جستجوی استاد راهنمای دانشگاهی

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +۲ مورد دیگر

Syed Ejaz Ahmed is a distinguished Professor of Mathematics and Statistics at Brock University , with a career spanning multiple institutions including the University of Windsor, University of Regina, and University of Western Ontario. He serves as a Review Editor for Technometrics and holds editorial roles for several journals. PhD, Carleton University MSc, University of Guelph MSc in Statistics, University of Karachi BSc (Honors), University of Karachi His research focuses on big data analytics , statistical machine learning , and shrinkage estimation , with applications in healthcare, economics, and environmental science. He has organized international workshops on high-dimensional data analysis since 2011. Recent scientific contributions highlight advanced methods for high-dimensional regression, censored data analysis, and predictive modeling. His work has received international recognition through awards and fellowships. Fellow, American Statistical Association Fellow, Royal Statistical Society Elected Member, International Statistical Institute NSERC Discovery Grant (2017-2022) ISOSS Gold Medal A dedicated educator, he has supervised numerous PhD/MSc students and postdoctoral fellows. Currently, he leads the Centre for Business Analytics at Brock and collaborates with institutions worldwide through honorary professorships and visiting roles.

• Big Data Analytics
• Statistical Machine Learning
• Predictive Modeling
• +۶ مورد دیگر

Professor Selva Selvanathan is a Professor of Econometrics at Griffith University, affiliated with the Department of Accounting, Finance, and Economics at the Griffith Business School. He serves as Director of the Economics Policy Analysis Program (EPAP) and a member of the Griffith Asia Institute. His research focuses on applied econometrics, time series modeling, tourism and energy economics, and consumer demand analysis. He has authored/co-authored over 100 articles and multiple monographs, contributing to leading journals such as the Review of Economic Studies and Journal of Econometrics . Education: PhD in Economics (University of WA and Murdoch University, Australia), MSc (University of Bucharest, Romania), BSc (University of Jaffna, Sri Lanka). Research Interests: Professor Selvanathan’s work spans econometric methodologies, tourism economics, energy policy, and consumer behavior. His recent studies include modeling financial asset demand in Sri Lanka, analyzing alcohol consumption patterns in Australia, and exploring the gender dimensions of cleaner energy transitions. Awards and Honors: Pro-Vice Chancellor’s Research Excellence Award 2014 (Research Supervision) University of Western Australia Distinguished Teaching Award (1988) Griffith Awards for Excellence in Teaching 2005 – Innovation Across the Institution Grants and Collaborations: Leading projects funded by the Department of Foreign Affairs and Trade, Australian Research Council, and Griffith University. Notable grants include policy capacity-building initiatives in Vietnam and Sri Lanka, and studies on alcohol control measures. Advising and Mentorship: Supervised over 20 doctoral students, including works on foreign aid impacts, renewable energy transitions, and rural livelihoods. Current research includes market integration dynamics and energy policy effectiveness. Labs/Teams: Active in Griffith Asia Institute and EPAP, fostering interdisciplinary research on Asian economies and policy analysis.

• Applied Econometrics
• Time Series Modelling
• Tourism Economics
• +۶ مورد دیگر

Ljiljana Trajkovic is a Professor in the Department of Engineering Science at Simon Fraser University's Faculty of Applied Sciences. She holds a Ph.D. from the University of California, Los Angeles (1986), M.Sc. from Syracuse University (1979), and Dipl.Ing. from the University of Pristina (1974). Her research focuses on communication networks, nonlinear circuits, and machine learning applications for network security. She actively contributes to IEEE initiatives, including roles as conference committee chair and editorial board member. Education highlights include a strong foundation in electrical engineering and advanced studies in circuit theory and systems science. Her work bridges theoretical analysis with practical applications, such as anomaly detection in communication networks using machine learning. She teaches courses like ENSC 220 D100 Electric Circuits I, integrating research insights into education. Research interests emphasize network security, traffic analysis, and distributed systems. Recent articles explore BGP anomaly classification, ransomware detection, and virtual network embedding. She collaborates on tools like VNE-Sim and Anonym for network analysis. Awards and recognitions are highlighted through her leadership roles in IEEE and academic contributions. Advising and grants involve mentoring graduate students in cybersecurity and networking projects. She leads research teams exploring complex networks and their applications in autonomous systems. Her lab focuses on interdisciplinary projects merging electronics engineering with AI-driven network solutions.

• Communication Networks
• Nonlinear Circuits and Systems
• Electronics Engineering
• +۳ مورد دیگر

Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.

• Data Science
• Machine Learning
• Statistical Analysis
• +۳ مورد دیگر

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +۸ مورد دیگر

Patrick J. Curran is a Professor in the Department of Psychology at the University of North Carolina, affiliated with the L.L. Thurstone Psychometric Laboratory and the Quantitude initiative. His research focuses on quantitative methodologies, particularly longitudinal data analysis and structural equation modeling, applied to developmental psychopathology and adolescent substance use. He collaborates on statistical education through YouTube tutorials, a blog (Help Desk), and workshops, alongside a podcast with Greg Hancock. His work bridges methodological innovation with substantive research in psychology and public health. Key contributions include advancing latent curve models with structured residuals, integrative data analysis techniques, and the dissemination of statistical tools. His research spans disciplines such as developmental psychology, clinical psychology, and epidemiology, addressing topics like alcohol use trajectories, chronic depression mechanisms, and pandemic-related disparities in healthcare workers’ mental health. Lab Affiliations: L.L. Thurstone Psychometric Lab, Quantitude Collaborations: Dan Bauer (statistical education), Greg Hancock (podcast), and interdisciplinary teams in public health and clinical research His publications emphasize methodological rigor and practical application, often addressing gaps in longitudinal data analysis and measurement equivalence. Notable work includes the latent curve model with structured residuals (LCM-SR) and integrative data harmonization strategies for prevention science.

• Quantitative Methods
• Longitudinal Data Analysis
• Developmental Psychopathology
• +۳ مورد دیگر

Joaquin Vanschoren is an Associate Professor of Machine Learning at Eindhoven University of Technology (TU/e), affiliated with the Faculty of Mathematics and Computer Science. He leads the Automated Machine Learning group and serves as Education Director for the Data Science program. His research focuses on democratizing AI, algorithm selection, and open science platforms like OpenML. He has received awards including the Dutch Data Prize and Amazon Research Award. Education: PhD in Engineering (KU Leuven, Belgium), MSc in Computer Science (KU Leuven). Research visits included IBM, Amazon Research, and universities globally. Research Interests: Machine Learning, Automated ML, Meta-learning, AI Safety, Data-centric AI. He co-founded OpenML and chairs MLCommons' AI Safety working group. Key Projects: NeurIPS Datasets and Benchmarks track, MLCommons initiatives, OpenML platform. Supervised 78 research works and authored 200+ papers. Awards: Dutch Data Prize (2016), Amazon Research Award (2019), Microsoft Azure Research Awards (2016–2017). Labs/Teams: OpenML open source team, MLCommons collaborations, Automated Machine Learning group at TU/e.

• Machine Learning
• Automated Machine Learning
• Algorithm Selection
• +۴ مورد دیگر

Mahmoud Zarepour is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds an MSc from Shiraz University (Iran) and a PhD from the University of Toronto. His research focuses on advanced statistical methodologies including Time Series Analysis, Nonparametric Bayesian Inference, and Analysis of Random Variables with Infinite Variance. He has supervised numerous graduate students and postdoctoral researchers, including Nada Habli, Reyhaneh Hosseini, and Sicheng Huang, among others. Education: MSc (Shiraz University, Iran) PhD (University of Toronto) Dr. Zarepour's research interests emphasize Bayesian nonparametric techniques, stochastic processes, and robust statistical methods. His work bridges theoretical developments with practical applications in areas such as change point detection, multivariate analysis, and resampling schemes. Notable contributions include advancements in Dirichlet process-based methods, bootstrap techniques for complex distributions, and asymptotic theory for unstable time series. His publications span over two decades, with recent works addressing Bayesian bootstrapping, quasi-Bayesian change point detection, and nonparametric inference for spherically symmetric distributions. His research group is part of the Statistics and Probability cluster at the University of Ottawa. Dr. Zarepour has advised multiple students and contributed to collaborative projects in statistical theory and methodology. His work often explores the intersection of probability theory and applied statistics, with an emphasis on rigorous mathematical foundations.

• Time Series Analysis
• Nonparametric Bayesian Inference
• Analysis of Random Variables with Infinite Variance
• +۳ مورد دیگر

Yves Marc Räth is a Ph.D. researcher at the ETH Zürich within the Department of Civil, Environmental, and Geomatic Engineering (D-BAUG). His work intersects urban morphology, historical settlement analysis, and network modeling. Education: MSc in Spatial Development and Infrastructure Systems (2018-2020), BSc in Geography (2015-2018) at University of Zurich Research focuses on urbanization patterns , settlement network evolution , and geospatial time series analysis . Key projects include the EMPHASES study on socio-ecological systems and HistoRiCH research on historical river landscapes. Recent publications analyze settlement archetypes through ResearchGate projects, with a 2025 Cities journal article identifying five distinct urban development pathways on the Swiss Plateau since 1899. His 2023 Scientific Reports study examines morphological homogeneity in small settlements near urban centers. Current affiliations include: Ph.D. Researcher at Institute for Spatial and Landscape Development Visiting Scholar at Urban Systems Lab, New York City (2024) Contributor to Swiss transport planning at INFRAS and EBP Technical expertise spans: Historical map digitization Building footprint analysis Commuter flow modeling Predictive urban archetypes

• Urban Morphology
• Settlement Development
• Geospatial Analysis
• +۳ مورد دیگر

Xiajun Jiang is an Assistant Professor in the Department of Computer Science at the University of Memphis, joining in Fall 2024. He holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2024), an M.S. in Computer Science from the University of Southern California (2018), and a B.S. in Electrical Engineering and Automation from Zhejiang University (2016). His research focuses on adaptive AI computing, physics-informed deep learning, and their applications in healthcare, particularly in medical imaging and cardiac simulation. Key contributions include hybrid neural state-space modeling for electrocardiographic imaging and physics-informed frameworks for bi-ventricular electrophysiological simulations. Education: PhD, Rochester Institute of Technology, 2024 M.S., University of Southern California, 2018 B.S., Zhejiang University, 2016 Research Interests: Machine learning for healthcare Adaptive computing in AI models Physics-informed deep learning His work bridges machine learning and biomedical engineering, with applications in cardiac imaging and electrophysiology. Recent articles highlight advancements in hybrid models for ECGI and meta-learning approaches for personalized cardiac simulations. He has reviewed for top conferences like ICLR, NeurIPS, and MICCAI, and contributed to projects like the Computational Biomedical Lab (CBL).

• Adaptive AI Computing
• Physics-Informed Deep Learning
• AI for Healthcare
• +۳ مورد دیگر

Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.

• Mobile & Wearable Computing
• Activity Recognition
• Smart Health
• +۳ مورد دیگر

Hui Zhang is a Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on data-driven networking systems, video streaming optimization, and network control frameworks. He has contributed to innovations in adaptive resource allocation, real-time analytics, and sustainable strategies for resource utilization. Key research themes include time-state analytics, network anomaly detection, and integrating machine learning for enhanced performance. His work addresses challenges in content delivery networks (CDNs), peer-to-peer systems, and environmental applications like waste management. Recent publications (2021–2024) highlight advancements in neural network-based prediction, timeline frameworks, and sustainable material science innovations. No scientific awards are mentioned in the provided text. His research emphasizes practical solutions for improving video quality of experience (QoE), network efficiency, and cross-disciplinary applications.

• Computer Science
• Networking
• Data-Driven Systems
• +۳ مورد دیگر

Anna Pidgeon is Professor in the Department of Forest and Wildlife Ecology at University of Wisconsin-Madison. Her research addresses habitat requirements of vertebrate species, particularly birds, within human-modified landscapes. Pidgeon investigates conservation challenges through habitat suitability modeling, landscape connectivity analysis, and remote sensing applications. Research integrates field surveys with satellite-derived metrics like Dynamic Habitat Indices to predict biodiversity patterns across scales. Current studies examine how climate change and land-use alter habitat resilience, with projects spanning Wisconsin forests, Patagonian ecosystems, and tropical regions. Methodological innovations include texture analysis of satellite imagery to quantify habitat heterogeneity. Educational background includes PhD in Wildlife Ecology from UW-Madison (2000), MS from Central Washington University (1995), and BS degrees from University of Minnesota in Life Science Education (1989) and Wildlife Management (1983).

• Wildlife Habitat Ecology
• Conservation Biology
• Avian Community Dynamics
• +۲ مورد دیگر

Eoin Delaney is a Lecturer in the School of Computer Science and Statistics at Trinity College Dublin (as of Spring 2025). Previously, he was a Postdoctoral Researcher at the Oxford Internet Institute (OII), University of Oxford (October 2023 – March 2025), focusing on Trustworthiness Auditing in AI. His research spans Explainable AI, algorithmic fairness, computer vision, and sustainable AI applications. He holds a PhD from University College Dublin (UCD), advised by Dr. Derek Greene and Prof. Mark T. Keane, where he developed counterfactual explanations for time series and image data. His work bridges technical AI research with societal impact, including collaborations with Accenture Labs and the VistaMilk SFI Research Centre. Key achievements include developing the OxonFair toolkit (accepted to NeurIPS 2024), winning the 2022 AI Ireland Award for Best Application of AI in a Student Project, and authoring papers in top venues like NeurIPS, AIJ, and IJCAI. He is actively involved in academic service, serving on program committees for FAccT 2025 and AAAI workshops. Eoin has supervised students at the University of Oxford and organizes workshops on AI accountability, such as the Auditing Accountability in Trustworthy Artificial Intelligence in personalized medicine. His teaching and outreach efforts include promoting STEM education through initiatives like an educational website for children, emphasizing cryptography and probability. He continues to explore fairness in AI systems, user-centric explanations, and scalable machine learning frameworks (PyTorch, TensorFlow).

• Explainable AI
• Trustworthy AI
• Computer Vision
• +۶ مورد دیگر