistics in life sciences
• +۹ مورد دیگر

Anindya Bhadra is a Professor in the Department of Statistics at Purdue University and a University Faculty Scholar. His research focuses on Bayesian methods for high-dimensional and complex data, computational statistics, and applications in life sciences including genomics, infectious disease epidemiology, and nutrition. Research Interests: Bayesian inference for graphical models and high-dimensional data Gaussian processes with novel covariance functions (e.g., Confluent Hypergeometric) Deep learning and statistical learning intersections Measurement error models and zero-inflated data analysis Spatiotemporal modeling for geostatistics Scientific Contributions: Developed horseshoe+ estimator for ultra-sparse signal recovery Designed confluent hypergeometric covariance functions for spatial modeling Advanced posterior inference in infinite-width neural networks with heavy-tailed weights Created evidence estimation techniques for Gaussian graphical models Scientific Awards: University Faculty Scholar, Purdue University IISA 2022 Best Poster Award (for student K. Sagar) ENAR Distinguished Student Paper Award 2022 (for student K. Sagar) Editorial Roles: Associate Editor, Journal of the American Statistical Association (2020–2023) Associate Editor, Journal of Computational and Graphical Statistics (2021–present) Associate Editor, Statistical Analysis and Data Mining (2021–present) Associate Editor, Sankhya A (2018–present) Email: bhadra@purdue.edu

Cynthia Rushمشاهده پروفایل

دانشیار

• Mathematical Statistics
• High-Dimensional Inference
• Approximate Message Passing
• +۷ مورد دیگر

Cynthia Rush is an Associate Professor in the Department of Statistics at Columbia University, where she conducts research at the intersection of mathematical statistics, information theory, and high-dimensional data analysis. She is affiliated with the Graduate School of Arts and Sciences and is based in the School of Social Work building in New York City. Ph.D. in Statistics, Yale University, 2016 B.S. in Mathematics, University of North Carolina at Chapel Hill Her research focuses on the development and analysis of algorithms for high-dimensional inference, particularly using Approximate Message Passing (AMP), with applications in sparse regression, signal processing, and machine learning. She integrates tools from information theory, statistical physics, and applied probability to understand fundamental limits and performance of modern statistical methods. Her recent publications span topics such as SLOPE regularization, community detection, power posteriors, and massive multiple access in communications. These works appear in top journals including IEEE Transactions on Information Theory, Annals of Statistics, and NeurIPS. Her research shows a consistent trend toward rigorous theoretical analysis of high-dimensional statistical algorithms and their practical implications. Notable scientific recognition includes being a finalist for the JKW Student Paper Award at IEEE ISIT in 2016. She has delivered numerous invited talks at leading institutions and conferences worldwide, reflecting her active role in the theoretical data science community. JKW Student Paper Award Finalist, IEEE ISIT 2016 She advises and collaborates with researchers in statistics, machine learning, and information theory, though formal doctoral advisees are not listed. Her work is supported by theoretical grants and collaborations across engineering and mathematical sciences. She is involved in the broader academic community through seminars, workshops, and program committees, contributing to the advancement of rigorous statistical learning. Cynthia Rush leads a research group focused on high-dimensional inference and algorithmic analysis, often collaborating with researchers at institutions like Yale, Princeton, and international centers in Europe and Asia. Her lab emphasizes theoretical foundations with practical relevance in data science and communications.

• Applied Harmonic Analysis
• Signal Processing
• Compressed Sensing
• +۷ مورد دیگر

Xuemei Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of North Carolina Wilmington (UNCW), where she has been a faculty member since 2020, promoted to Associate Professor in 2023. She previously served as an Assistant Professor at New Mexico State University (2018–2020) and the University of San Francisco (2016–2018), following postdoctoral positions at the University of Missouri Columbia and the University of Maryland College Park. Ph.D. in Mathematics, Vanderbilt University Research focus: Applied harmonic analysis, compressed sensing, frame theory, and high-dimensional data solvers Active Principal Investigator on NSF grants DMS-2307827 and DMS-1908880 Mentors undergraduate and graduate students in signal and image processing projects Her research lies at the intersection of mathematical signal processing and data science, with a strong emphasis on theoretical foundations of sparse representations, frame design, and iterative algorithms like the Kaczmarz method. She investigates atomic norm minimization, matrix separation, and structured signal recovery, contributing to both theoretical understanding and practical applications in image inpainting and tensor recovery. Her work frequently appears in top journals such as SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis . The 15 most recent articles reflect a consistent trajectory in applied harmonic analysis and compressed sensing, with increasing focus on structured recovery, frame optimization, and algorithmic convergence. Keywords span Mathematics, Signal Processing, Numerical Analysis, Optimization, Image Processing, and Data Science , with subfields including Frame Theory, Sparse Recovery, Kaczmarz Algorithms, Atomic Norm Minimization, Low Coherence Frames, and Tensor Recovery . The trend shows deepening theoretical analysis paired with algorithmic innovation for high-dimensional problems. NSF DMS-2307827: Recovering structured signals: atoms, matrix separation, and applications (PI, 2023–2026) NSF DMS-1908880: Recovering Signals Sparse in a Frame: Theory and Applications (PI, 2019–2023) Xuemei Chen actively advises students, including undergraduates engaged in research on sparse signal recovery and image processing. She supports students through research assistantships and hourly positions, emphasizing strong analytical and coding skills. Her teaching portfolio includes courses in statistics, regression, numerical analysis, linear algebra, and data science, reflecting her interdisciplinary expertise. She maintains open educational resources on GitHub and encourages student engagement through research and professional development opportunities. She leads a research group focused on mathematical data science, with projects suitable for master’s and advanced undergraduate students. Her GitHub repositories, such as Undergraduates and Image-Processing , support student training and open collaboration. She fosters a research environment that bridges pure mathematics with real-world applications in data and image analysis.

• Computer Engineering
• Mathematical Engineering
• Graph Algorithms
• +۵ مورد دیگر

Alexandru-Cristian Mara is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . His research focuses on network representation learning, graph embeddings, and machine learning methodologies. Research Interests : Network embedding evaluation, link prediction, graph robustness, and reproducibility in machine learning. Methodological Contributions : Developed EvalNE , a framework for evaluating network embeddings on link prediction tasks. Key Findings : Demonstrated limited progress in network embedding methods compared to simple heuristics and analyzed robustness against network perturbations.

Xiao Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Adversarial Machine Learning
• Computer Security
• +۴ مورد دیگر

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He obtained his PhD in Computer Science from the University of Virginia in 2022, advised by Prof. David Evans, and holds an MS in Statistics from UVA and a BS in Mathematics from Tsinghua University. His research focuses on trustworthy machine learning, particularly adversarial robustness, security, and privacy-preserving techniques. Zhang's research interests span: Adversarial machine learning and intrinsic robustness limitations Trustworthy AI systems for security-critical applications Statistical machine learning and optimization methods Privacy-preserving techniques and data poisoning defenses Robust representation learning and generative models His recent publications (2022-2025) demonstrate a strong focus on: Developing new frameworks for adversarial robustness measurement Creating defenses against data poisoning and membership inference attacks Exploring fundamental limits of robust learning Applying diffusion models and generative approaches to security Advancing anti-facial recognition techniques Zhang leads a research group at CISPA investigating robust machine learning systems and has presented work at top venues including NeurIPS, ICLR, ICML, and CCS. While no awards are mentioned in the source material, his research addresses critical challenges in AI security.

• Telecommunications
• Communication Systems
• Signal Processing
• +۳ مورد دیگر

Huiping Huang is a researcher at Chalmers University of Technology's Department of Electrical Engineering. Their work focuses on telecommunications and signal processing, particularly in the areas of 5G positioning, massive MIMO systems, and dual-function radar-communication integration. Huang's research explores hybrid beamforming techniques under hardware constraints like limited-resolution phase shifters. Huang is involved in multiple funded projects: Beyond 5G Positioning (FFI), Hardware-aware Integrated Localization and Sensing (Swedish Research Council), and Near-field Enhanced Accuracy Tracking for 6G Networks (European Commission). Current collaborations span institutions including Lund University, University of Luxembourg, and Shanghai Jiao Tong University. Research Contributions: Developing algorithms for sidelobe suppression in MIMO-OFDM systems Optimizing transmit patterns for dual-function radar-communication systems Advancing distorted sensor detection methods in array signal processing Exploring terahertz communication systems with reconfigurable intelligent surfaces Designing sparse arrays for radar-communication coexistence Technical Expertise: Massive antenna arrays, hybrid analog-digital beamforming, direction-of-arrival estimation, waveform optimization, and sensor calibration under hardware limitations.

• Robotics
• Prosthetics Engineering
• Control Systems
• +۴ مورد دیگر

Donald Ebeigbe is an Assistant Professor in the Department of Electrical Engineering at Cleveland State University. His research spans interdisciplinary domains including robotics, prosthetics, infectious disease dynamics, and control systems. He has made significant contributions to nonlinear filtering, robust control, and mathematical modeling of epidemics with behavioral feedback. Academic Rank: Assistant Professor Department: Electrical Engineering University: Cleveland State University His work integrates robotics with biomedical applications , focusing on adaptive control techniques for prosthetic limbs and drones. In epidemiology, he investigates parameter identifiability and equilibrium stability in disease models. Recent publications highlight his expertise in Kalman filtering and non-Gaussian forecasting . Research trends include: Generalized unscented transformation for non-Gaussian processes (2025) Behavioral feedback in infectious disease dynamics (2025) Regressor-free control algorithms (2024) Manifold-based identifiability analysis in epidemics (2022) Key collaborations include research with scholars at institutions like IEEE and SIAM journals, with applications in both biomedical engineering and public health.

• Nonsmooth Optimization
• Variational Analysis
• Mathematical Programming
• +۱ مورد دیگر

Nghia T. A. Tran is an Associate Professor in the Department of Mathematics and Statistics at Oakland University, Rochester, MI, USA. His research focuses on mathematical optimization, variational analysis, and inverse problems, with applications spanning signal processing, statistics, and numerical algorithms. He serves as an Associate Editor for the journal Set-Valued and Variational Analysis . Research Interests: Dr. Tran specializes in: Nonsmooth optimization methods and variational inequalities Stability analysis of convex and non-convex optimization problems Regularization techniques for linear inverse problems Convergence properties of iterative algorithms Publication Trends: His 15 most recent publications (2016–2025) demonstrate a consistent focus on: Geometric characterizations of optimization stability (Lipschitz/tilt properties) Solution uniqueness in regularization frameworks (e.g., Lasso, nuclear norm) Convergence analysis of splitting methods and proximal algorithms Applications to matrix completion, inverse problems, and statistical learning Works predominantly appear in premier journals including SIAM Journal on Optimization, Mathematical Programming, and Journal of Optimization Theory and Applications.

Isabel Valeraمشاهده پروفایل

استاد

• Machine Learning
• Robust Machine Learning
• Interpretable AI
• +۱۰ مورد دیگر

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)

Peter Manoharمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +۴ مورد دیگر

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

• Physiology
• Exercise Physiology
• Musculoskeletal Rehabilitation
• +۴ مورد دیگر

Oktay Kuru is an Associate Professor in the Department of Physiotherapy and Rehabilitation at Muğla Sıtkı Koçman University's Faculty of Health Sciences. With a PhD in Physiology from Akdeniz University (2006), he has established a robust research career focusing on exercise physiology, renal function, and oxidative stress mechanisms. Dr. Kuru's research interests span Physiology , Exercise Physiology , Musculoskeletal Rehabilitation , Renal Physiology , Oxidative Stress , Blood Pressure Regulation , and Proteinuria . His work primarily investigates how exercise affects renal function, vascular tone, and oxidative stress pathways, with particular attention to differences between sedentary and trained individuals. His research employs both human and animal models to explore fundamental physiological mechanisms. His publication record shows consistent productivity since 2000, with recent work expanding into molecular genetics (vitiligo research) and medical education. The articles demonstrate a clear trajectory from basic exercise physiology research toward more complex molecular investigations while maintaining his core focus on exercise-induced physiological responses. His work appears in reputable journals across physiology, nephrology, and sports medicine. Dr. Kuru has served as principal investigator for research projects including 'SIRT1 Gene Polymorphisms in Vitiligo Disease' (2013-2015) and has been an active member of the Clinical Research Ethics Committee since 2019. He has also contributed as an editor for the Karya Journal of Health Science from 2020-2023. In teaching, Dr. Kuru delivers core physiology courses across multiple programs including Physiology I, Physiology II, Exercise Physiology, and supervises graduation projects. His 2022 research on physiology course success levels in face-to-face versus remote education demonstrates his commitment to pedagogical excellence.

Enrico Au-Yeungمشاهده پروفایل

دانشیار

• Harmonic Analysis
• Mathematical Signal Processing
• Functional Analysis
• +۲ مورد دیگر

Enrico Au-Yeung serves as a full-time Associate Professor in the Department of Mathematical Sciences at DePaul University's College of Science and Health, where he has been a faculty member since 2014 after completing a postdoctoral fellowship at the Pacific Institute for the Mathematical Sciences. His academic background includes: PhD in Mathematics from the University of Maryland at College Park (2011) Postdoctoral Fellowship at the Pacific Institute for the Mathematical Sciences Dr. Au-Yeung's research focuses on pure and applied harmonic analysis with significant contributions to mathematical signal processing . His work bridges theoretical frameworks in functional analysis , random matrix theory , and Banach space geometry to solve practical problems in data representation and recovery. This interdisciplinary approach has implications for modern data science, communication systems, and machine learning algorithms. Analysis of his 2014-2017 publications reveals a cohesive research trajectory centered on frame theory and signal recovery. Key innovations include developing random matrix frameworks for sparse signal reconstruction, creating erasure-robust frame constructions, and establishing theoretical connections between balayage techniques and pseudo-differential operators. His collaborations with John Benedetto and Somantika Datta demonstrate strong interdisciplinary engagement. No scientific awards are documented in the provided information. Details regarding graduate student advising and research funding mechanisms are not specified in the current text. Dr. Au-Yeung maintains active research collaborations with mathematicians including John Benedetto, contributing to the theoretical foundations of harmonic analysis through joint publications on Fourier frames and operator theory.

Guy Breslerمشاهده پروفایل

دانشیار

• Information Theory
• Statistical Inference
• Computational Complexity
• +۲ مورد دیگر

Guy Bresler is Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, with affiliations in LIDS, Theory of Computation, Center for Statistics, FODSI, and IDSS. He researches the interface of information theory, statistics, theoretical computer science, and probability, developing mathematical models to understand computational tractability of high-dimensional inference. His research characterizes relationships between combinatorial structure and computational tractability in inference problems, with applications in statistical physics, random graphs, and learning theory. Recent work establishes fundamental limits in statistical estimation and develops efficient algorithms for high-dimensional problems. Publications demonstrate consistent focus on computational-statistical tradeoffs across random graphs, Markov chains, neural networks, and graphical models. Techniques involve information-theoretic analysis, reduction methods, and phase transition characterization. He advises doctoral students Kiril Bangachev, Alina Harbuzova, Chenghao Guo, and Brice Huang. Previously advised researchers include Enric Boix-Adserà, Dheeraj Nagaraj, and Mina Karzand. He co-organized the Simons Institute program on computational aspects of high-dimensional inference.

R. Raviمشاهده پروفایل

استاد

• Operations Research
• Computer Science
• Combinatorial Optimization
• +۴ مورد دیگر

R. Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University's Tepper School of Business. He holds a B.Tech from IIT Madras and a Ph.D. from Brown University. His research focuses on combinatorial optimization, network design, and their applications in business and technology, particularly in supply chains, logistics, and online advertising. Ravi has held prestigious roles including Associate Dean for Intellectual Strategy, Chair of the Future Educational Delivery Committee, and Director of Analytics Strategy. He is an INFORMS Fellow (2017) and recipient of the 30-year Test of Time Award (2023) for his 1989 STOC paper. His work is supported by NSF, ONR, and industry grants. Ravi has advised over 25 Ph.D. students and contributed to numerous editorial and conference leadership roles. Education: Bachelor's: Computer Science, IIT Madras (1989) Master's and Ph.D.: Computer Science, Brown University (1991, 1993) Postdoctoral Fellowships: Princeton University and UC Davis (1993-1995) Research Interests: Optimization models for business-technology intersections, including supply chains, logistics, and online advertising. His methods blend theory with practical applications, leveraging funding from NSF, Google, and others. Awards & Honors: INFORMS Fellow (2017) NSF Career Award ACM STOC 30-Year Test of Time Award (2023) Current Roles: Director of Analytics Strategy at Tepper School, Amazon Scholar (since 2021), and adjunct faculty at CMU's Computer Science Department.

• Transportation
• Logistics
• Supply Chains
• +۶ مورد دیگر

Assoc Prof Kok Choon Tan is an Associate Professor in the Department of Analytics and Operations at the NUS Business School. He holds a Deputy Director role at the Centre for Maritime Studies (CMS), Singapore. His research focuses on container terminal operations optimization, maritime logistics, and simulation-based decision-making. He has been with NUS since 1984, starting as a Senior Tutor in Mathematics, and later earning a Post-Graduate Diploma in Teaching in Higher Education (PG DipTHE) to enhance his pedagogical skills. Research Interests: Transportation and logistics optimization, applied mathematics, operations research modeling, and maritime systems. He leads the Port Operations Modelling & Analysis track at CMS, focusing on container terminal efficiency through mathematical optimization and simulation. Teaching Philosophy: Emphasizes student-centered learning, active learning strategies (e.g., collaborative learning, case studies), and bridging theory to real-world applications. His teaching integrates problem-solving, critical thinking, and presentation skills development through group projects and industrial case studies. Recent Research Trends: His articles address AGV scheduling, container relocation under uncertainty, and multi-modal port operations. These contributions highlight innovations in automated systems, data-driven decision-making, and sustainability in port logistics. Labs/Teams: Active in the NUS Centre for Maritime Studies and collaborates with departments in Industrial Systems Engineering and Civil & Environmental Engineering. His work supports Singapore’s maritime and logistics industry through applied research and strategic planning.