جستجوی استاد راهنمای دانشگاهی

J. Derek Tuckerمشاهده پروفایل

دانشگاهی

• Functional Data Analysis
• Geometric Methods
• Statistical Image Understanding
• +۴ مورد دیگر

J. Derek Tucker is an Adjunct Clinical Associate Professor in the Department of Statistics at the University of Illinois and a Distinguished Member of the Technical Staff at Sandia National Laboratories. He holds a Ph.D. in Statistics from Florida State University (2014) and degrees in Electrical Engineering from Colorado State University (B.S. 2007, M.S. 2009). His research focuses on functional data analysis, geometric methods, unaligned data modeling, statistical image understanding, and Bayesian model calibration. At Illinois, he teaches a topics course on functional data analysis. Education: Ph.D. in Statistics, Florida State University (2014) M.S. in Electrical Engineering, Colorado State University (2009) B.S. in Electrical Engineering, Colorado State University (2007) Research Interests: Functional data analysis with geometric methods Statistical image understanding Bayesian model calibration Point processes and spatio-temporal modeling Uncertainty quantification His recent work includes advancements in elastic distance metrics for anomaly detection and functional data registration. He has received the Director of National Intelligence Team Award for contributions to SLiCE team projects. Publications span statistical methodology, climate impact analysis, and computational statistics. Awards: Director of National Intelligence Team Award (2020) Grants and collaborations involve Sandia National Labs and interdisciplinary projects in climate science, materials science, and healthcare analytics. He actively contributes to R packages like 'fdasrvf' for elastic functional data analysis.

Oksana Chkrebtiiمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Uncertainty Quantification
• Functional Data Analysis
• +۴ مورد دیگر

Oksana Chkrebtii is an Associate Professor in the Department of Statistics at The Ohio State University's College of Arts and Sciences, with courtesy appointments in Materials Science Engineering and affiliation with the HAMMER ERC. She holds a PhD from Simon Fraser University (2014) and specializes in Bayesian statistics, uncertainty quantification, and spatio-temporal modeling. Her research addresses inferential challenges in systems governed by differential equations, with applications ranging from invasive species dynamics to epidemic modeling. Her primary research areas include: Bayesian uncertainty quantification for differential equation models Functional data analysis under sparse sampling regimes Spatio-temporal statistics for environmental and biological systems Probabilistic numerical methods for inverse problems Her publication portfolio demonstrates consistent focus on developing Bayesian methodologies for complex dynamical systems, with recent emphasis on likelihood-free inference, functional data registration, and applications in climate science and epidemiology. Articles frequently integrate computational statistics with domain-specific challenges in oceanography, materials science, and public health. Dr. Chkrebtii actively mentors graduate students, including recent advisees Jake Roberts (MS) and Rui Zhang (PhD), and collaborates with NASA and NSF-funded projects. She leads the Digital Twins research group at Ohio State and serves as Associate Editor for the SIAM/ASA Journal of Uncertainty Quantification.

Jie (Jack) Yangمشاهده پروفایل

مدرس

• Natural Language Processing
• Large Language Models
• Multimodal NLP
• +۴ مورد دیگر

Dr. Jie (Jack) Yang is a Lecturer in Database Systems & Big Data at the School of Computing and Information Technology, University of Wollongong. He holds a PhD from the same institution. His research focuses on Natural Language Processing (NLP), including Large Language Models (LLMs), Multimodal NLP, Machine Reading Comprehension, and Knowledge Graph applications across domains like Education, Healthcare, and Finance. He is affiliated with the Association for Computational Linguistics (ACL) and IEEE. Research Interests Dr. Yang's work emphasizes theoretical advancements and practical solutions in NLP. His recent projects include developing robust document retrieval systems, adversarial detection techniques, and enhancing AI models' robustness through masking and contrastive learning. He collaborates on applications like automated postural assessment using CNNs and AI-driven educational tools. Grants & Projects He leads projects such as AMKD.AI (Knowledge Graph toolkit), Next-Gen AIOT (interactive kiosks), and AI4U (AI competency initiatives). His funded research spans areas like LLM-based tourism data management, cybersecurity for AI models, and healthcare NLP applications. Teaching & Supervision Dr. Yang coordinates courses like Advanced Programming (CSCI851/CSCI251) and supervises PhD/Master’s students in topics like LLM explainability, skeleton-based action recognition, and adversarial learning. He emphasizes practical industry alignment in education and research. Labs & Teams He contributes to interdisciplinary teams advancing AI in education, healthcare, and industry collaboration through initiatives like the Telstra-UOW AIOT Hub and UOW’s Pretrained Language Models (PLMs) for medical document processing.

Bernard De Baetsمشاهده پروفایل

استاد

• Fuzzy Set Theory
• Machine Learning
• Cellular Automata
• +۳ مورد دیگر

Bernard De Baets is a Senior Full Professor and Head of the Department of Data Analysis and Mathematical Modelling at the Faculty of Bioscience Engineering, Ghent University. He leads the Knowledge-based Systems research unit KERMIT. With interdisciplinary expertise in mathematics, computer science, and knowledge engineering, his research bridges fundamental and applied domains in biological sciences. His research integrates three core threads: Knowledge-based modelling for intelligent systems Predictive modelling using statistical and machine learning approaches Spatio-temporal modelling for dynamic systems analysis Specific domains include Fuzzy Set Theory, Cellular Automata, and hybrid AI methods with applications in environmental science, agriculture, and computational biology. He has authored over 550 peer-reviewed journal papers (accumulating ~25,000 citations) and earned multiple best paper awards. As Editor-in-Chief of Fuzzy Sets and Systems and editorial board member for several journals, he significantly contributes to the scientific community. Major honors include: Honorary Professor at Budapest Tech Doctor Honoris Causa (University of Turku) Professor Extraordinarius (University of South Africa) Fellow of the International Fuzzy Systems Association EUSFLAT Scientific Excellence Award

Wynne Hsuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Healthcare AI
• +۵ مورد دیگر

Wynne Hsu serves as Provost's Chair Professor in the Department of Computer Science at NUS School of Computing and Director of the Institute of Data Science (IDS), driving Singapore's AI research agenda through strategic academic leadership and healthcare innovation. Her academic foundation includes: B.Sc. in Computer Science, National University of Singapore M.Sc. in Computer Science, Purdue University, U.S.A. Ph.D. in Electrical & Computer Engineering, Purdue University, U.S.A. (1994) Professor Hsu's research integrates Artificial Intelligence and Machine Learning with critical healthcare applications, specializing in Retinal Image Analysis for diabetic complications, Social Media Analytics for misinformation containment, and Spatio-temporal Data Mining for dynamic system modeling. Her patient similarity analytics framework has enabled risk communication tools deployed in Singapore's national diabetes program, demonstrating translational impact from algorithmic innovation to clinical practice. Recent work emphasizes multimodal AI systems that bridge ophthalmology with systemic disease prediction. Analysis of her 2025 publications reveals three converging trajectories: (1) Healthcare AI extending from retinal diagnostics to kidney disease and dementia prediction using deep learning, (2) Temporal reasoning frameworks for claim verification addressing misinformation through timeline analysis, and (3) Privacy-preserving multimodal LLMs tackling inadvertent data memorization. These strands reflect her consistent focus on robust, clinically actionable AI systems. Her exceptional contributions are recognized by: President’s Technology Award, Singapore (2014) for national AI healthcare impact SIGKDD Test-of-Time Award (2014) for foundational social network influence research As IDS Director, Professor Hsu secures strategic grants for national AI screening programs while mentoring cross-disciplinary teams that combine computer vision expertise with clinical partnerships. Her leadership has established NUS as a hub for healthcare AI validation, particularly in multi-ethnic populations through collaborations with Singapore's National Healthcare Group. The Institute of Data Science under her direction operates as Singapore's central AI innovation engine, with current initiatives focused on multimodal disease progression modeling and temporal fact verification systems. Key projects integrate retinal vessel analysis with cardiovascular risk prediction and develop LLM-based tools for physician-patient communication in chronic disease management, maintaining strong alignment with national healthcare priorities.

• Bayesian Statistics
• Computational Statistics
• Probabilistic Machine Learning
• +۵ مورد دیگر

Professor Sumeetpal Singh holds the Tibra Foundation Chair in Mathematical Sciences at the University of Wollongong's Faculty of Engineering and Information Sciences, School of Mathematics and Applied Statistics (since 2023). Previously, he served as Professor of Engineering Statistics at the University of Cambridge (2007–2023) and as a Fellow and Director of Studies at Churchill College, Cambridge. His research focuses on Bayesian Statistics, Computational Statistics, Probabilistic Machine Learning, and Time-series analysis, with notable contributions to particle filtering, Monte Carlo methods, and statistical inference. He has supervised research on topics like 'American options and their application in political science'. His work has been recognized with awards such as the M. Barry Carlton award (2014). Singh is actively involved in editorial roles, including serving as Associate Editor for the Annals of Applied Probability and Senior Associate Editor for ACM Transactions on Probabilistic Machine Learning . Current research includes grants like the Air Force Office of Scientific Research-funded project on Bayesian Spatio-Temporal Analysis (2023–2025). Teaching includes modules like Statistical Modelling and Analysis of Time-series Data and Statistical Inference and Introduction to Model Building . His publications span computational statistics, signal processing, and machine learning, emphasizing methodological innovation and theoretical rigor.

Michael Greenspanمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Object Recognition
• +۴ مورد دیگر

Michael Greenspan is a Professor in the Department of Electrical and Computer Engineering at Queen's University, affiliated with the Ingenuity Labs Research Institute. His expertise spans computer vision, image processing, and robotics. He holds a PhD in Systems and Computer Engineering from Carleton University and has extensive industry experience, including leading the Computational Video Group at the National Research Council of Canada. His research focuses on object recognition, motion planning, and applied computational geometry, with applications in autonomous robotics and 3D scene analysis. He has published over 30 technical papers and 3 patents, and collaborates with industry on applied projects. Education BSc in Physics and Applied Mathematics (1986), University of Toronto BASc and MASc in Electrical Engineering (1989, 1991), University of Ottawa PhD in Systems and Computer Engineering (1991), Carleton University Research Interests Object recognition and pose determination using geometric probing and minimalist template matching Efficient motion planning algorithms for robotic systems Computational geometry solutions like TINN for nearest neighbor problems Applications in autonomous robotic capture, LiDAR data analysis, and collaborative robotics Dr. Greenspan’s work bridges theoretical computer vision with practical robotic systems, emphasizing real-time performance and industrial relevance. His lab, the Robotics and Computer Vision Laboratory, develops algorithms for 3D localization, sensor fusion, and autonomous navigation. He is active in professional organizations like IEEE and serves on the Research Management Committee of Precarn Associates. Key Contributions Developed TINN for nearest-neighbor problem optimization Pioneered efficient collision detection methods for robotic motion planning Advanced 3D point cloud registration techniques using virtual interest points Co-created the CycleCrash dataset for bicycle collision analysis

Chun-Hung Liuمشاهده پروفایل

دانشیار

• Machine Learning Theory
• Stochastic Control and Optimization
• Data Science
• +۴ مورد دیگر

Chun-Hung Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University (MSU). He previously held positions at the University of Michigan, National Chiao Tung University, and Qualcomm Inc. His research focuses on machine learning theory, stochastic control, data science, wireless communication, and cyber-physical systems security. Prof. Liu has received prestigious awards including the Air Force Research Lab Faculty Fellowship (2023), Taiwan’s Young Scholar Award (2015), and an IEEE Globecom Best Paper Award (2014). Education: Ph.D., Electrical and Computer Engineering, University of Texas at Austin M.S., Mechanical Engineering, MIT M.S., Electrical Engineering, National Taiwan University B.S., Mechanical Engineering, National Taiwan University Research Interests: Dr. Liu’s work spans theoretical and applied domains, including deep learning topology analysis, edge computing optimization, federated learning architectures, and underwater acoustic communication systems. He explores practical solutions for energy-efficient computing, cyber-physical system security, and next-generation wireless networks (e.g., 5G/6G, RIS-enabled systems). Publications: His recent work emphasizes interdisciplinary approaches, with articles addressing topics like RIS-assisted MIMO systems, federated learning over UAV networks, and solar-powered edge computing. These studies highlight innovations in both algorithm design and system-level performance evaluation. Awards: Air Force Research Lab Faculty Fellowship (2023) Taiwan’s Young Scholar Award (2015) IEEE Globecom Best Paper Award (2014) Advising & Grants: While specific grant details are not provided, his publications indicate sustained research activity supported by military and academic collaborations. His lab focuses on experimental platforms for federated learning and wireless mesh networks, as evidenced by a mini-PC-based testbed described in 2022 work. Labs/Teams: Leads a research group at MSU investigating edge computing, federated learning, and reconfigurable intelligent surface technologies. Collaborates with industry partners (e.g., Qualcomm) and international institutions on communication systems and AI integration.

Yu Luoمشاهده پروفایل

دانشیار

• Underwater Communications
• Wireless Communications
• Internet of Things
• +۴ مورد دیگر

Yu Luo serves as Associate Professor in Electrical and Computer Engineering at Mississippi State University's Bagley College of Engineering, focusing on cutting-edge wireless systems and sustainable network architectures. His work bridges theoretical innovation with experimental validation across underwater and terrestrial communication domains. His academic foundation includes: Ph.D. in Computer Science and Engineering, University of Connecticut (2015) M.S. in Electrical and Information Engineering, Northwestern Polytechnical University, China (2012) B.S. in Electrical and Information Engineering, Northwestern Polytechnical University, China (2009) Research centers on Underwater Acoustic Communications with reconfigurable intelligent surfaces, Renewable Energy IoT systems using RF harvesting, and Cybersecurity for wireless networks. Recent work integrates Edge Computing and Machine Learning for real-time applications like structural health monitoring and PPE detection, emphasizing energy optimization in solar-powered edge systems and federated learning frameworks. Publication trends (2022-2025) reveal accelerated output in experimental underwater communications, with 40% of recent work dedicated to reconfigurable surfaces and energy harvesting. Key themes include practical implementation challenges, hardware-software co-design for edge AI, and security mechanisms for resource-constrained IoT devices, demonstrating consistent translation of theoretical concepts to field-deployable solutions.

• Precision Medicine
• Functional Data Analysis
• High-Dimensional Regression
• +۵ مورد دیگر

Xinyi Li is an Assistant Professor in the School of Mathematical and Statistical Sciences at Clemson University. She holds a Ph.D. in Statistics from Iowa State University, an M.S. in Statistics from the University of Georgia, and a B.S. in Mathematical Statistics from Beijing Normal University. Prior to joining Clemson, she was a postdoctoral fellow at SAMSI and UNC Chapel Hill. Her research focuses on precision medicine, functional data analysis, high-dimensional regression, spatio-temporal analysis, and reinforcement learning, with applications in statistical genetics, neuroimaging, and public health. She teaches courses in probability theory, data analysis, machine learning, and mathematical statistics. Dr. Li's recent publications demonstrate strong emphasis on machine learning applications in statistical modeling, with innovations in nonparametric regression, dynamic treatment regimes, and high-dimensional data analysis. Her work frequently appears in top-tier journals including the Journal of Machine Learning Research and Journal of the American Statistical Association. National Science Foundation DMS-2210658 (2022-2025) IMS New Researcher Travel Award (2019) ASA GA Chapter Best Student Poster Award (2017) 2nd Place in 2016 Data Mining Cup She maintains active collaborations through affiliations with the Center for Human Genetics and the Institute for Engaged Aging, and serves as an AI advocate for the Clemson-MUSC AI Hub.

• Categorical Data Analysis
• Group Testing
• Bayesian Estimation
• +۳ مورد دیگر

Christopher McMahan is a Full Professor at Clemson University's School of Mathematical and Statistical Sciences within the College of Science. His academic roles include serving as Associate Director for Graduate Studies and co-leading the Clemson-MUSC AI Hub. McMahan holds a Ph.D. in Statistics from the University of South Carolina (2012), M.S. in Mathematics from Western Kentucky University (2008), and B.S. in Mathematics from Austin Peay State University (2006). His research focuses on developing statistical methodologies for complex data problems, with interdisciplinary applications spanning epidemiology, agriculture, and biomedical sciences. Core research areas include Bayesian modeling, group testing designs, high-dimensional regression, and spatio-temporal analysis. Current projects involve genomic prediction in crop science, infectious disease modeling, and machine learning applications for public health surveillance. McMahan's publication record demonstrates consistent contributions to statistical theory and applied interdisciplinary research. Recent work exhibits strong emphasis on: Bayesian computational methods for large-scale problems Epidemiological modeling of COVID-19 transmission Genomic prediction of agricultural phenotypes Statistical learning approaches for public health data Biomedical applications of group testing designs Environmental risk assessment methodologies His scholarly recognition includes: Fellow of the American Statistical Association (2022) Multiple ASA Outstanding Statistical Application Awards ENAR Distinguished Student Paper Awards College of Science Dean's Associate Professor (2021-2023) McMahan maintains an active research program with substantial grant funding, including NIH R01 awards totaling over $2 million. He currently advises four postdoctoral scholars and has mentored eight doctoral graduates. His collaborative work involves interdisciplinary teams across computational biology, plant genomics, and public health epidemiology. Laboratory and team leadership includes directing graduate research through the Clemson-MUSC AI Hub and coordinating multi-institutional consortia like the NCAA-DoD CARE Consortium. Future research directions emphasize developing computationally efficient statistical methods for high-dimensional biomedical data and expanding applications in precision agriculture.

Whitney Huangمشاهده پروفایل

استادیار

• Statistics of Extremes
• Spatio-Temporal Statistics
• Environmental Applications
• +۴ مورد دیگر

Whitney Huang is an Assistant Professor in the School of Mathematical and Statistical Sciences at Clemson University. She holds a PhD in Statistics from Purdue University (2017) and has held postdoctoral positions at the University of Victoria and SAMSI. Her research focuses on extreme value theory, spatio-temporal statistics, and environmental applications. She advises multiple PhD and MS students, with active projects in climate extremes, risk quantification, and physiological data analysis. Education: PhD (Statistics, Purdue, 2017); MS (Statistics, University of Akron, 2009); BS (Mechanical Engineering, National Cheng Kung University, 2006). Research interests include statistical methods for climate extremes, wind speed modeling, and high-frequency biomedical signals. Recent work emphasizes joint modeling of environmental variables and uncertainty quantification. She teaches advanced statistical courses including Time Series Analysis and Multivariate Analysis. Notable awards include the 2022 Clemson Faculty Teaching Award and recognition at national climate statistics conferences. Active service roles include editorial work for *Data Science in Science* and leadership in environmental statistics working groups at Clemson.

André Skupinمشاهده پروفایل

استاد

• Geographic Information Systems (GIS)
• Geovisualization
• Data Mining
• +۷ مورد دیگر

André Skupin is a Professor of Geography at San Diego State University (SDSU), affiliated with the College of Arts and Letters and the Department of Geography. He is currently on leave for the academic year 2024-2025. His research focuses on geovisualization, data mining, and geographic information systems (GIS), with applications in environmental science, digital humanities, and health informatics. Skupin holds a Ph.D. from SUNY Buffalo (1998) and a Dipl.-Ing. from the Technical University of Dresden, Germany (1992). Skupin teaches advanced GIS courses such as GEOG 581: Cartographic Design and GEOG 780: Seminar in Geovisualization and Geocomputation . His research areas include visual data mining, natural language processing, social network analysis, and semantic web technologies. He has contributed to projects like the Nokia MDC Atlas and SOMViz , emphasizing the integration of machine learning and geospatial analytics. Skupin’s work bridges traditional cartographic methods with modern data science, addressing challenges in knowledge representation, spatio-temporal modeling, and emergency management. His recent publications (2020–2024) highlight innovations in wildfire recovery visualization, crop phenology modeling, and medical data analytics.

Dane Taylorمشاهده پروفایل

استادیار

• Networks
• Data Science
• Complex Systems
• +۴ مورد دیگر

Dane Taylor is an Assistant Professor at the University of Wyoming's School of Computing and Department of Mathematics and Statistics, with an adjunct appointment in Electrical Engineering and Computer Science. He joined UW in 2023 after serving as assistant professor at University at Buffalo (2017-2023). His research focuses on network science, complex systems, and data science methodologies. Education includes: Ph.D. Applied Mathematics, University of Colorado Boulder (2013) B.S. Electrical Engineering (bioengineering), University of Wyoming (2008) B.S. Physics, University of Wyoming (2008) Research interests span: network-based modeling, spectral theory, random matrix theory, neurocomputation, and self-organization in complex systems. His work develops mathematical frameworks for temporal/multilayer networks and simplicial complexes. Publication trends show consistent focus on network dynamics, topological data analysis, and applications in neuroscience, epidemiology, and social systems. Recent work explores machine learning integration with network theory and pandemic-related human mobility patterns. Awards and research support: NSF Algorithms for Threat Detection (ATD) grant NSF Mathematical Biology grant Nakatani Foundation (2020-2021) Simons Foundation (2018-2024) Postdoctoral Award for Research Excellence, UNC (2015) Teaching Award, UNC/HHMI (2014) ARCS Foundation Fellowship (2009-2012) NASA Space Grant Fellowship (2006) Organizational leadership includes: 2022 Northeast Regional Conference on Complex Systems, 2020 SIAM Workshop on Network Science, and multiple minisymposia for major conferences. Outside academia, enjoys hiking, snowboarding, and mountain biking.

Derek Schneiderمشاهده پروفایل

پژوهشگر

• Precision Agriculture
• Remote Sensing
• Sensor Networks
• +۳ مورد دیگر

Derek Schneider is a Senior Technical Officer in Precision Agriculture at the School of Science and Technology , University of New England (UNE). He coordinates fieldwork, manages sensor data, and supports PhD students in agricultural research. Qualifications: BRurSc (Hons) Research Interests: Precision agriculture devices (EMI sensors, vegetation sensors, livestock tracking), spatio-temporal pasture monitoring, and sensor network implementation Publications focus on precision agriculture applications in livestock behavior, pasture biomass estimation, and sensor data processing. Key areas include GPS tracking of animals, remote sensing for crop analysis, and data federation frameworks. Memberships: Institute of Electrical and Electronics Engineers (IEEE) Labs: Precision Agriculture Research Group (PARG), UNE SMART Farm