جست‌وجوی استاد و پژوهشگر

• Statistics
• Statistical Inference
• Time-Series Analysis
• +۳ مورد دیگر

Emanuele Taufer is a Full Professor of Statistics at the Department of Economics and Management of the University of Trento. His academic career includes roles as Vice Director of the Department of Computer Science and Business Studies and Faculty Delegate for International Relations. He holds a Ph.D. in Statistics from Cardiff University, an M.Sc. in Mathematical Statistics from George Washington University, and a Laurea in Economics from the University of Trento. His research focuses on statistical inference, stochastic processes, goodness-of-fit tests, and applications in ESG analysis. Notable contributions include work on exponentiality testing, graphical models, and financial dependence modeling. He has been recognized for his 2002 paper on mean residual life characterization at the SIS2002 conference. Recent research trends emphasize methodological advancements in ESG performance measurement, sparse network estimation for heavy-tailed data, and generalized precision matrices for financial risk modeling. His work spans theoretical statistics, applied econometrics, and interdisciplinary topics like environmental governance. Education: Ph.D. in Statistics, Cardiff University (UK) M.Sc. in Mathematical Statistics, George Washington University (USA) Laurea in Economics, University of Trento (Italy) Professional Roles: Full Professor of Statistics at University of Trento (2003–present) Associate Professor (2003–2003), Assistant Professor (1996–2002) Awards: 2002 SIS2002 Recognition for innovative statistical testing methodology Key Research Themes: Stochastic processes and estimation ESG methodology and financial reporting High-dimensional data analysis Goodness-of-fit tests and tail index estimation

Tatjana Pavlenkoمشاهده پروفایل

استاد

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +۴ مورد دیگر

Tatjana Pavlenko is a Professor in Statistics at Uppsala University, affiliated with the Department of Statistics. Her research bridges mathematical statistics, probability theory, and computational methods, focusing on high-dimensional data analysis in biomedical and machine learning contexts. Key research areas include: High-dimensional statistical inference and Bayesian graph structure learning Sparse signal detection and adaptive thresholding methods Statistical machine learning with applications to biomedical datasets Her recent publications demonstrate expertise in: Bayesian model averaging and junction tree sampling Asymptotic theory for high-dimensional classifiers Testing independence and covariance structures L2-type statistics and empirical process theory She supervises PhD students working on: High-dimensional causal inference in media Bayesian graphical models Sparse classification algorithms Currently active in Uppsala University's AI4Research initiative, Pavlenko develops adaptive data-driven procedures for statistical learning problems with complex sparsity patterns.

Marc Chevretteمشاهده پروفایل

استادیار

• Natural Product Discovery
• Genome Mining
• Microbial Interactions
• +۴ مورد دیگر

Marc Chevrette serves as an Assistant Professor and Discovery Fellow in the Department of Plant Pathology at the University of Wisconsin–Madison's College of Agricultural and Life Sciences, with laboratory operations based in the Discovery Building. His interdisciplinary research bridges genomics, bioinformatics, and microbiology to investigate microbial secondary metabolism and interspecies interactions within complex communities. Education: PhD in Genetics, University of Wisconsin–Madison Dr. Chevrette's research program centers on evolutionary-guided natural product discovery, specializing in genome mining for novel antibiotics and biosynthetic pathway characterization. His lab develops computational tools like GATOR-GC to map biosynthetic diversity across bacterial lineages, with particular emphasis on lasso peptides, nonribosomal synthetases, and microbial community dynamics. This work integrates phylogenetics with experimental validation to uncover chemical ecology principles driving secondary metabolism. Analysis of his 2023-2025 publications reveals consistent innovation in bioinformatics infrastructure (antiSMASH, MIBiG) and machine learning applications for substrate specificity prediction. His research demonstrates strong translational potential for antimicrobial discovery, with recurring themes of evolutionary adaptation in biosynthetic gene clusters and community-mediated metabolite regulation. Scientific Awards: Discovery Fellow (Wisconsin Institute for Discovery) Dr. Chevrette directs an active research group supported by federal grants focused on antibiotic discovery and microbial ecology. His mentorship includes training graduate students in interdisciplinary methodologies spanning computational biology, microbial genetics, and natural product chemistry, with emphasis on translating genomic insights into experimental validation. The Chevrette Lab operates within the Wisconsin Institute for Discovery, leveraging its collaborative environment to integrate genomic data with ecological modeling. Current projects investigate how nutrient availability and interspecies competition shape biosynthetic potential in soil and marine microbial communities, with implications for drug discovery pipelines.

Omid Chatrabgounمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Bayesian Machine Learning
• Gaussian Processes
• +۵ مورد دیگر

Dr. Omid Chatrabgoun serves as an Assistant Professor in Data Science and AI at the CEES School of Science, holding a PhD in Data Science from Shahid Chamran University (Iran, 2015). Previously affiliated with Malayer University, he teaches undergraduate and postgraduate courses including Statistical Learning, Data Science, and Machine Learning while actively supervising PhD students. His research focuses on uncertainty quantification and Bayesian machine learning for complex systems. Education: PhD in Data Science, Shahid Chamran University, Iran (2015) Dr. Chatrabgoun specializes in Modelling, Optimization, and Uncertainty Quantification (UQ) using (Deep) Gaussian processes, with applications spanning bioinformatics (Gene Regulatory Networks, Protein-Protein Interactions) and environmental engineering (coastal protection, flood modeling). He develops novel approaches for machine learning of linear operational equations to quantify uncertainty in highly complex systems, emphasizing computational efficiency through sparse approximations and kernel methods. His work bridges theoretical statistics with practical implementations in healthcare, environmental science, and industrial applications. Analysis of his 29 publications (2016-2025) reveals a consistent trajectory in Gaussian process methodologies, evolving from foundational graphical models to advanced deep Gaussian processes and probabilistic surrogate modeling. Key thematic clusters include bioinformatics network inference, environmental risk assessment, and computational optimization techniques, demonstrating interdisciplinary impact across computer science, statistics, and domain-specific applications. Dr. Chatrabgoun has secured research funding and conducted consultancy projects with the Iran National Science Foundation (INSF) and Research Institute for Grapes and Raisin (RIGR). His collaborative network spans international institutions, as evidenced by co-authorships across civil engineering, bioinformatics, and environmental science domains. Current projects involve developing machine learning frameworks for linear differential operators and expanding applications of pair-copula Bayesian models in high-dimensional data analysis. His research group actively explores uncertainty propagation in PDE-based models and spatio-temporal environmental simulations, maintaining strong industry-academia partnerships for knowledge exchange in data-driven decision systems.

Haohan Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

Ekaterina Sereginaمشاهده پروفایل

استادیار

• Financial Econometrics
• Econometric Theory
• Graphical Models
• +۳ مورد دیگر

Ekaterina Seregina is the Douglas Assistant Professor of Economics and Finance at Colby College's Department of Economics. Her research focuses on financial econometrics, econometric theory, graphical models, portfolio optimization, machine learning, and forecasting. She teaches courses such as Corporate Finance I, Corporate Finance II, and Seminars on Financial Technology. Her work bridges statistical methodologies with practical applications in finance and economics, emphasizing high-dimensional data analysis and machine learning techniques. Her research interests include developing sparse portfolio strategies, volatility forecasting using Bregman functions, and integrating graphical models with machine learning for econometric analysis. She has contributed to optimizing portfolios under unstable conditions and improving forecast accuracy through structural break detection. Her recent studies address challenges in financial risk modeling and efficient data science techniques for public health crises like the pandemic. Dr. Seregina’s publications span topics from covariance matrix estimation to applications of graphical Lasso in portfolio management. Her work often highlights interdisciplinary approaches, combining econometrics with modern computational methods. She maintains an active research agenda in financial technology and macroeconomic forecasting, reflecting her commitment to advancing both theoretical and applied economics.

• Statistical Genetics
• Graphical Models
• Genomics
• +۵ مورد دیگر

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

Qing Zhouمشاهده پروفایل

استاد

• Causal inference and graphical models
• High-dimensional statistics
• Monte Carlo methods
• +۵ مورد دیگر

Qing Zhou is Professor and Chair of Statistics & Data Science at the University of California, Los Angeles, and a faculty member in the Interdepartmental Program in Bioinformatics. His methodological work integrates causal inference, high-dimensional statistics, Monte-Carlo computation and statistical bioinformatics, with applications ranging from gene-regulatory networks to financial risk models. Education & Affiliations: Professor & Chair, Department of Statistics and Data Science, UCLA Faculty, UCLA Interdepartmental Program in Bioinformatics Ph.D. training in Statistics (implied by faculty rank and publication record) Research Interests: Zhou develops theory and algorithms for learning directed acyclic graphs (DAGs) to uncover causal relations from large-scale data. His group designs penalized likelihood, divide-and-conquer and federated strategies for structure learning of Bayesian networks, accompanied by R packages and scalable software. In high-dimensional inference he introduced “estimator augmentation” to deliver honest confidence sets for lasso-type estimators, and recently proposed projection-and-shrinkage techniques that merge sparse regularization with Stein estimation. Monte-Carlo innovation is another pillar: he invented the equi-energy sampler and random-walk schemes to explore energy landscapes and reconstruct density-of-states in statistical physics and Bayesian computation. In bioinformatics he builds model-based and sparse-regression tools to integrate RNA-seq, ChIP-seq, chromatin-interaction and sequence data, leading to biological discoveries on RNA editing, alternative splicing and embryonic-stem-cell regulatory circuits. Publication Trends: Recent articles focus on (i) causal discovery in nonlinear and high-dimensional DAGs, (ii) federated and privacy-preserving learning of causal networks, (iii) confidence-set construction post model-selection, and (iv) stochastic blockmodels and graphons for network data. A 2025 research monograph synthesizes advances in latent structure and causal graphical models. Scientific Awards & Recognition: Authored research monograph “Latent Structure and Causality: Inference from Data”, World Scientific 2025 Consistent federal and UCLA funding (implied by continuous publication output and chair position) Advising & Grants: While specific student names and grant numbers are not listed in the source text, the sustained publication stream in top-tier journals and the role as department chair indicate active supervision of doctoral students and postdocs and ongoing NIH/NSF support. Labs & Teams: Zhou leads the computational statistics and causal inference group within the UCLA Department of Statistics & Data Science, maintaining cross-departmental collaborations with the Bioinformatics program and experimental biology labs on campus.

Xiangnan Kongمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۵ مورد دیگر

Xiangnan Kong is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), serving as the Graduate Coordinator of the Data Science Program. His academic journey includes a BS and MS from Nanjing University (2006, 2009) and a PhD from the University of Illinois at Chicago (2014). He maintains an active research program with significant contributions to data mining and machine learning. Dr. Kong's primary research interests focus on data mining and machine learning, with emphasis on addressing data science problems in biomedical and social applications. His work specifically targets data variety issues across multiple domains including neuroscience, biomedical informatics, social networks, and business intelligence. He has published extensively in top-tier conferences and journals such as KDD, ICDM, SDM, WWW, WSDM, CIKM, and TKDE. His recent publications demonstrate a strong trend toward interdisciplinary applications, with significant work in neuroimaging analysis, healthcare applications, urban traffic estimation, and efficient deep learning architectures. His research bridges theoretical advances in graph mining, heterogeneous information networks, and deep learning with practical applications in biomedical domains and social computing. Dr. Kong has successfully advised numerous PhD and Master's students, with former students now working at prominent organizations including Facebook Research, VISA Research, MIT CSAIL, and Amazon AI. His research is supported by multiple grants including NSF awards, Adobe Gift Grant, Huawei Research Grant, and WPI TRIAD Grant.

Sang-Yun Ohمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning
• High-Dimensional Data Analysis
• +۶ مورد دیگر

Sang-Yun Oh is an Associate Professor at the University of California, Santa Barbara. His research focuses on statistical learning, high-dimensional data analysis, and machine learning applications across diverse domains, including genomics, climate science, and physics. He is affiliated with the university's research community and maintains an office in South Hall 5514. His work bridges theoretical advancements in statistical methods and practical applications in areas like graphical models, covariance estimation, and deep learning. Oh's research interests encompass scalable algorithms for high-dimensional data, Bayesian networks, and robust optimization. His contributions span computational methods for inverse covariance estimation, distributed optimization, and deep learning applications in neutrino physics and climate pattern detection. He has also explored interdisciplinary applications, such as analyzing high-energy physics data at NERSC and designing web-based systems for online advertising. His publications reflect a trajectory toward integrating statistical theory with real-world challenges, including the development of FROSTY for Bayesian network learning and successive standardization techniques for biomedical studies. His work emphasizes methodological innovation and computational efficiency, often addressing scalability and robustness in complex datasets.

Tim Hoheiselمشاهده پروفایل

دانشیار

• Nonsmooth Optimization
• Variational Analysis
• Continuous Optimization
• +۴ مورد دیگر

Tim Hoheisel is an Associate Professor at McGill University's Department of Mathematics and Statistics. His research focuses on nonsmooth optimization, variational analysis, and their applications in continuous optimization and machine learning. He is the director of the CRM Applied Math Laboratory and co-chair of the CRM thematic program on Mathematical Foundations of Data Science (2025). He serves as an Associate Editor for *Mathematics of Operations Research* and *Open Journal of Mathematical Optimization*. Hoheisel has organized conferences and seminars, including the CRM Applied Math Seminar in Montreal. He has advised numerous PhD and MSc students and postdocs, collaborating on topics like regularization methods, inverse problems, and convex analysis. Education: PhD in Mathematics from University of Würzburg (2009). Committee Roles: Advisor for Honours Applied Mathematics (B.Sc.) Program, elected member of the Chair's Advisory Committee. His research integrates theoretical analysis with applications, bridging applied and pure mathematics. Recent work includes contributions to the Maximum Entropy on the Mean method for inverse problems, Lipschitz stability in LASSO, and regularization techniques. Hoheisel is a member of SIAM and the MTL MLOpt group, focusing on optimization-machine learning interfaces. Recent presentations include a semi-plenary talk at the 2025 International Conference on Continuous Optimization and lectures on regularized least-squares at the University of Trier. His publications span optimization theory, convex analysis, and applications in image processing and quantum Monte Carlo data analysis. Awards: None explicitly mentioned. Grants/Funding: Not detailed; inferred from editorial and leadership roles. He leads the CRM Applied Math Lab and collaborates with institutions like Concordia University and the University of Ottawa through INTER-MATH-AI. His work emphasizes interdisciplinary approaches, particularly in data science and AI integration with mathematical optimization.

• Machine Learning
• Signal Processing
• Pattern Recognition
• +۳ مورد دیگر

Mário A. T. Figueiredo is an IST Distinguished Professor and Feedzai Professor of Machine Learning at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST) . He leads the Lisbon Unit for Learning and Intelligent Systems (LUMLIS) within ELLIS and coordinates research clusters at Instituto de Telecomunicações. His research focuses on machine learning , signal processing , and image restoration , particularly using sparsity-based methods , wavelet transforms , and Bayesian algorithms . Recent work includes 3D shape correspondence , hyperspectral sharpening , and adaptive optimization techniques . Key article themes: Biclustering , ADMM algorithms , sparsity regularization , and generative embeddings Scientific recognition: EURASIP Fellow , Clarivate Highly Cited Researcher , IEEE/IAPR Fellow , and multiple best paper awards He has mentored numerous PhD/MSc students and contributed to information-theoretic kernels , feature discretization , and multimodal data analysis in biomedical and computer vision applications.

• Stochastic Processes
• High-Dimensional Statistics
• Financial Econometrics
• +۳ مورد دیگر

Yuta Koike is an Associate Professor at the Graduate School of Mathematical Sciences, University of Tokyo . His research focuses on statistical inference for stochastic processes , particularly in high-frequency financial data and high-dimensional statistics . He has contributed to covariance estimation under non-synchronous observations, microstructure noise, and jumps, and recently explores lead-lag relationships between stochastic processes. Research Interests : Stochastic processes, high-dimensional statistics, financial econometrics, high-frequency data, probability theory. Awards : The 32nd JSS Ogawa Award The 1st ISI Tokyo Memorial Award Editorial Roles : Associate Editor for Asia-Pacific Financial Markets (2019–present), Bernoulli (2025–present), and Japanese Journal of Statistics and Data Science (2023–present). Teaching : Courses in statistical analysis, econometrics, and probability theory at the University of Tokyo, Seijo University, and Tokyo Metropolitan University. His publications span journals like Annals of Statistics , Stochastic Processes and their Applications , and Journal of Theoretical Probability . He actively presents at international conferences, including the Joint Statistical Meetings and SPA Conference .

• High-dimensional time-series analysis
• Statistical modeling under non-stationarity
• Sparse dynamic factor models
• +۳ مورد دیگر

Dr Alex Gibberd is a Senior Lecturer (equivalent to Associate Professor) in Statistics within the School of Mathematical Sciences at Lancaster University. He has been a faculty member since 2018, following postdoctoral research at Imperial College London and a PhD in Statistics from University College London (UCL) in 2017. He also holds an MPhys in Astrophysics from the University of St Andrews (2012). Education: PhD in Statistics, University College London (2017) MPhys in Astrophysics, University of St Andrews (2012) Research Interests: Dr Gibberd's research focuses on high-dimensional time-series analysis, with methodological contributions in statistical modeling under non-stationarity and high-dimensionality. His work spans both theoretical developments and practical applications, particularly in neuroscience and finance. Key areas include: Sparse dynamic factor models and regularized estimation techniques Spectral analysis and locally-stationary wavelet models Optimization algorithms for model selection in high-dimensional settings Applications in brain connectivity analysis and economic forecasting Research Themes: His recent publications demonstrate a strong focus on developing interpretable statistical methods for complex systems. These include advances in principal component analysis with joint rank and covariance estimation, sparse dynamic factor models, and regularized spectral estimation for high-dimensional point processes. The applications range from neural connectivity modeling to energy efficiency policy analysis. Grants & Funding: Research in Paris Grant (2024) Support of Collaborative Research with Dr S. Roy at University of Bath (2021) Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics (2021-2023) Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation (2021-2025) Wavelet Methods for Dependency Analysis in Multivariate Time Series (2020) STOR-i: Information Fusion for Non-homogeneous Panel and Time-series Data (2019-2025) PhD Supervision & Students: Dr Gibberd supervises PhD students at the intersection of high-dimensional statistics and time-series analysis. Current students include Carla Pinkney (STOR-i CDT), Ziyan Zhao, and Kai Zheng (Centre for Marketing Analytics & Forecasting). Research Affiliations: STOR-i Centre for Doctoral Training Centre for Marketing Analytics & Forecasting Changepoints and Time Series Research Group Data Science Institute - Foundations Social and Economic Statistics Group

Xiaohui Xieمشاهده پروفایل

استاد

• AI/Machine Learning
• Neural Networks
• Deep Learning
• +۳ مورد دیگر

Xiaohui Xie is a Professor in the Department of Computer Science at the Bren School of Information and Computer Sciences, University of California, Irvine. He joined UC Irvine in 2007 after completing his PhD at MIT and postdoctoral training at the Broad Institute of MIT and Harvard University. His research focuses on AI/machine learning, neural networks, deep learning, and genomics. PhD in Computer Science, MIT Postdoctoral training at Broad Institute Research interests span AI, neural networks, deep learning, and genomics, with applications in medical imaging, computational biology, and computer vision. Recent publications highlight advancements in 3D convolutional networks for nodule detection, medical image analysis, and hybrid models for DNA sequence function quantification. Scientific awards and honors are not explicitly mentioned in the provided text. His teaching includes courses like CS273P (Machine Learning and Data Mining), CS175 (Project in AI), and foundational classes in computational linear algebra and optimization, reflecting his interdisciplinary expertise.