جست‌وجوی استاد و پژوهشگر

Alex Gittensمشاهده پروفایل

استادیار

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +۳ مورد دیگر

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.

Omer Sanمشاهده پروفایل

دانشیار

• Scientific Machine Learning
• Digital Twins
• Artificial Intelligence
• +۳ مورد دیگر

Omer San is an Associate Professor in the Department of Mechanical, Aerospace and Biomedical Engineering at the University of Tennessee, Knoxville (2023–present). Previously, he served as Associate Professor (2021–2023) and Assistant Professor (2015–2021) at Oklahoma State University. His research focuses on scientific machine learning, digital twins, and fluid dynamics, emphasizing hybrid analysis and modeling approaches to address challenges in turbulence simulation and data assimilation. PhD in Engineering Mechanics, Virginia Tech (2012) MS in Aerospace Engineering, Old Dominion University (2007) BS in Aeronautical Engineering, Istanbul Technical University (2005) Research interests include physics-guided machine learning, data-driven reduced order modeling, and large eddy simulation closure techniques. His work bridges first-principles models and data-driven methods to enhance predictive accuracy in complex systems such as geophysical flows and turbulence. He leads the Digital Twin Lab, advancing hybrid modeling frameworks for digital twin technologies. Notable awards include the DOE ASCR Early Career Award (2018) and OSU’s Distinguished Early Career Faculty Award (2022). His research is funded by AFOSR, DOE, NSF, NASA, and industry partnerships.

Jan Rosinskiمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Stable and Infinitely Divisible Processes
• +۳ مورد دیگر

Jan Rosinski is a Professor in the Department of Mathematics at the University of Tennessee. He holds a Ph.D. from Wrocław University (Poland). His research focuses on Probability and Stochastic Processes, particularly Stable and Infinitely Divisible Processes, Stochastic Analysis, and High-Dimensional Probability. He has held editorial roles at journals such as Probability and Mathematical Statistics and Discussiones Mathematicae - Probability and Statistics . His service includes managing editor roles and editorial board memberships across multiple international journals. Rosinski has contributed extensively to stochastic processes, Lévy processes, and infinitely divisible distributions, with a focus on theoretical developments and applications. Education: Ph.D., Wrocław University, Poland. Research Interests: Probability Theory, Stochastic Processes, Stable Processes, Infinite Divisibility, and High-Dimensional Probability. His work explores spectral representations, stochastic integrals, and applications in theoretical and applied probability. Editorial Contributions: Managing Editor of Probability and Mathematical Statistics (2008–present), and Editorial Board Member of Discussiones Mathematicae (2000–present), among others.

Daniel McKenzieمشاهده پروفایل

استادیار

• Derivative-free optimization
• Implicit neural networks
• Geometric methods in data science
• +۳ مورد دیگر

Daniel McKenzie is an Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on derivative-free optimization, implicit neural networks, and geometric methods in data science. He holds a B.Sc.(hons) and M.Sc. in Mathematics from the University of Cape Town (2010, 2014) and a PhD in Mathematics from the University of Georgia (2019). B.Sc.(hons): Mathematics and Applied Mathematics, University of Cape Town, 2010 M.Sc.: Mathematics, University of Cape Town, 2014 PhD: Mathematics, University of Georgia, 2019 His research explores the intersection of optimization theory and machine learning, with applications in spatial data modeling, geometric data analysis, and high-dimensional clustering. Recent work emphasizes curvature-aware algorithms, comparison-based optimization, and implicit network architectures like LatticeVision. His methods address challenges in non-stationary spatial data and convex game equilibria prediction. Key contributions include Fermat distance metrics for clustering, Jacobian-Free Backpropagation (JFB) for implicit networks, and zeroth-order algorithms for black-box optimization. While no scientific awards are listed, his publications reflect a strong focus on advancing optimization techniques for modern data science problems. No specific grants or advising roles are detailed in the provided text. His work bridges computational mathematics and applied AI, with potential applications in robotics, spatial statistics, and algorithmic game theory.

Chihwa Kaoمشاهده پروفایل

استاد

• Econometrics
• Panel Data Analysis
• Time Series Econometrics
• +۶ مورد دیگر

Chihwa Kao is a Professor in the Department of Economics at the University of Connecticut, affiliated with the College of Liberal Arts and Sciences. His research focuses on panel data econometrics, structural change analysis, cointegration, high-dimensional models, and time series econometrics. He has extensively contributed to methodologies addressing cross-sectional dependence, factor models, and non-stationary panel data. Key research interests include developing robust statistical techniques for panel data, testing for structural breaks in economic relationships, and analyzing economic growth factors. His work bridges theoretical econometrics with practical applications in finance and policy analysis. His articles emphasize advancements in panel data methodologies, such as bias-correction in dynamic models, clustering techniques for fixed effects, and handling serial correlation. Recent trends show a focus on high-dimensional data challenges, factor models, and structural stability assessments in large datasets. Awards and honors are not explicitly listed in the provided materials. He advises no students listed here, but his courses include Econ5315/3315 and Econ5323/4323. His CV and additional materials are available via provided links.

Harrison Huibin Zhouمشاهده پروفایل

استاد

• High-Dimensional Data Analysis
• Nonparametric Estimation
• Statistical Decision Theory
• +۳ مورد دیگر

Harrison Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University. He has held leadership roles, including Department Chair of Statistics and Data Science (2018–present) and former Chair of Statistics (2012–2017). His academic career at Yale spans over two decades, with promotions from Assistant Professor (2004–2009) to Associate (2009–2010) and full Professor (2010–present). Research Interests: Dr. Zhou specializes in high-dimensional statistical theory, including nonparametric estimation, minimax theory, and applications in network analysis, machine learning, and functional data analysis. His work bridges theoretical foundations with computational methods, addressing challenges in modern statistical decision-making. Publications: His recent work focuses on spectral clustering, quantum state tomography, and optimal estimation in high-dimensional models. Notable contributions include theoretical guarantees for algorithms like the EM method in Gaussian mixtures and advancements in community detection in networks. Teaching: He teaches advanced courses such as Functional Data Analysis, Nonparametric Estimation, and Decision Theory, reflecting his expertise in statistical methodology and theory. Professional Service: Organized workshops on topics like Empirical Processes (2015) and High-Dimensional Data (2012), underscoring his role in fostering academic collaboration.

• Actuarial science and mathematical finance
• Biostatistical methods
• High dimensional data analysis
• +۶ مورد دیگر

Narayanaswamy Balakrishnan is a Professor in the Department of Mathematics and Statistics at McMaster University, Canada. His research focuses on probabilistic and statistical models with applications in science, engineering, and medicine. Key areas include reliability analysis, statistical inference, high-dimensional data analysis, and actuarial science. He has authored/co-authored over 60 books and numerous research papers, contributing significantly to fields like order statistics, censored data analysis, and survival analysis. Notable works include Hybrid Censoring: Models, Methods and Applications (2023) and A First Course in Order Statistics (2008). His research has been recognized with prestigious honors, including election to the Royal Society of Canada in 2023. Dr. Balakrishnan mentors graduate students in statistics and has collaborated widely, with contributions to statistical methodologies in engineering, medical studies, and industrial problems. His work bridges theoretical developments with practical applications, emphasizing robust statistical techniques and innovative data analysis approaches.

Patrycja Strycharczukمشاهده پروفایل

مدرس ارشد

• Phonetics
• Articulatory Phonology
• Language Variation
• +۱ مورد دیگر

Dr. Patrycja Strycharczuk is Senior Lecturer in Linguistics at the University of Manchester, specializing in experimental phonetics and sociophonetics. Her award-winning research employs ultrasound imaging, acoustic analysis, and computational modeling to study speech production and perception. Research Focus: Strycharczuk's investigations address lingual articulation dynamics, vowel variation, and sound change mechanisms. Current projects examine speaker-specific articulation strategies, neural modeling of phonetic accommodation, and diphthongization patterns in English varieties. Methodologies include high-speed ultrasound, electromagnetic articulography, and machine learning approaches. Publication Trends: Recent articles (2023-2025) demonstrate increasing focus on computational modeling of speech production and perception. Earlier work established insights into dialect levelling, coda /l/ vocalization, and sociophonetic variation in UK English. Awards: Journal of Phonetics Best Early Career Scholar Article (2016).

Angela Yaoمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Angela Yao is a Dean's Chair Associate Professor and Assistant Dean of Research at the National University of Singapore's School of Computing, Department of Computer Science. She leads the Computer Vision and Machine Learning Group and specializes in visual perception of people, focusing on both high-level semantics of human actions and lower-level physical modeling. Her research interests span Computer Vision , Machine Learning , and Artificial Intelligence , with specific expertise in human action recognition, 3D human modeling, video understanding, and small data AI. Dr. Yao's work bridges theoretical advances with practical applications, particularly in activity anticipation and human-computer interaction. Dr. Yao's publication trends reveal a strong focus on zero-shot learning for activity anticipation, 3D human modeling, and techniques for working with limited training data. Her research has evolved from foundational work in 3D pose estimation to more recent innovations in diffusion models and cross-modal learning, demonstrating consistent contributions to advancing computer vision capabilities. NRF Fellowship for Artificial Intelligence (2019) German Pattern Recognition (DAGM) Award (2018) Dr. Yao has successfully mentored PhD students including Fadime Sener and secured significant research funding including the NRF Fellowship. Her research group focuses on developing AI systems capable of understanding and anticipating human activities with applications in robotics and human-computer interaction. She teaches CS4243 Computer Vision and Pattern Recognition and leads the Computer Vision and Machine Learning Group at NUS Computing.

Abbas Khaliliمشاهده پروفایل

استاد

• High-dimensional statistics
• Distributed learning in big data
• Neural Networks
• +۵ مورد دیگر

Abbas Khalili is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His research focuses on statistical methodology in data science, particularly high-dimensional statistics, distributed learning in big data, latent variable models (e.g., finite mixtures, hidden Markov models), and time series analysis. He holds a PhD from the University of Waterloo and BSc/MSc degrees from Isfahan University of Technology. Education: PhD in Statistics, University of Waterloo (supervised by Jiahua Chen) MSc in Mathematical Sciences, Isfahan University of Technology BSc in Mathematical Sciences, Isfahan University of Technology His research interests include post-selection inference, neural networks, sparse network analysis, and change point detection. His work has been funded by NSERC (Canada) and Fonds de recherche du Québec-Nature et technologies. Recent articles focus on mixture models, regularization in autoregressive systems, and hub structure analysis in networks. His work bridges theoretical advancements with practical applications in drug discovery and network science. Dr. Khalili advises students in PhD, MSc, and undergraduate projects in his research areas. He can be contacted at abbas.khalili@mcgill.ca .

Y. Samuel Wangمشاهده پروفایل

استادیار

• Causal Discovery
• Graphical Models
• High-Dimensional Statistics
• +۳ مورد دیگر

Y. Samuel Wang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. He holds a PhD in Statistics from the University of Washington and a BS in Applied Mathematics and Economics from Rice University. Prior to academia, he worked as a management consultant and served as a postdoctoral researcher at the University of Chicago’s Booth School of Business. His research focuses on causal discovery, graphical models, mixed membership models, and high-dimensional data analysis, with applications in network science, environmental studies, and healthcare. Education: PhD in Statistics, University of Washington BS in Applied Mathematics & Economics, Rice University Research Interests: Development of interpretable statistical methods for causal inference and graphical model structures High-dimensional data analysis, particularly in non-Gaussian settings Applications in collaborative networks, environmental microbiology, and healthcare outcomes Recent Research Trends: Recent publications emphasize causal discovery under latent confounding, functional graphical models, and gender dynamics in scholarly collaborations. Methodological contributions include robust high-dimensional inference techniques and computational tools for cyclic structural equation models. Professional Activity: Licensed on GitHub, Google Scholar, and ORCID GitHub repositories include projects on causal discovery (highDNG), gender homophily analysis (genderHomophily), and mixed membership models (mixedMem)

Auguste Genovesioمشاهده پروفایل

پژوهشگر

• Computational Bioimaging
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.

Jiaming Liangمشاهده پروفایل

استادیار

• Mathematical Optimization
• Sampling Algorithms
• Stochastic Programming
• +۳ مورد دیگر

Jiaming Liang is an Assistant Professor in the Department of Computer Science and Data Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. His research focuses on designing efficient algorithms for optimization and sampling in data science, with expertise in convex/nonconvex optimization, stochastic programming, and algorithmic game theory. He holds a PhD in Operations Research from Georgia Tech and completed a postdoctoral fellowship at Yale University under Andre Wibisono. Education PhD in Operations Research, Georgia Institute of Technology Postdoctoral Researcher, Yale University (Yale CS Department) Research Interests Liang's work bridges theoretical optimization and practical algorithm design. He develops scalable methods for nonsmooth, stochastic, and high-dimensional problems, with applications in machine learning and data analysis. Key areas include: Proximal bundle methods for hybrid optimization Sampling algorithms under non-smooth conditions Accelerated gradient techniques Algorithmic game theory applications Publications His recent work emphasizes algorithmic unification (e.g., combining accelerated gradients with proximal bundle methods) and variance reduction strategies. He explores stochastic optimization without smoothness assumptions and develops sampling methods for nonconvex potentials. Labs/Teams While specific lab affiliations aren't detailed here, his research aligns with computational optimization and data science groups at the Hajim School.

Carlo Cavicchiaمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Machine Learning
• +۶ مورد دیگر

Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.