جست‌وجوی استاد و پژوهشگر

Hiba Nassarمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Computational Mathematics
• Numerical Methods
• +۳ مورد دیگر

Hiba Nassar is an Associate Professor at the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. Her research focuses on functional data analysis, spline-based methods, and numerical algorithms for computational mathematics. Current affiliation: Technical University of Denmark Academic rank: Associate Professor Research domains: Functional data analysis, numerical analysis, statistical modeling Research Interests Dr. Nassar specializes in developing mathematical frameworks for analyzing complex data structures. Key areas include: Orthonormalization of B-splines Functional representation on multivariate domains Band matrix diagonalization techniques Data-driven basis selection in functional analysis Academic Contributions Her recent publications (2022–2025) demonstrate expertise in tensor methods, knot optimization, and sparsity preservation. She actively supervises PhD projects in computational mathematics and applied statistics. Supervision & Projects Dr. Nassar serves as a supervisor in three PhD projects: Federated Learning for Personalized Audiology (2025–2028) Modelling with 3D elastica (2024–2027) Mathematics of Surface Stackability (2024–2027)

John Wrightمشاهده پروفایل

دانشیار

• High-dimensional data analysis
• Signal processing
• Computer vision
• +۵ مورد دیگر

John Wright is an Associate Professor in the Department of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, with additional affiliations in the Department of Applied Physics and Applied Mathematics and the Data Science Institute. He maintains his office at 408 Mudd Hall within the Data Science Institute and leads a research group focused on high-dimensional data analysis. Wright received his PhD in Electrical Engineering from the University of Illinois at Urbana-Champaign in October 2009, followed by a research position at Microsoft Research from 2009-2011 before joining Columbia University. His research focuses on developing algorithms for robustly recovering structured signal representations from incomplete and corrupted observations, with applications in imaging and vision. His research interests span high-dimensional data analysis, signal processing, computer vision, and optimization. Wright's work places particular emphasis on finding methods that perform well even when data is unreliable - noisy or corrupted - and which come with proofs of correctness. His research group develops tools for robustly analyzing high-dimensional data and applying them to practical problems such as image and video compression, face and object recognition. Wright's publication record shows a clear evolution from sparse representation and dictionary learning toward more complex deep learning architectures. His recent work focuses on geometric approaches to nonconvex optimization problems, blind deconvolution methods, and the theoretical foundations of deep networks. His publications consistently bridge theoretical guarantees with practical applications across computer vision and signal processing domains. Scientific Awards and Honors: 2009 Lemelson-Illinois Prize for Innovation for his work on face recognition 2009 UIUC Martin Award for Excellence in Graduate Research 2008-2010 Microsoft Research Fellowship 2012 COLT Best Paper Award (with Wang and Spielman) Wright has successfully mentored numerous PhD students who have gone on to prestigious positions at institutions including TTIC, Google, Rutgers University, and Boston University. He also co-authored the influential textbook 'High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications' published by Cambridge University Press in 2022, which has been used as course material at multiple leading universities. His research group maintains active collaborations across multiple institutions and regularly presents at top conferences including NeurIPS, ICML, CVPR, and COLT. The group holds weekly seminars (typically on Thursdays from 10-12 in 408 Mudd) where current research is discussed and developed.

Eric Priceمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Lower Bounds
• +۶ مورد دیگر

Eric Price is an Associate Professor in the Department of Computer Science at the University of Texas at Austin. He received his undergraduate and graduate degrees from MIT under the advisement of Piotr Indyk, and held postdoctoral positions at the Simons Institute (Berkeley) and IBM Almaden Research Center before joining UT in Fall 2014. Starting Fall 2024, he is on leave at Microsoft AI. Education: MIT (Undergraduate and Graduate), advised by Piotr Indyk Research Interests: Theoretical computer science, focusing on data-limited computational problems Algorithms and lower bounds for sparse recovery, streaming, and compressed sensing Integrating deep learning with traditional signal processing Provably efficient methods for high-dimensional estimation Connections between neural networks and algorithmic analysis Computational limits of modern architectures Article Trends: Recent work spans attention mechanisms, diffusion models, and LoRA fine-tuning Focus on theoretical guarantees for generative models, gradient dynamics, and security analysis Active in improving efficiency and robustness of AI systems Benchmark design for evaluating model limitations Scientific Awards: SODA Best Student Paper (2014) George M. Sprowls Award (2013) Simons Graduate Fellowship (2012) NSF Graduate Research Fellowship (2009) Multiple math/CS competition accolades Advising: PhD advisee Zhao Song (2019)

Karystinos Georgiosمشاهده پروفایل

استاد

• Telecommunications theory and systems
• Coding theory
• Adaptive signal processing
• +۷ مورد دیگر

George N. Karystinos is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete , where he has served since 2005. Since 2021, he also holds the position of Dean of the School. Previously, he was an Assistant Professor at Wright State University (2003–2005). Education: Ph.D. in Electrical Engineering, State University of New York at Buffalo , 2003 Diploma in Computer Engineering and Science, University of Patras , 1997 Additional studies in Piano, National Conservatory of Athens and State University of New York at Buffalo Research Interests: His research spans telecommunications theory and systems , coding theory , adaptive signal processing , wireless communications , MIMO systems , neural networks , and optimization with limited data . He is particularly known for contributions to L1-norm PCA , noncoherent detection , RFID systems , and code design for CDMA . Scientific Awards: IEEE Transactions on Neural Networks Outstanding Paper Award (2003) IEEE ICT Best Paper Award (2001) IEEE ISWCS Best Paper Award (2013) IEEE ICASSP Best Student Paper Award (2015) IEEE RFID-TA Second Best Student Paper Award (2011) IEEE MOCAST Best Student Paper Award (2018) Laboratory and Teaching: He leads research at the Telecommunications Laboratory and teaches courses such as Signals and Systems , Information and Code Theory , and Probability and Random Process Theory . Grants and Projects: While specific grant details are not listed, his extensive publication record and award history indicate active participation in funded research projects, particularly in wireless communications and signal processing.

Badr-Eddine Chérief-Abdellatifمشاهده پروفایل

پژوهشگر

• Mathematical Statistics
• Machine Learning Theory
• Generalized Bayesian Inference
• +۶ مورد دیگر

Badr-Eddine Chérief-Abdellatif is a CNRS Researcher at Sorbonne Université , affiliated with the Laboratoire de Probabilités, Statistique et Modélisation (LPSM) . His research spans mathematical statistics and machine learning theory , focusing on generalized Bayesian inference , robustness , and variational inference . He previously held a postdoctoral position at the University of Oxford and earned his PhD in 2020 from Institut Polytechnique de Paris under Pierre Alquier. His work addresses robust statistical methods using Maximum Mean Discrepancy (MMD) , PAC-Bayesian theory , and kernel techniques . Recent publications explore meta-learning , VAE reconstruction guarantees , and missing data analysis . He received the Best Student Paper Award at AISTATS 2022 and a Best Paper Award at ACML 2019. Chérief-Abdellatif contributes to online learning , copula estimation , and sparse deep learning . His theoretical results on variational inference have been published in top venues like NeurIPS , JASA , and JMLR . He also co-invented a patent for proportions determination in biological ensembles (2024). Scientific Awards : Best Student Paper Award, AISTATS 2022 Best Paper Award, ACML 2019 Travel Award for Academic Conferences

Santanu Deyمشاهده پروفایل

استاد

• Non-convex Optimization
• Mixed Integer Programming
• Operations Research
• +۵ مورد دیگر

Dr. Santanu S. Dey is a Professor and Anderson-Interface Chair at the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. He holds a Ph.D. in Industrial Engineering from Purdue University (2007) and previously worked as a postdoctoral fellow at CORE, Catholic University of Louvain. Research Interests: Non-convex optimization, mixed-integer programming, energy systems optimization, and algorithm development for engineering problems. Leadership: Director of Doctoral Recruiting and Admissions at Georgia Tech ISyE. Served on editorial boards of Mathematical Programming and SIAM Journal on Optimization . Grants: Funded by DOE, NSF, ONR, Argonne National Lab, Sandia National Lab, and ExxonMobil for projects in power systems, decomposition algorithms, and sparse optimization. Scientific Awards include INFORMS Optimization Society Balas Prize (2020), NSF CAREER Award (2012), and IBM Faculty Award (2009). His work has been recognized with professorships and teaching fellowships at Georgia Tech.

Thierry Kleinمشاهده پروفایل

استاد

• Statistics
• Sensitivity analysis
• Machine learning
• +۷ مورد دیگر

Thierry Klein is a Professor of Statistics at École Nationale de l'Aviation Civile (ENAC) and a member of the Toulouse Institute of Mathematics. His research spans statistical methodology with applications in diverse fields including aviation safety, environmental science, and computational mathematics. Dr. Klein's research focuses on advanced statistical methods, particularly in sensitivity analysis, probability theory, and machine learning. His work on Sobol indices has contributed significantly to variance-based sensitivity analysis, while his research on Wasserstein spaces has advanced the understanding of probability distributions in metric spaces. He has developed innovative methods for Gaussian process regression and has applied statistical techniques to problems in aviation safety, coastal flooding prediction, and paleoenvironmental reconstruction. His recent publications demonstrate a strong focus on sensitivity analysis methods, particularly Sobol indices, with applications across multiple domains. He has also made significant contributions to the theory of Wasserstein spaces and their applications in statistical learning. His work bridges theoretical statistics with practical applications in engineering, environmental science, and aviation. Dr. Klein is currently involved in three major funded projects: the PEPR PDE-AI project (2023-2028) on Partial Differential Equations for Artificial Intelligence, the ANR GATSBII project (2025-2029) as project leader, and the ANR MBAP-P project (2025-2029) as a member. These projects reflect his interdisciplinary approach, combining statistical theory with applications in artificial intelligence, engineering, and environmental science.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۵ مورد دیگر

Krzysztof Podgórski is a Professor and Head of the Department of Statistics at Lund University School of Economics and Management (LUSEM). His research spans applied probability, statistics, and interdisciplinary applications in engineering, finance, and environmental sciences. Key Research Areas: Multivariate non-Gaussian stochastic models Statistical analysis of spatio-temporal random fields Distributions at random crossing events Applications: Mechanical engineering (road modeling) Ocean engineering (wave/ship reliability) Financial econometrics (market linkages, risk analysis) Actuarial sciences (non-Gaussian claims) Methodological Contributions: Include ergodic theory of stochastic processes, extreme value theory, and computational statistics. His recent work explores functional data analysis with periodic splines and matrix variate distributions. Collaborative Networks: Extensive partnerships across theoretical and applied disciplines, with projects involving road dynamics, stochastic fields, and uncertainty quantification.

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

Yuchen Zhou is an Assistant Professor in the Department of Statistics at the University of Illinois. His research focuses on high-dimensional statistics, tensor methods, and optimization, with applications in data analysis and machine learning. He has contributed to advancements in areas such as heteroskedastic PCA, low-rank tensor inference, and sparse group lasso regularization. His work emphasizes statistical theory and methodology, addressing challenges in high-dimensional data structures and computational efficiency. Collaborations and research outputs highlight interests in matrix and tensor decomposition, regularization techniques, and non-asymptotic analysis. Zhou's publications span reputable journals like the Annals of Statistics and IEEE Transactions on Information Theory, reflecting his expertise in both theoretical and applied statistical problems. He has no listed academic awards or current advisees.

Joshua Agterbergمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Matrix and Tensor Analysis
• Nonconvex Optimization
• +۴ مورد دیگر

Joshua Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois, College of Liberal Arts & Sciences. His research focuses on high-dimensional statistics, matrix and tensor analysis, nonconvex optimization, and statistical network analysis. He holds a B.S. in Actuarial Science and Mathematics from the University of Wisconsin-Madison, followed by M.S. and Ph.D. degrees in Statistics from Johns Hopkins University. After a postdoctoral fellowship at the University of Pennsylvania, he returned to the Midwest to join UIUC. Education: Bachelor of Science in Actuarial Science and Mathematics, University of Wisconsin-Madison Master of Science in Statistics, Johns Hopkins University Doctor of Philosophy in Statistics, Johns Hopkins University Research Interests: His work bridges theoretical foundations and practical applications, addressing problems in nonconvex matrix sensing, multilayer network analysis, and statistical-computational gaps. He aims to develop methodologies for analyzing complex data structures, particularly in neuroscience, sociology, and engineering. Recent projects include advancing spectral methods, nonconvex optimization, and hypothesis testing frameworks for networks. Teaching Philosophy: Agterberg emphasizes statistical intuition over formulaic approaches, encouraging students to tackle problems from first principles. He teaches courses on theoretical statistics and hopes to develop a high-dimensional statistics curriculum for PhD students. Future Goals: He seeks to create a principled pipeline for network data analysis and to enhance understanding of machine learning algorithms' theoretical underpinnings. Collaborations include work on dynamic networks and statistical methodologies for connectomics.

Marco Stefanucciمشاهده پروفایل

استادیار

• Statistical Methodology
• Functional Data Analysis
• Spectroscopy
• +۳ مورد دیگر

Marco Stefanucci is an Assistant Professor in Statistics at the Department of Economics and Finance, University of Rome Tor Vergata. Previously, he held positions at the University of Rome La Sapienza, University of Trieste, and served as a postdoctoral researcher at the University of Padova. He earned his PhD from the University of Rome La Sapienza under Professor Pierpaolo Brutti. His research focuses on statistical methodology, functional data analysis, and applications in spectroscopy, mortality trends, and neuroscience. Collaborations include work with Professors Mauro Bernardi, Pierpaolo Brutti, and Laura Sangalli, among others. Research interests include advanced statistical techniques for functional data, compositional data analysis, and their applications in diverse fields such as demography, chemistry, and medical imaging. His recent work emphasizes adaptive regression frameworks, sparse modeling, and classification of complex datasets. He has published extensively on topics like mortality trend analysis, spectroscopic data interpretation, and multimodal imaging methodologies. While no awards or grants are explicitly mentioned, his contributions to statistical theory and applied research are evident through his academic trajectory and collaborative projects. Teaching roles include lecturing on Quantitative Methods and Statistical Learning at undergraduate levels.

Yaqing Chenمشاهده پروفایل

استادیار

• Metric Space Valued Data
• Non-Euclidean Data Analysis
• Functional/Longitudinal Data
• +۵ مورد دیگر

Yaqing Chen is an Assistant Professor in the Department of Statistics at Rutgers, The State University of New Jersey. Her primary affiliation is with the School of Arts and Sciences (SAS). She holds a Ph.D. and M.S. from the University of California, Davis, and a B.S. from Peking University. Her research focuses on statistical methodologies for complex data structures, including metric space valued data, non-Euclidean objects, functional/longitudinal data, and distributional data. Applications span longitudinal studies, biological and medical sciences, and social sciences. Key areas of innovation include distance profile-based inference, gradient synchronization in brain connectivity analysis, and Wasserstein regression techniques. Recent work emphasizes neurodevelopmental applications, such as analyzing brain volume trajectories in children and their associations with maternal education. Methodological contributions include R packages like 'fdaconcur' and 'frechet' for functional data analysis and non-Euclidean statistics. Her research bridges theoretical advancements with practical tools for high-dimensional and non-standard data types. Dr. Chen has secured grant funding supporting her work on temporal dynamics of disease spread and neurodevelopmental modeling. She collaborates with interdisciplinary teams in neuroscience and public health. Her lab focuses on developing scalable statistical methods for high-dimensional biomedical data.

Lixin Shenمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +۴ مورد دیگر

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

• High-Dimensional Data Modeling
• Multivariate Time Series
• Sparse Principal Components Analysis
• +۱ مورد دیگر

Dr. Victoria Harris is a researcher at King's College London specializing in modeling high-dimensional data. Her doctoral work focused on modeling multivariate spatio-temporal structure in ecological data and responses to climate change. Current research involves latent variable modeling and latent transition analysis, contributing to UN Sustainable Development Goals.

Justin Rombergمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Optimization
• +۳ مورد دیگر

Justin Romberg is the Schlumberger Professor and Associate Chair for Research in the School of Electrical and Computer Engineering at Georgia Tech, and serves as Associate Director for the Center for Machine Learning. He holds a B.S.E.E., M.S., and Ph.D. from Rice University, followed by postdoctoral work at Caltech and visiting roles at UCLA and the Laboratoire Jacques-Louis Lions. His research bridges signal processing, machine learning, optimization, and applied probability, with notable contributions to imaging inverse problems, data compression, and broadband beamforming. Dr. Romberg has received prestigious awards including the ONR Young Investigator Award (2008), PECASE (2009), Packard Fellowship (2009), and IEEE Fellowship. His recent work emphasizes real-time RF emulation, neural adjoint methods for sound speed learning, and decentralized optimization frameworks. Current projects include radar signal processing, multi-agent reinforcement learning, and hardware-friendly sparse neural networks. His lab focuses on translating theoretical insights into practical systems, such as low-bit beamforming accelerators and medical monitoring devices using MEMS sensors.