جست‌وجوی استاد و پژوهشگر

Nagi Gebraeelمشاهده پروفایل

استاد

• Predictive Analytics
• Machine Learning
• Prognostics and Health Management
• +۵ مورد دیگر

Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.

Shen Weiمشاهده پروفایل

استاد

• International Investment Law
• Corporate Governance
• Financial Regulation
• +۱۰ مورد دیگر

Shen Wei is the KoGuan Distinguished Professor of Law at the Shanghai Jiao Tong University Law School, with a concurrent role as Visiting Professor (2025). His academic career spans legal practice and academia, focusing on international investment law, corporate governance, financial regulation, and international commercial arbitration. Concurrently, his research extends into computational and mathematical domains, including machine learning, deep neural networks, and approximation theory. He teaches international investment law, international financial regulation, company law, and international economic law. His interdisciplinary work bridges legal scholarship with advanced mathematical modeling and algorithmic analysis. Recent research emphasizes neural network architecture, optimization techniques, and approximation theory applied to complex systems. Notable contributions include studies on deep network expressivity, gradient methods, and wavelet-based image restoration. Awards and grants are not explicitly mentioned, but his work reflects significant contributions to both legal and computational fields.

Hamid Krimمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Hamid Krim is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He leads the Vision, Information and Statistical Signal Theories and Applications (VISSTA) group, focusing on statistical signal/image analysis, data science, and machine learning. His prior roles include Research Scientist at MIT’s Laboratory for Information and Decision Systems and Member of Technical Staff at AT&T Bell Labs. He holds a Ph.D. in Electrical Engineering from Northeastern University, and degrees from the University of Washington and University of Southern California. Education: Ph.D., Electrical Engineering, Northeastern University (MA), 1990s Master's, Electrical Engineering, University of Washington Bachelor's, Electrical Engineering, University of Southern California and University of Washington Research Interests: Machine Learning, AI, Signal Processing, Communications, and Control Systems . His work bridges formal mathematical frameworks with applied problems, emphasizing generative AI, adversarial robustness, and subspace-driven data analysis. Recent innovations include Volterra neural networks and expansive synthesis techniques for data generation. Awards & Recognition: 2000 NSF CAREER Award 2008 IEEE Fellow 2019 IEEE SPS Sustained Impact Paper Award Multiple extended research invitations at top institutions globally Grants & Advising: Leads the VISSTA Lab, collaborating on projects like medical algorithm development (e.g., lung wheeze analysis) and hurricane activity prediction. His work spans interdisciplinary applications in healthcare, robotics, and defense systems. Labs & Teams: Director of the VISSTA Lab, fostering research in signal theory and machine intelligence. Collaborates with academia and industry on cutting-edge AI and sensor fusion technologies.

Xiaofeng Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

• Data Science
• Machine Learning
• Harmonic Analysis
• +۷ مورد دیگر

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Wenzhong Liمشاهده پروفایل

استاد

• AI-Empowered Distributed Systems
• MultiModal Large Models
• Embodied Intelligence
• +۹ مورد دیگر

Wenzhong Li is a Professor at the School of Computer Science, Nanjing University, where he leads research at the State Key Laboratory for Novel Software and Technology. His academic career spans over 15 years with significant contributions to AI-empowered distributed systems, big data mining, and networking applications. He teaches Computer Networks and guides graduate students in Distributed Computing Research. Professor Li's research focuses on cutting-edge areas including AI-Empowered Distributed Systems and Applications (MultiModal Large Models, Embodied Intelligence, Edge Computing), Big Data Mining (Time Series Analysis, Graph Computing, Social Networks Analysis), and AI-Based Distributed Resource Scheduling. His work bridges theoretical foundations with practical implementations in real-world systems. His recent publications demonstrate a strong trend toward integrating deep learning with graph theory and time series analysis, with applications in human activity recognition, network optimization, and multimodal systems. The research spans multiple disciplines including artificial intelligence, computer vision, networking, and data mining, with a particular emphasis on practical implementations for real-world problems. Best Paper Runner Up at KSEM 2023 for 'Learning-based Dichotomy Graph Sketch for Summarizing Graph Streams with High Accuracy' Best Paper Award at APNet 2018 for 'Toward Effective and Fair RDMA Resource Sharing' Professor Li has advised numerous PhD and Master's students who have gone on to prominent positions at institutions like Nanjing University, Huawei, Alibaba, Microsoft, and various international universities. His research is supported by substantial grants from the National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, National Power Grid, and other major funding bodies, totaling multiple multi-year projects with significant budgets. He leads the AINet Group and is affiliated with the Sino-German Institute of Social Computing and MobileCloud research initiatives. His DISLAB provides the organizational framework for his research team, which includes dozens of graduate students and collaborators working on cutting-edge problems in AI, networking, and distributed systems.

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Kenan Liمشاهده پروفایل

دانشیار

• Spatial Computation
• Environmental Health
• Community Resilience
• +۱۲ مورد دیگر

Kenan Li, Ph.D., is an Associate Professor in the Department of Epidemiology and Biostatistics at Saint Louis University’s College for Public Health and Social Justice. He joined SLU in August 2022 and teaches courses such as Statistical Learning, R for Spatial Analysis, and Environmental Determinants of Health. His research bridges data science, GIS, and public health, focusing on spatial computation, environmental exposures, and community resilience. Ph.D. in Environmental Sciences, Louisiana State University M.S. in Environmental Sciences, Louisiana State University B.S. in Environmental Sciences and Applied Mathematics, Nankai University, China Dr. Li’s research interests lie at the intersection of spatial computation, environmental health, and community resilience . He develops geo-AI frameworks , integrated geo-cyber-infrastructures , and biostatistics algorithms using big data, deep learning, and sensor data. His work emphasizes understanding human-environment interactions, urban sustainability, and health disparities. His recent publications from 2023 to 2015 reveal a strong trend in spatial modeling of population dynamics , machine learning for environmental exposure analysis , and resilience assessment in vulnerable coastal regions. He has pioneered methods like Dynamic Time Warping Self-Organizing Maps and Wavelet-based Shapelet Discovery to extract meaningful patterns from high-frequency sensor data. His scientific awards include the Taylor Geospatial Institute Seed Grant (2023) , the Saint Louis University 2023 Health Research Grant , and selection for the Scholarly Undergraduate Research Grants and Experiences . He has secured funding from NSF, NIH, USC Keck School of Medicine, and the US Army Corps of Engineers. Dr. Li has advised and collaborated on numerous research projects, particularly in interdisciplinary teams studying the Mississippi River Delta and urban health interventions. He has been involved in NIH/NIBIB-funded projects and led research on emergency management of trail systems in Los Angeles County. He is actively involved in building research labs and teams focused on spatial data science and public health analytics , having previously worked at USC’s Spatial Sciences Institute and Population and Public Health Sciences Department.

Bei Jiangمشاهده پروفایل

دانشیار

• Biostatistics
• Functional Data Analysis
• Statistical Machine Learning
• +۶ مورد دیگر

Dr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , Canada. She holds the Canada CIFAR AI Chair and is affiliated with the Alberta Machine Intelligence Institute (Amii) . Her academic journey includes a PhD in Biostatistics (2014) from the University of Michigan, MS (2008) and BS (2004) from the University of Alberta and Beijing University of Technology, respectively. Current Positions : 2021–Present (Associate Professor), 2022–Present (CIFAR AI Chair) Past Appointments : Assistant Professor (2015–2021), Postdoctoral Fellow at Columbia University (2014–2015), Research Assistant at University of Michigan (2009–2013) Research Interests : Dr. Jiang specializes in methods for joint modeling of longitudinal and health outcome data , Bayesian hierarchical modeling , functional and imaging data analysis , and statistical machine learning . Her work integrates kernel machine regression , differential privacy , and synthetic data generation to address challenges in heterogeneous health data and neuroimaging. Scientific Awards : Highlights include the 2015 SAMSI New Research Fellow , multiple Rackham Conference Travel Awards (2013, 2012), and prestigious NSERC scholarships (2009–2012). She has also received the J Gordin Kaplan Graduate Award (2008) and Statistical Society of Canada Travel Award (2008). Grants : $375,000 (CIFAR AI Chairs, 2022–2027), $480,000 (MITACS Accelerate, 2022–2025), and $210,000 (Canadian Statistical Sciences Institute, 2022–2025) Students and Postdocs : She mentors numerous PhD , MSc , and Postdoctoral Fellows , including Junxi Zhang (2023–Present), Enze Shi (2022–Present), and former advisees like Wenxing Guo (now Lecturer at University of Essex) and Yafei Wang (Assistant Professor at University of Alberta).

Spiridon Ivanov Penevمشاهده پروفایل

استاد

• Wavelet Methods in nonparametric curve estimation
• Edgeworth expansions and Saddlepoint Approximation Methods
• Structural Equation Models
• +۸ مورد دیگر

Professor Spiridon Ivanov Penev is a leading academic in the School of Mathematics and Statistics at the University of New South Wales. He holds a PhD in Mathematical Statistics from Humboldt University (Berlin, Germany) and has been affiliated with UNSW since 1992, progressing from Lecturer to Professor in 2019. His research spans wavelet methods, saddlepoint approximations, structural equation models, and stochastic risk analysis. Education: PhD in Mathematical Statistics, Humboldt University Current Affiliation: Department of Statistics, School of Mathematics and Statistics, UNSW His work focuses on advanced nonparametric techniques, including wavelet-based signal recovery with adaptive sampling rates, and robust inference in structural equation models. He has developed bias-corrected reliability measures for psychometric applications and contributed to stochastic optimization problems in finance and engineering. Recent publications highlight his expertise in semiparametric regression, robust portfolio optimization, and marine engineering applications using machine learning. Key trends include the use of Bregman divergence for shape-preserving estimation and Markov chain methods for climate model weighting. Scientific Awards: DAAD award Elected member of the International Statistical Institute (ISI) He has supervised numerous grants as Chief Investigator, including Australian Research Council projects and industry collaborations. Administrative roles include membership in the School of Mathematics and Statistics Executive Committee. Teaching duties span advanced statistical inference, multivariate analysis, and data science applications.

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
• +۳ مورد دیگر

Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

Jochen Garckeمشاهده پروفایل

استاد

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +۵ مورد دیگر

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

Pierre Duchesneمشاهده پروفایل

استاد

• Time Series Analysis
• Sampling Theory
• Multivariate Analysis
• +۲ مورد دیگر

Pierre Duchesne is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal . He serves as Professor-responsibility for the M.Sc. and Ph.D. in Statistics programs (2000-2004). His research focuses on applied statistics with emphasis on: Time Series Analysis (univariate and multivariate models, serial correlation testing, portmanteau statistics) Sampling Theory (robust estimation methods, calibration estimators) Multivariate Analysis (ARCH effects, vector autoregressive models, causality testing) Applications in Econometrics and Financial Econometrics His work combines theoretical development with practical implementation through: Wavelet-based diagnostic methods Simulation studies for model validation Software development (S-PLUS/SAS) for statistical analysis Collaboration with organizations like Statistics Canada and Canadian Journal of Statistics He has served as Associate Editor for journals including Computational Statistics & Data Analysis (CSDA) and Canadian Journal of Statistics (CJS/RCS) .

Jian Kangمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +۱۱ مورد دیگر

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

• Quality Inspection and Control
• Predictive Modeling
• Smart Manufacturing Systems
• +۳ مورد دیگر

Dr. Yu (Chelsea) Jin is an Assistant Professor in the Department of Industrial Engineering at the University at Buffalo, specializing in quality inspection, predictive modeling, and data analytics for advanced manufacturing systems. She holds a PhD in Industrial Engineering from the University of Arkansas, an ME from the University of Michigan, and dual BS degrees in Network Engineering and Finance from Jinan University. Her research focuses on integrating machine learning and physics-based models to optimize manufacturing processes, such as additive manufacturing, PCB assembly, and pharmaceutical distribution systems. She has developed frameworks like ReflowNet for reflow oven optimization and physics-informed neural networks for thermal profile prediction. Her work emphasizes both theoretical advancements and practical applications in smart manufacturing and healthcare logistics. Dr. Jin's recent publications highlight contributions to generative AI for knowledge retrieval, AGV system optimization, and multi-source transfer learning for pandemic modeling. She actively collaborates with industry partners to bridge academic research and real-world manufacturing challenges.