Gaussian Process Regression Professors and Supervisors | ApplySharif

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Giulia De PasqualeView profile

Assistant Professor

University of PaduaItaly

Giulia De Pasquale is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, specializing in Control Systems. Her research focuses on social network interventions, optimization algorithms, and fairness in AI systems. She leads the Control Systems Group, addressing challenges in distributed control, hypergraph reconstruction, and ethical algorithm design. Her work integrates machine learning, network science, and control theory to develop fair and safe recommendation systems. Recent projects include hypergradient-based optimization for social network interventions and optimal transport approaches to fairness in influence maximization. She actively collaborates with institutions like Santa Fe Institute and UC San Diego. Giulia organizes interdisciplinary workshops on algorithmic fairness and has presented at NeurIPS, CDC, and NetSci. She advises PhD students like Alessandro Casu and hosts visiting scholars such as Camilla Quaresmini. Her research has been published in prestigious venues including Nature Communications and ICML.

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Hans-Georg MüllerView profile

Professor

University of California, DavisUnited States

Hans-Georg Müller is a Distinguished Professor of Statistics at the University of California, Davis. His work focuses on functional data analysis, Fréchet analysis of random objects, and applications in longitudinal studies, brain imaging, and aging research. He has contributed to foundational methodologies such as Fréchet regression and Wasserstein-based statistical models. Key research interests include the statistical analysis of non-Euclidean data, covariance structures in high-dimensional spaces, and temporal dynamics of complex systems. Müller has published extensively in top-tier journals like the Annals of Statistics and IEEE Transactions on Information Theory. Awards: 2022 Rietz Lecture & Award (IMS), 2017 Humboldt Research Award, and multiple fellowships from prestigious societies. Advising: Recognized with the 2020 Graduate Program Advising & Mentoring Award at UC Davis. Publications: Over 200 papers, including influential works on Fréchet regression, Cox process regression, and longitudinal brain development studies. His research bridges theoretical statistics with applied domains, particularly in understanding neurodevelopmental processes and the statistical mechanics of aging biomarkers.

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Debashis PaulView profile

Professor

University of California, DavisUnited States

Debashis Paul is a Professor in the Department of Statistics at the University of California, Davis. His research focuses on high-dimensional statistics, random matrix theory, functional data analysis, and their applications in neuroimaging and spatial statistics. He has contributed to methodologies for spectral analysis, covariance modeling, and nonparametric estimation in complex datasets. His work spans theoretical developments in multivariate analysis and practical applications in fields such as medical imaging and genomics. Recent projects include modeling fiber orientation in diffusion MRI, analyzing high-dimensional genomic data, and studying pandemic dynamics through statistical frameworks. Key research themes include: High-dimensional time series analysis Random matrix theory applications Functional data smoothing techniques Non-Gaussian spatial field modeling Covariance structure inference His recent publications highlight advancements in spectral estimation, latent graph inference, and meta-learning frameworks in high-dimensional settings. He has also contributed to methodological improvements in GWAS analysis and nonautonomous dynamical systems modeling.

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Kailai LiView profile

Assistant Professor

University of GroningenNetherlands

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

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Kaizheng WangView profile

Assistant Professor

Columbia UniversityUnited States

Kaizheng Wang is an Assistant Professor at Columbia University, specializing in the intersection of Machine Learning , Optimization , and Statistics . He develops scalable algorithms for analyzing unstructured, incomplete, and heterogeneous data with applications in revenue management, signal processing, and distributed computing. Education: PhD in Operations Research and Financial Engineering from Princeton University (2020), BS in Mathematics from Peking University (2015). Wang's research focuses on data integration for learning and decision-making, addressing challenges in data privacy, distributed storage, and uncertain relevance. His work leverages optimization and statistical tools to create principled approaches for high-quality solutions. Recent publications emphasize machine learning under non-stationarity , adaptive transfer clustering , and robust statistical estimation . Key themes include handling heterogeneous data, uncertainty quantification, and algorithm design for large-scale problems.

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David Garcia CavaView profile

Senior Lecturer

University of EdinburghUnited Kingdom

Dr. David Garcia Cava is a Senior Lecturer in Mechanical Engineering at the University of Edinburgh's School of Engineering. His research specializes in structural dynamics, engineering vibrations, and structural health monitoring (SHM) systems. He leads research developing vibration-based monitoring techniques for engineering structures including wind turbines, aerospace components, and composite materials. His work focuses on: Developing adaptive methodologies for structural health monitoring Damage detection under environmental variability Experimental characterization of dynamic responses Machine learning applications in structural diagnostics Recent publications demonstrate advancements in mitigating environmental variability in SHM, fatigue testing of renewable energy structures, physics-informed machine learning methods, and vibration analysis of composite materials. His research has applications across wind energy, tidal power, aerospace, and mechanical systems. Dr. Garcia Cava teaches courses in Structural Mechanics and Dynamics, Advanced Dynamics, and Research Methods. He is actively recruiting PhD students for projects on structural health monitoring and vibration analysis.

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Kengo KatoView profile

Professor

Cornell UniversityUnited States

Kengo Kato is a Professor in the Department of Statistics and Data Science at Cornell University , affiliated with the College of Arts and Sciences. Previously, he held a position at the Faculty of Economics at The University of Tokyo and served as a visiting scholar at MIT’s Department of Economics. His research focuses on Mathematical Statistics, Applied Probability, and Econometrics , with an emphasis on high-dimensional statistical models and optimal transport theory. His work spans topics such as statistical inference in optimal transport, Gromov-Wasserstein distances, bootstrap methods, and high-dimensional data analysis. Notably, he is Editor-in-Chief of the journal Bernoulli (2025-2027) and serves as an associate editor for Japanese Economic Review and Journal of Statistical Planning and Inference . His research bridges theoretical advancements with practical applications in econometrics and machine learning. Recent publications emphasize foundational results in optimal transport theory, including limit laws for Gromov-Wasserstein alignment, stability of entropic maps, and statistical guarantees for sliced Wasserstein distances. His work on bootstrap techniques addresses challenges in high-dimensional and spatial data analysis, offering robust inference methods for modern datasets. While no formal advisees are listed, his contributions to statistical theory and methodology suggest significant mentorship in graduate training programs. His research is supported by Cornell’s interdisciplinary environment and collaborations within affiliated institutes.

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Haruko Murakami WainwrightView profile

Assistant Professor

Massachusetts Institute of TechnologyUnited States

Haruko Murakami Wainwright is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Nuclear Science and Engineering. Her research focuses on environmental informatics, integrating mechanistic modeling and machine learning to address challenges in nuclear waste management, radiation monitoring, and environmental resilience. She leads interdisciplinary projects such as the DOE’s Advanced Long-term Environmental Monitoring Systems (ALTEMIS) and the Artificial Intelligence for Earth System Predictability (AI4ESP) initiative. Education: BEng in Engineering Physics from Kyoto University (2003), MS and PhD in Nuclear Engineering and Statistics from UC Berkeley (2006–2010). Research emphasizes Bayesian geostatistical methods, real-time environmental monitoring systems, and applications in watershed science. Notable contributions include radiation dose rate mapping post-Fukushima, groundwater contamination modeling, and sustainable remediation strategies. Her work bridges nuclear engineering, environmental science, and data science, with a focus on resilience to environmental disasters and sustainable energy waste management.

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Zach BransonView profile

Academic

Carnegie Mellon UniversityQatar

Zach Branson is an Associate Teaching Professor and Assistant Director for the Undergraduate Program at Carnegie Mellon University's Department of Statistics & Data Science within the Dietrich College of Humanities and Social Sciences. His work bridges theoretical and applied statistics, with a focus on experimental design and causal inference. PhD in Statistics , Harvard University (2019) BS in Economics and Statistics , Carnegie Mellon University (2014) BA in Professional Writing , Carnegie Mellon University (2014) Branson's research centers on experimental design and causal inference, particularly addressing questions like "Does a treatment cause a change in outcomes?" His methodological interests include covariate balance , matching , randonization tests , and regression discontinuity designs . Applications span education , epidemiology , mental health , and text analysis . His recent publications emphasize causal inference in educational and social sciences, with methodological innovations in rerandomization, propensity score trimming, and regression discontinuity designs. Key themes include improving covariate balance, handling continuous treatments, and applying Bayesian nonparametric approaches to spatial data. NSF Graduate Research Fellowship (for PhD work on experimental design) Branson actively engages in teaching causality and statistical communication to undergraduates, advocating for capstone courses (e.g., 36-490, 36-493, 36-497) to foster practical data science skills. He also contributed to public-facing statistical pedagogy through "data science portfolio" projects.

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Matthias KatzfussView profile

Professor

University of Wisconsin-MadisonUnited States

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

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Chunming ZhangView profile

Professor

University of Wisconsin-MadisonUnited States

Chunming Zhang is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Statistics within the School of Computer, Data & Information Sciences. He holds academic leadership roles and has been a faculty member since 2000. His research focuses on statistical learning theory and applications in neuroinformatics, bioinformatics, financial econometrics, and imaging data analysis. Key areas include high-dimensional inference, robust statistics, and functional data analysis. Education: Ph.D. in Statistics (University of North Carolina-Chapel Hill, 2000), M.S. in Computational Mathematics (Academia Sinica, 1995), B.S. in Mathematical Statistics (Nankai University, 1992). His research interests span computational neuroscience, medical imaging, and financial time series analysis. Notable contributions include methodologies for neuroimaging data analysis, robust statistical estimation, and large-scale multiple testing. He has authored over 50 peer-reviewed publications and serves on editorial boards of leading journals such as the Annals of Statistics and Journal of the American Statistical Association. Professional service includes roles as Associate Editor for multiple top-tier journals and leadership in statistical societies. Teaching focuses on advanced statistical theory, including courses on Mathematical Statistics and Nonparametric Methods.

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Dennis FokView profile

Professor

Erasmus University RotterdamNetherlands

D. (Dennis) Fok is a Professor of Econometrics and Data Science at the Department of Econometrics, Erasmus School of Economics, Erasmus University Rotterdam. He has been affiliated with the institution since 1999 and serves as a Fellow at the Erasmus Research Institute of Management (ERIM). His research focuses on applied econometrics in marketing contexts, particularly: Modeling unobserved heterogeneity Nonlinear econometric models Bayesian statistical methods Consumer-level decision modeling High-dimensional choice modeling Dynamic marketing mix effects Dennis Fok's work combines econometric theory with practical marketing applications, developing efficient estimation techniques for complex consumer behavior models. His methodological contributions have been adopted in various business contexts including retail analytics and loyalty program optimization. ERIM Top Article Junior Award (2017) ERIM Top Article Award (2010) ERIM Award for Outstanding Performance by a Young Researcher (2006) ERIM Dissertation Award (2004) He has supervised numerous PhD candidates across diverse topics including: Marketing modeling for new products Econometric advances in diffusion models Heterogeneity in customer purchase behavior Brand portfolio management High-dimensional assortment modeling Response style modeling

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Joao MaiaView profile

Professor

Case Western Reserve UniversityUnited States

Joao Maia, PhD, is a Professor in the Department of Macromolecular Science and Engineering at Case School of Engineering, Case Western Reserve University. His research focuses on polymer rheology, extensional rheometry, and advanced processing techniques like layer multiplication coextrusion. He holds a PhD in Polymer Science and Engineering from the University of Minho (2007), a Bachelor of Science in Physics Engineering from the Technical University of Lisbon (1992), and a Bachelor of Science in Rheology/Applied Mathematics from the University of Wales (1996). Research Interests: Multi-scale polymer-based materials Computational and experimental polymer rheology Nano-layered materials processing On-line monitoring in extrusion Multiscale modeling of complex fluids Recent work highlights include applying machine learning to predict material behavior in suspensions and developing advanced extrusion technologies for layered polymer systems. His publications span topics like graphene oxide nanocomposites, drug delivery nanocarriers, and computational modeling of polymer dynamics. Grants and advising details are not explicitly listed, but his extensive publication record reflects active collaboration with industry and academic partners. He contributes to innovations in additive manufacturing and biomedical polymer applications.

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Rahul ParhiView profile

Assistant Professor

University of California, San DiegoUnited States

Rahul Parhi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego (UCSD), where he has been since 2024. Previously, he was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) from 2022 to 2024, and completed his Ph.D. in Electrical Engineering at the University of Wisconsin–Madison in 2022. His research focuses on the mathematics of data science, particularly the theoretical foundations of neural networks and deep learning, with a strong emphasis on functional/harmonic analysis, signal processing, and nonparametric statistics. Key research themes include the mathematics of computed tomography, sparsity, compressed sensing, and the geometry of Banach spaces. Education: B.S. in Mathematics, University of Minnesota, Twin Cities (2018) B.S. in Computer Science, University of Minnesota, Twin Cities (2018) Ph.D. in Electrical Engineering, University of Wisconsin–Madison (2022) His research interests span the interplay between functional and harmonic analysis with data science, including: Foundations of neural networks and deep learning Approximation properties of neural networks Nonparametric statistical estimation Inverse problems and compressed sensing Wavelet-based signal processing Banach space theory His recent publications explore topics such as neural network optimization, attention mechanisms, and the theoretical limits of learning in high-dimensional spaces. He has contributed to understanding the mathematical principles underlying deep learning's success in breaking the curse of dimensionality. His work bridges abstract mathematical concepts with practical applications in signal processing and machine learning. Parhi's research also extends to collaborative projects with experts in optimization and applied mathematics, aiming to develop novel algorithms with rigorous theoretical guarantees. His lab at UCSD focuses on advancing interdisciplinary research at the intersection of mathematics and data science.

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Michał DerezińskiView profile

Assistant Professor

University of Michigan-Ann ArborUnited States

Michał Dereziński is an Assistant Professor of Computer Science and Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on the theoretical foundations of randomized algorithms for numerical linear algebra, machine learning, optimization, and data science. Previously, he held postdoctoral positions at UC Berkeley (Department of Statistics) and the Simons Institute for the Theory of Computing, and completed his Ph.D. in Computer Science at UC Santa Cruz under Manfred Warmuth, alongside Master's degrees in Mathematics and Computer Science from the University of Warsaw. He teaches courses such as Foundations of Computer Science (EECS 376), Algorithms for Data Science (EECS 474), and specialized RandNLA courses. His work emphasizes bridging theory and practice in RandNLA, with applications to ML and optimization. Key achievements include an NSF CAREER Award and a NeurIPS 2020 Best Paper Award for work on column subset selection. Current advisees include Sachin Garg, Jiaming Yang, and Shabarish Chenakkod. His research explores topics like sketching algorithms, stochastic optimization, and random matrix theory, with publications in top venues such as NeurIPS, COLT, and SIAM journals. He actively contributes to the RandNLA community through tutorials and software development efforts like RandBLAS/RandLAPACK initiatives.

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