جستجوی استاد راهنمای دانشگاهی

Jiayi Tongمشاهده پروفایل

استادیار

• Biostatistics
• Real-World Data Analysis
• Meta-Analysis
• +۶ مورد دیگر

Jiayi (Jessie) Tong, PhD is an Assistant Professor in the Department of Biostatistics at Johns Hopkins University, with joint appointments in the Bloomberg School of Public Health and School of Medicine. She received her PhD from the University of Pennsylvania in 2024 and has rapidly established herself as a leading researcher in biostatistical methods for real-world data analysis. Dr. Tong's research program focuses on clinical evidence generation and evidence synthesis with real-world data (RWD). Her work spans three main areas: clinical evidence generation using data from distributed research networks, surrogate-assisted semi-supervised learning methods, and systematic reviews and meta-analyses. She has developed innovative statistical methodologies for analyzing electronic health records across multiple institutions while preserving patient privacy through distributed computing approaches. Her research has significant implications for improving evidence generation in healthcare, particularly for rare conditions and emerging health threats where traditional clinical trials may be impractical. Analysis of Dr. Tong's publication record reveals a strong emphasis on methodological innovation in biostatistics, particularly in the areas of federated learning, meta-analysis, and electronic health record analysis. Her work frequently addresses challenges in multi-site collaborative studies, developing one-shot algorithms that enable analysis without sharing patient-level data. Much of her recent research has focused on applications to SARS-CoV-2 infection and its sequelae, demonstrating the practical utility of her methodological contributions to pressing public health challenges. Dr. Tong has demonstrated exceptional productivity since completing her PhD in 2024, with numerous high-impact publications in top biostatistics and medical journals. Her work has been recognized through mentions on various platforms including Mendeley readership and blog coverage. As a new faculty member, Dr. Tong is actively building her research program and mentoring the next generation of biostatisticians. Her expertise in distributed analysis of healthcare data positions her at the forefront of methodological developments needed to address contemporary challenges in evidence generation.

Brian Ziebartمشاهده پروفایل

استاد

• Machine Learning
• Robotics
• Assistive Technologies
• +۳ مورد دیگر

Brian Ziebart is a Professor in the Department of Computer Science at the University of Illinois at Chicago. He earned his Ph.D. in Machine Learning from Carnegie Mellon University in 2010. Research Interests: Machine Learning, Robotics, Assistive Technologies, Human-Computer Interaction, Adversarial Prediction, Inverse Optimal Control, Structured Prediction. Key Grants: NSF CAREER (RI)-1652530, NSF EAGER (SCH)-1650900, NSF IIS-1526379, NSF III-1514126, Future of Life Institute grant, NSF NRI-1227495. Notable Awards: Best Paper Runner-Up (ECCV, 2012), Best Paper Award (ICML, 2011), CMU School of Computer Science Dissertation Honorable Mention (2011). Teaching & Leadership: Senior Lecturer at CMU, actively involved in mentoring students and leading research teams.

Vedhus Hoskereمشاهده پروفایل

استادیار

• Image Analysis
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston. His research focuses on interdisciplinary areas at the intersection of civil engineering, computer science, and robotics, emphasizing automated infrastructure inspection, machine learning, and AI-driven solutions for structural health monitoring. He holds the Liu Huixian Earthquake Engineering Scholarship (2018) for his work on automated post-earthquake building inspections. Key research areas include image analysis, machine learning, robotics, scientific computing, and visualization. His work integrates computer vision with civil infrastructure challenges, such as flood modeling, structural damage assessment, and autonomous inspection systems using drones and robotics. Notable projects include developing digital twin frameworks for bridges, synthetic environment testing for inspection algorithms, and semi-supervised learning for disaster damage analysis. His recent publications highlight advancements in transformer networks for instance segmentation, physics-informed generative models for damage assessment, and AI-driven hurricane resilience strategies. Hoskere collaborates on platforms like InstaDam for automated damage segmentation and explores Bayesian neural networks for quantifying uncertainties in infrastructure monitoring. Key Awards: Liu Huixian Earthquake Engineering Scholarship (2018) Advising & Labs: While no specific student names or grant details are provided, his research group likely focuses on robotics, computer vision, and AI applications in civil engineering. He contributes to open-source tools like the InstaDam platform and collaborates with institutions like the University of Illinois and USACE.

Qin Luمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Communications
• +۳ مورد دیگر

Qin Lu is an Assistant Professor at the University of Georgia's School of Electrical & Computer Engineering. Her research focuses on robust and adaptive Bayesian methods for decision-making using sequential/networked data, including online Gaussian processes, Bayesian optimization, reinforcement learning, and spatio-temporal inference over graphs. Applications span autonomous systems and mobile edge computing. Education details are not explicitly listed in the provided text. Her work emphasizes scalability and uncertainty quantification, with contributions to edge computing resource management, active learning strategies, and multi-agent systems in IoT. Recent publications highlight advancements in Gaussian process dynamical modeling, adaptive Bayesian optimization, and ensemble learning techniques for online systems. No scientific awards are mentioned in the provided materials. Advising and grants details are not provided, though her research indicates collaborations in autonomous systems, underwater acoustics, and sensor networks. She is affiliated with the Boyd GSRC building and reachable at Qin.Lu@uga.edu.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۳ مورد دیگر

Professor Kevin Swingler is a Professor and Head of the Division of Computing Science and Mathematics at the University of Stirling. His research focuses on Artificial Intelligence (AI), particularly in data science, machine learning, and computer vision, with a strong emphasis on healthcare applications. He leads the AISLA project , developing assistive technologies for visually impaired individuals using AI tools like computer vision and natural language understanding. His work addresses challenges in AI innovation while aiming to enhance independence for people with sight loss. Before academia, he ran a software company specializing in neural network-based solutions for banking, insurance, and marketing. He champions innovative teaching methods, including graduate apprenticeships, online degrees, and international programs. His research extends to applying AI in livestock disease surveillance, social media analysis for farming communities, and digital health tools for elder care. He also runs a university spin-out company providing data collection and analytics services. His recent publications span AI-driven health monitoring systems, neuro-symbolic models for small datasets, and haptic interfaces for accessibility. Collaborations with charities and health organizations highlight his commitment to translational research addressing societal challenges.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Chenyang Zhao is an Assistant Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. His research spans multiple domains at the intersection of computer vision, machine learning, and engineering applications. Dr. Zhao's primary research interests include: Computer Vision and Deep Learning Robotics and Autonomous Systems Medical Imaging and Healthcare Applications Hardware Acceleration for AI Precision Measurement and Instrumentation His recent work demonstrates a strong focus on practical applications of AI across diverse domains, from medical imaging to infrastructure inspection. Dr. Zhao has developed innovative approaches in areas such as sewer inspection systems using evidential deep learning, RGB-D semantic SLAM for robotics, and lightweight computing-in-memory architectures for edge AI applications. His research shows a consistent pattern of addressing real-world engineering challenges with cutting-edge machine learning techniques, particularly emphasizing trustworthiness, uncertainty quantification, and practical implementation constraints. Dr. Zhao's laboratory focuses on developing trustworthy AI systems with applications in: Infrastructure monitoring and maintenance Medical diagnostics and imaging Autonomous robotic systems Energy-efficient AI hardware

Aobo Liمشاهده پروفایل

استادیار

• Particle Physics
• Neutrino Detection
• Machine Learning Applications
• +۲ مورد دیگر

Aobo Li is an Assistant Professor in the Department of Physics at the University of California, San Diego. His research bridges particle physics and machine learning, with a focus on neutrino physics and rare-event detection. He contributes to major international collaborations including LEGEND and KamLAND-Zen, developing advanced instrumentation and AI-driven data analysis techniques for fundamental physics discoveries. Dr. Li's research interests center on experimental particle physics, particularly neutrinoless double beta decay, neutrino oscillations, and dark matter detection. His work employs machine learning for data cleaning, detector optimization, and real-time analysis in large-scale experiments. His publications demonstrate a strong focus on improving sensitivity in rare-event physics through innovative detector technologies and computational methods. Recent articles highlight advancements in background reduction, surrogate modeling for detector design, and hardware-AI co-processing systems. Dr. Li leads experimental physics initiatives and collaborates on next-generation observatories like XLZD and DARWIN, focusing on liquid xenon detector technologies for dark matter and neutrino physics.

Abhishek Chakraborttyمشاهده پروفایل

استادیار

• Concentration inequalities
• Empirical processes
• Debiasing methods
• +۴ مورد دیگر

Abhishek Chakrabortty is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on advanced statistical methodologies, including concentration inequalities, empirical processes, and debiasing techniques. He specializes in semi-supervised learning, causal inference, and high-dimensional statistical analysis with applications in biomedical informatics and electronic medical records. Key research interests include developing robust frameworks for handling missing data, model misspecification, and non-parametric regression. His work emphasizes practical applications such as phenome-wide association studies and improving treatment effect estimation in complex datasets. His recent publications highlight contributions to semi-supervised inference methods, doubly robust causal estimation, and moving beyond classical sub-Gaussian assumptions in high-dimensional problems. This reflects a strong focus on bridging theoretical advancements with real-world data challenges. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research areas suggest active involvement in mentoring graduate researchers.