جستجوی استاد راهنمای دانشگاهی

Xiaotong T Shenمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Statistical Inference
• +۱۲ مورد دیگر

Xiaotong T Shen is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. He is actively involved in research and currently accepting PhD students. His work is supported by major grants from the National Science Foundation (NSF) and the National Institutes of Health (NIH), including projects on gene regulatory networks, Alzheimer’s disease genetics, and generative learning on unstructured data. Research Interests: Machine Learning and Deep Learning Statistical Inference and Modeling Biomedical Applications in Genomics and Neurodegenerative Diseases Natural Language Processing and Video Interpretation Generative Models and Synthetic Data Causal Inference and Graphical Models His recent publications reflect a strong trend in integrating deep learning with statistical rigor for causal and biomedical discovery, particularly in high-dimensional settings. Articles span applications in hepatology, transcriptomics, manufacturing, and AI-driven data generation, demonstrating interdisciplinary impact. Scientific Projects and Grants: PI, NSF: Generative Learning on Unstructured Data (2020–2024) PI, NIH: Estimation and Inference in Gene Regulatory Networks (2017–2024) Co-I, NIH: Causal Deep Learning for Alzheimer’s Disease Genetics (2021–2026) PI, NIH: Directed Acyclic Graphical Models for Biological Networks (2022–2026) He has advised numerous research projects and collaborated extensively with researchers in biostatistics and computer science. His work contributes to UN Sustainable Development Goals through data science for health and well-being. He leads a research group focused on foundational and applied statistical learning, with ongoing efforts in developing robust, interpretable models for complex scientific data.

Lee Sue Hanمشاهده پروفایل

مدرس ارشد

• Computer vision
• Artificial intelligence
• Machine Learning
• +۲ مورد دیگر

Dr. Lee Sue Han serves as Senior Lecturer and Head of Department for Information and Communication Technologies at Swinburne University of Technology Sarawak's Faculty of Engineering, Computing and Science. She joined Swinburne in April 2020 following post-doctoral research with Pl@ntNet (CIRAD and INRIA) in France. Her academic credentials include a BEng (Hons) in Electronic Engineering from Multimedia University (2011), MSc in Electrical and Electronic Engineering from Shinshu University, Japan (2014), and PhD in Computer Science from University of Malaya (2018). Dr. Lee's research bridges artificial intelligence and plant biology, focusing on computer vision, machine learning, and deep learning applications for plant disease identification, cross-domain classification, and environmental monitoring. Her work explores how deep learning models interpret botanical features and align with human perception in disease diagnosis, with significant contributions to automated plant health systems. Recent publications (2021-2022) demonstrate advanced work in multi-task learning for disease identification, global-scale plant recognition, and interpretable AI models. Her research consistently appears in top venues including Frontiers in Plant Science, IEEE Transactions, and Pattern Recognition. Her accolades include the 2022 Outstanding Student Feedback on Teaching award, 2022 Swinburne Research Success Award, and Best Paper Awards in 2020 and 2018. She actively secures research funding as PI for FRGS 2021 and co-PI for FRGS 2022 and SDEC 2021 projects. Dr. Lee supervises research in generative models, sign language production, drug discovery, animal re-identification, and plant disease modeling. She maintains international collaborations initiated during her France-based post-doctoral work and participates in global challenges like PlantCLEF.