Sparse Linear Models Professors and Supervisors | ApplySharif

Find Academic Supervisors

Find and connect with academic supervisors, professors, and scholars for your research journey.

Showing 721-735 of 974 supervisors

Profile data may be stale. It is sourced from public faculty pages.

KashlakView profile

Associate Professor

University of AlbertaCanada

Dr. Kashlak is an Associate Professor in the Department of Mathematical & Statistical Sciences at the University of Alberta. He holds a PhD from the University of Cambridge (2017), MSc from Johns Hopkins University (2011), and BSc from McGill University (2008). His research focuses on nonasymptotic statistics, concentration inequalities, functional data analysis, and probabilistic methods in Banach spaces. He is particularly known for developing permutation and randomization tests, integrating group theory into statistical frameworks, and applying statistical methods to diverse datasets ranging from sleep apnea diagnosis to electoral analysis. **Research Contributions**: His work bridges theoretical statistics and applied problems, with key contributions in covariance operator inference, topological hidden Markov models, and sparse precision matrix estimation. Notable publications include advancements in bootstrap methods for generalized linear models and analytic permutation testing for functional ANOVA. He has collaborated with institutions globally, including the University of Cambridge and Baylor University. **Teaching & Outreach**: He teaches advanced courses like Applied Regression Analysis (Stat 378) and Probability and Measure (Stat 571). His YouTube channels, 'Cache Lack Stats' and 'Cache Lack Math & Stats Lectures', provide accessible explanations of statistical concepts and dataset analyses. He has developed R packages like sparseMatEst for sparse covariance estimation and fdcov for functional data analysis. **Awards & Service**: Kashlak has received an NSERC Discovery Grant ($161,000), SSC New Investigator Award, and multiple grants from the University of Alberta. He organized workshops like the Alberta Math Dialogue (2019) and served as Session Chair at EcoSta 2019 and WNAR 2018. His work has been featured in Significance Magazine and presented at global conferences such as the IMS-Bernoulli Symposium and the RSS Conference. **Lab & Collaborations**: His research group focuses on statistical methodology with applications in healthcare, spatial analysis, and high-dimensional data. He actively mentors students, including Xinyu Zhang, and collaborates with researchers in machine learning and biomedical fields.

Official University Profile

Alexander LitvakView profile

Professor

University of AlbertaCanada

Alexander Litvak is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on convex geometry, random matrix theory, and probability theory with applications to high-dimensional geometry and stochastic processes. Education: He holds a Ph.D. from Tel Aviv University and an M.Sc. from Saint Petersburg State University. Research Interests: Litvak explores geometric probability, random structures, and their applications in optimization and functional analysis. His work often addresses questions related to convex bodies, matrix singularity, and the behavior of high-dimensional random systems. Key topics include minimal dispersion, random polytopes, and spectral properties of random graphs. Publications: His recent work (2020-2024) emphasizes probabilistic methods in convex geometry and random matrix theory, with notable contributions to linear bandits, sparse matrix analysis, and the circular law for digraphs. These studies reveal trends in understanding high-dimensional phenomena and probabilistic structures. Grants and Awards: No specific grants or awards listed in the provided materials. Advising and Labs: No student advisees or lab affiliations explicitly mentioned in the text.

Official University Profile

Yafei WangView profile

Assistant Professor

University of AlbertaCanada

Yafei Wang is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada, since July 2023. Previously, they served as a Lecturer at the University of Essex (2022–2023) and held a postdoctoral research fellowship at the University of Alberta (2019–2022) under Drs. Linglong Kong and Bei Jiang. Their academic journey includes a PhD from the Beijing University of Technology (2019) and a visiting PhD program at the University of Alberta (2017–2018). Education: PhD in Statistics from Beijing University of Technology (2019), supervised by Dr. Zhongzhan Zhang. Visiting PhD Student at the University of Alberta’s Department of Mathematical and Statistical Sciences (2017–2018). Research focuses on complex data analysis, with expertise in robust statistical methods for functional data (e.g., brain imaging applications), stochastic optimization for data-driven decisions, and interpretability in reinforcement learning. Current projects explore large language models in medical imaging while prioritizing fairness and privacy. Key Funding: NSERC-RGPIN Grant (2024–2029): $130,000 (PI) for Robust Statistical Modeling under Uncertainty. NSERC-DGECR Grant (2024–2029): $12,500 (PI). Networking Grant (2024–2025): £18,620 (Co-I with Peng Liu) on AI in Medical Imaging. Teaching Experience: Fall 2024: STAT 537 (Statistical Methods for Applied Research). Winter 2024: STAT 562 (Discrete Data Analysis) and STAT 432/532 (Survival Analysis). Spring 2023: MA331 (Programming and Text Analytics with R).

Official University Profile

Magnus JanssonView profile

Professor

KTH Royal Institute of TechnologySweden

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

Official University Profile

Soham JanaView profile

Assistant Professor

University of Notre DameUnited States

Soham Jana is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, part of the College of Science. He holds a Ph.D. in Statistics and Data Science from Yale University (2022), an M.Stat. and B.Stat. from the Indian Statistical Institute (ISI), Kolkata. His research focuses on high-dimensional statistics, robust estimation, statistical machine learning, causal inference, and empirical Bayes methods. Previously, he was a Post-doctoral Research Associate at Princeton University's Operations Research and Financial Engineering department. Education: Ph.D., Yale University, 2022 (Thesis: Learning non-parametric and high-dimensional distributions via information-theoretic methods) M.Stat. (Hons.), Indian Statistical Institute, 2017 (Dissertation: Characterization of single-integral non-kernel divergences) B.Stat. (Hons.), Indian Statistical Institute, 2015 Research interests include theoretical and methodological aspects of high-dimensional statistics, robust estimation, neural networks, causal inference, and empirical Bayes. He has received awards such as the INSPIRE Scholarship and the Kaneb Center Course Design Academy Award (2024–2025, $5000). Grants and professional contributions include organizing sessions at conferences like JSM and CFE-CMStatistics. His work spans collaborations with institutions like Princeton, Yale, and the First Republic Bank Research Program. He teaches courses such as Introduction to Probability and Modern Machine Learning Techniques at Notre Dame.

Official University Profile

Daren WangView profile

Assistant Professor

University of Notre DameUnited States

Daren Wang is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, within the College of Science. He holds a Ph.D. in Statistics from Carnegie Mellon University (2018), an M.S. in Mathematics from the University of Michigan (2011), and a B.S. in Mathematics from the University of Virginia (2010). His research focuses on high-dimensional statistical methods, including scientific computing, nonparametric estimation, and change point detection in functional and temporal-spatial models. He has developed R packages such as 'changepoints' for change point localization and 'functional_regression' for mixed predictor models. His work bridges theoretical statistics, machine learning, and computational methods. Research interests include high-dimensional linear algebra applications to machine learning, nonparametric estimation in complex systems, and the development of efficient algorithms for change point detection in dynamic networks and time series. He advises Ph.D. students from diverse backgrounds, including applied mathematics and computational physics. His contributions include advancements in density estimation via tensor decomposition and variance-reduced sketching techniques.

Official University Profile

Guo YuView profile

Assistant Professor

University of California , Santa Barbara (UCSB)United States

Guo Yu is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB). His research focuses on high-dimensional statistics, machine learning, and applied probability, with applications to econometrics and healthcare analytics. He holds a PhD from Cornell University. Education: PhD in Statistics, Cornell University Research Interests: Statistical methodologies for high-dimensional data Machine learning algorithms with interpretability Applications in healthcare prediction (e.g., COVID-19 modeling) Econometric models with error control Recent Work Trends: His articles emphasize robust statistical frameworks for high-dimensional inference, including error variance estimation, interaction modeling, and cost-efficient feature selection. He also explores hybrid models combining autoregressive methods with neural networks for time-series prediction. His work often bridges theoretical statistics and practical applications in cloud computing and large-scale data analysis. Awards: None listed. Grants and Advising: No specific grants or advisees listed in the provided texts. His research likely involves collaborations with institutions like Microsoft Azure (as seen in the 2018 Deepview paper).

Official University Profile

Pablo TarazagaView profile

Professor

Texas A&M UniversityUnited States

Pablo Tarazaga is a Professor in the Department of Mechanical Engineering at Texas A&M University and holds the James J. Cain '51 Faculty Fellowship. He earned his PhD (2009) and MS (2004) from Virginia Tech, following a BS from the University of Puerto Rico at Mayagüez (2002). His research encompasses structural dynamics, smart infrastructure, and bio-inspired systems. Primary research areas include: Vibration analysis and modal testing Structural health monitoring and nondestructive evaluation Smart building technologies and IoT applications Energy harvesting from mechanical vibrations Biomedical device development Recent publications focus on wave propagation control, structural health monitoring using electromechanical impedance, and bio-inspired sensory systems. Research trends include advanced wave manipulation techniques for vibration control and particle manipulation, and data-driven modeling approaches for mechanical systems. Significant honors include ASME Fellowship (2022), AFOSR Young Investigator Award (2015), and multiple faculty fellowships at Virginia Tech and Texas A&M. He directs the Fusion of Analysis, Simulation & Testing (FAST) Laboratory, which develops integrated approaches for structural dynamics validation. Educational initiatives include developing curriculum in structural dynamics and mentoring students in smart infrastructure research. Research collaborations span aerospace, civil infrastructure, and biomedical engineering domains.

Official University Profile

Donsub RimView profile

Assistant Professor

Washington University in St. LouisUnited States

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a visiting position at the University of Washington during the 2024-2025 academic year. Previously, he was a visiting assistant professor at Tohoku University's International Research Institute of Disaster Science (IRIDeS) in 2023 and affiliated with Courant Institute and Columbia University. He earned his Ph.D. from the University of Washington under advisors Randall J. LeVeque and Gunther Uhlmann. Education: Donsub Rim obtained his Ph.D. in Mathematics from the University of Washington, advised by Professors Randall J. LeVeque and Gunther Uhlmann. His academic journey also included affiliations with the Courant Institute and Columbia University before joining Washington University. His research focuses on numerical analysis of partial differential equations (PDEs), model reduction, and inverse problems. Specific interests include low-rank neural representations (LRNRs) for real-time PDE solutions, stability analysis of neural networks in geophysical applications like tsunami early warning systems, and computational methods in geophysics, medical imaging, and plasma physics. He has developed the approximate discrete Radon transform (ADRT) with applications in fast inversion and sparse approximations. In recent work, he has explored machine learning techniques for improving tsunami forecasting and risk evaluation, leveraging sparse data and Bayesian methods. His contributions to model reduction and neural network stability have advanced applications in nonlinear hyperbolic PDEs and plasma instabilities. Collaborations include interdisciplinary projects in disaster science, computational geophysics, and scientific software development. Donsub Rim's work has been published in leading journals and conferences, including SIAM Journal on Scientific Computing, Journal of Geophysical Research, and NeurIPS. His research emphasizes bridging computational mathematics with real-world challenges in geophysics and engineering. He maintains an active GitHub repository for numerical software development, emphasizing open-source contributions in computational mathematics and geophysics. His projects include the ADRT Python package and tools for tsunami modeling.

Official University Profile

Todd MichaelView profile

Adjunct Professor

University of California, San DiegoUnited States

Todd Michael is an Adjunct Professor at the University of California, San Diego, affiliated with the Scripps Institution of Oceanography and the Center for Marine Biotechnology and Biomedicine. His work bridges advanced computational methods with structural engineering challenges, particularly in infrastructure monitoring and material degradation analysis. Research interests include Structural Health Monitoring (SHM), Non-Destructive Evaluation (NDE), and the integration of Machine Learning and Bayesian Inference into engineering systems. He focuses on optimizing sensor networks, improving corrosion diagnostics for large structures like miter gates, and developing digital twin frameworks for predictive maintenance. Recent work emphasizes probabilistic modeling, anomaly detection under complex conditions, and the application of deep learning to civil and mechanical engineering problems. His articles highlight innovations in SHM strategies for bridges and aircraft components, sensor reliability under operational failures, and frameworks for cost-effective monitoring. He also explores automated crack detection in underwater environments and the use of physics-constrained neural networks to model structural dynamics accurately. Michael collaborates with interdisciplinary teams on projects involving smart infrastructure and AI-driven inspection technologies. While no formal awards are listed, his contributions reflect a strong focus on applied research with real-world engineering implications.

Official University Profile

Wicher BergsmaView profile

Professor

London School of Economics and Political Science (LSE)United Kingdom

Professor Wicher Bergsma is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical modeling techniques such as I-priors, reproducing kernel methods, and empirical Bayes approaches. Notably, he co-developed the tau-star test for independence and advanced marginal models for categorical data analysis. He has held academic positions at LSE since 2005, preceded by postdoctoral work at Eurandom and Tilburg University, and a stint as an Assistant Professor at the Central European University. His applied work includes collaborations with industry partners like GlaxoSmithKline and Flextronics on clinical trials and fault detection systems. He is currently on sabbatical until Spring 2025. Education includes a Mathematics degree from Leiden University and a PhD in Social Statistics from Tilburg University. His expertise spans theoretical and applied statistics, with contributions to graphical models, conditional independence testing, and Bayesian variable selection. He has authored influential books and R packages, including Marginal Models and the cmm package for categorical marginal modeling. Research interests emphasize bridging theory and practice, particularly in developing robust statistical tools for complex data structures. His recent work explores I-prior methodologies for regression, causal inference with stochastic confounders, and scalable Gaussian process models for environmental data analysis.

Official University Profile

Benjamin PeherstorferView profile

Associate Professor

New York UniversityUnited States

Benjamin Peherstorfer is an Associate Professor at the Courant Institute of Mathematical Sciences, New York University. His research focuses on computational mathematics, machine learning, and scientific computing, with an emphasis on nonlinear model reduction, uncertainty quantification, and multifidelity methods. He holds editorial roles at journals such as Journal of Scientific Computing and SIAM Journal on Scientific Computing . Peherstorfer co-organizes workshops on topics like digital twins and fusion energy, reflecting his commitment to advancing interdisciplinary computational science. He has advised students like Jules Berman, who received the Harold Grad Memorial Prize. His work integrates machine learning with traditional numerical methods to address challenges in high-dimensional systems and real-time simulations. Notable contributions include developing Neural Galerkin schemes for solving evolution equations and advancing multifidelity Monte Carlo methods to accelerate uncertainty propagation. His research bridges theoretical foundations with practical applications, such as wildfire-atmosphere coupling and plasma confinement in stellarators. Key Roles: Secretary of the SIAM Activity Group on Computational Science and Engineering Co-organizer of the Long Program on Multi-Fidelity Methods for Fusion Energy (IPAM, 2026) Main Research Themes: Reduced-order modeling Data-driven operator inference Adaptive sampling algorithms

Official University Profile

Mario MicheliView profile

Senior Lecturer

Johns Hopkins UniversityUnited States

Mario Micheli is a Senior Lecturer in the Department of Applied Mathematics and Statistics at Johns Hopkins University, within the Whiting School of Engineering. His research focuses on mathematical image processing, shape analysis, variational methods, applied statistics, and mathematical 3D printing. He holds a Laurea in Telecommunications Engineering from the University of Padova (1999), an MS in Electrical Engineering from UC Berkeley (2001), and a PhD in Applied Mathematics from Brown University (2008). His work emphasizes modeling deformations in shape analysis and imaging/video data, leveraging differential geometry and variational methods. Notable contributions include centroid-based turbulence correction, matrix-valued kernels for shape deformation, and stochastic system analysis. He has been honored with the 2023 Robert B. Pond, Sr. Excellence in Teaching Award, 2024 Professor Joel Dean Excellence in Teaching Award, and the 2023 Excellence in Teaching, Advising, and Mentoring Award. His publications span turbulence imaging, geometric shape analysis, and stochastic systems, reflecting expertise in interdisciplinary applied mathematics. Micheli’s research bridges theoretical foundations with practical applications in computer vision, data science, and engineering systems.

Official University Profile

Tonatiuh Sanchez-VizuetView profile

Assistant Professor

University of ArizonaUnited States

Tonatiuh Sánchez-Vizuet is an Assistant Professor in the Department of Mathematics at the University of Arizona , where he develops numerical methods for magnetically confined plasma simulations and computational physics problems. Education B.Sc. in Physics, National Autonomous University of Mexico (UNAM) M.Sc. in Mathematics, National Autonomous University of Mexico (UNAM) Ph.D. in Applied Mathematics, University of Delaware Research Interests center on numerical analysis, scientific computing, and computational physics, with specific focus areas including: Hybridizable Discontinuous Galerkin (HDG) methods Boundary Element Method (BEM) for time-domain wave propagation Coupling integral and partial differential equations Uncertainty quantification in plasma physics Transient electromagnetic scattering analysis Thermo-piezoelectric wave interactions Scientific Contributions include 15+ peer-reviewed publications on HDG discretizations, boundary integral formulations, and surrogate modeling. His work spans applications in: Magnetic confinement fusion reactors Wave-structure interaction Transient thermoelasticity Dielectric scattering problems Grants NSF-DMS-2137305 (2021-2023): $250,000 as single PI for HDG methods in plasma confinement Awards Featured by Lathisms.org as prominent Hispanic mathematician (2018) Teaching includes graduate-level real analysis and mathematical statistics courses, with class notes freely available for student use.

Official University Profile

Bethany LuschView profile

Assistant Professor

Argonne National LaboratoryUnited States

Bethany Lusch is an Assistant Computer Scientist in the Data Science Group at the Argonne Leadership Computing Facility, Argonne National Laboratory. She holds advanced degrees in applied mathematics and focuses on integrating artificial intelligence with scientific computing, particularly for dynamical systems and PDE-based simulations. Education: PhD in Applied Mathematics, University of Washington, 2016 MS in Applied Mathematics, University of Washington, 2011 BS in Mathematics (Honors), University of Notre Dame, 2010 Her research centers on scientific machine learning, with emphasis on developing machine-learning emulators for expensive simulations such as climate models and computational fluid dynamics. She pioneers methods that embed domain knowledge into deep learning frameworks, particularly through Koopman operator theory to linearize nonlinear dynamics. Her work spans representation learning, reduced-order modeling, and analysis of supercomputing system logs. The 15 most recent publications reflect a strong trend toward using deep learning—especially recurrent and autoencoder architectures—to model complex physical systems. Key themes include surrogate modeling, latent-space dynamics, closure in reduced-order models, and interpretable AI in scientific contexts. Her work bridges theoretical rigor with practical applications in engineering and geophysics. Scientific Awards: NPSC Fellow, National Physical Science Consortium (now GFSD), Aug 2010 – May 2016 Six-Year Graduate Fellowship, NSF-SUMR Scholarship University of Notre Dame Mathematics Department Scholarship, Aug 2007 – Jun 2010 Bethany Lusch has collaborated extensively with researchers at Argonne and the University of Washington. Her projects often involve interdisciplinary teams working on high-performance computing applications. She has contributed to the development of tools like MELA for visual analytics of HPC logs and has secured competitive fellowships and funding. Her prior role as a Research Associate at the University of Washington (2016–2018) preceded her current position at Argonne (2018–present), indicating a continuous research trajectory. She is actively involved in advancing AI-driven scientific discovery, with ongoing work in universal embeddings for PDEs and multifidelity modeling. Her lab affiliations include the Argonne Leadership Computing Facility, where she leverages world-class supercomputing resources to develop and test her models.

Official University Profile