جست‌وجوی استاد و پژوهشگر

Lili Yuمشاهده پروفایل

استاد

• Biostatistics
• Survival Analysis
• Categorical Data Analysis
• +۵ مورد دیگر

Lili Yu is the Karl E. Peace Endowed Chair of Biostatistics and Professor in the Department of Biostatistics, Epidemiology & Environmental Health Sciences at Georgia Southern University, where she has been faculty since 2007. Her academic appointments include serving as Principal Investigator for the Office of International Chinese Statistical Association (2007-2017) and as Co-Principal Investigator for current research projects including 'Hierarchical Bayes Regression Models for Geospatial Inquiries' at Georgia Southern University. Dr. Yu's educational background is highly interdisciplinary, with degrees spanning medicine and statistics: PhD in Biostatistics from Ohio State University (2007) MS in Statistics from Ohio State University (2004) MS in Neuroscience from Capital Medical University (2001) MD in Clinical Medicine from Tianjin Medical University (1995) Her research interests focus on biostatistical methodology development and application, particularly in survival analysis, categorical data analysis, and multivariate data analysis. Dr. Yu has made significant contributions to accelerated failure time models, Bayesian statistics, and spatiotemporal modeling. Her work often addresses important public health questions related to disease modeling, mortality risk factors, and health outcomes assessment. Analysis of her publication trends shows a consistent trajectory from methodological development in survival analysis to applied studies addressing significant health issues. Dr. Yu has received recognition for her scholarly contributions, with an h-index of 11 according to Scopus data, and 1,876 citations across her publications. Her work has appeared in numerous high-impact journals across biostatistics, epidemiology, and public health disciplines. Her research funding includes multiple successful grant awards, demonstrating her ability to secure competitive research support. Notable projects include 'Hierarchical Bayes Regression Models for Geospatial Inquiries' (2022-2023), 'Efficient statistical methods in AFT model for cancer data' (2009-2010), and leadership of the Office of International Chinese Statistical Association (2007-2017). Dr. Yu's collaborative network is extensive, with co-authors from multiple institutions across the United States and internationally. Her work bridges methodological statistics with practical applications in public health and medicine, contributing to United Nations Sustainable Development Goals related to health and well-being.

• Artificial Intelligence
• Reinforcement Learning
• Multi-agent Systems
• +۷ مورد دیگر

Jun Wang is a Professor of Information and Data Science in the Department of Computer Science at University College London. He serves as the Founding Director of the MSc Web Science and Big Data Analytics program and is the Co-founder and Chief Scientist of MediaGamma Ltd (acquired), a UCL startup focused on AI for intelligent audience decision-making. His educational background includes: Doctor of Philosophy from Technische Universiteit Delft (2007) Master of Science from National University of Singapore (2003) Bachelor's degree from Southeast University Nanjing (1997) Professor Wang's primary research focuses on AI and intelligent systems, with particular expertise in multiagent reinforcement learning, deep generative models, and their applications across several domains. His work spans information retrieval, recommender systems and personalization, data mining, smart cities, bot planning, and computational advertising. His research has practical implications in healthcare, robotics, and intelligent systems for decision-making, aligning with Sustainable Development Goal 3 (Good Health and Well-Being). His recent publications demonstrate a strong trend toward integrating large language models with reinforcement learning techniques to create more efficient and capable systems. The research spans healthcare applications, robotics, information systems, and strategic decision-making, with an emphasis on privacy preservation, efficient computation, and multi-agent coordination. Professor Wang has received several prestigious awards: Beyond Search – Semantic Computing and Internet Economics award from Microsoft Research Yahoo! FREP Faculty award UCLB One-to-Watch award 2016 for MediaGamma First place in global real-time bidding algorithm contest (80+ participants worldwide) Multiple Best Paper awards With over 15 years of experience, Professor Wang has advised numerous startups including Last.Fm, Passiv Systems, Massive Analytic, Context Scout, and Polecat. He has also collaborated with major industry players such as BT, Microsoft, Yahoo!, Alibaba, and Didi. His knowledge transfer activities bridge academic research with practical industry applications. Professor Wang has developed the Information Retrieval and Data Mining MSc module and the MSc/MRes programme on Web Science and Big Data Analytics. He also teaches a multiagent AI MSc module, sharing his expertise in cutting-edge AI techniques with the next generation of computer scientists.

Yuejie Chiمشاهده پروفایل

استاد

• Theoretical Statistics
• Data Science
• Generative AI
• +۵ مورد دیگر

Yuejie Chi is the Charles C. and Dorothea S. Dilley Professor of Statistics and Data Science at Yale University, with a secondary appointment in Computer Science. She is a member of the Yale Institute for Foundations of Data Science. Previously, she held the Sense of Wonder Group Endowed Professor position at Carnegie Mellon University with affiliations in the Machine Learning Department (MLD) and CyLab. Her career includes a visiting researcher position at Meta's Fundamental AI Research (FAIR) group. Dr. Chi received her Ph.D. and M.A. in Electrical Engineering from Princeton University in 2012 and 2009, respectively, and her B.E. (Hon.) in Electrical Engineering from Tsinghua University, Beijing, China, in 2007. Her educational background laid the foundation for her interdisciplinary research approach spanning statistics, computer science, and engineering domains. Professor Chi's research focuses on the theoretical and algorithmic foundations of data science, with particular emphasis on generative AI, reinforcement learning, and signal processing. Her work lies at the intersection of statistics, learning, optimization, and sensing, addressing fundamental challenges in improving the performance, efficiency, and reliability of AI systems in data-intensive but resource-constrained scenarios. Her group's research is highly interdisciplinary, tackling problems that require theoretical rigor alongside practical implementation considerations. Theoretical foundations of generative models and diffusion processes Algorithmic guarantees for reinforcement learning systems Robust and efficient optimization methods for large-scale problems Low-dimensional structures in high-dimensional data Professor Chi's recent publications reveal a strong trajectory toward addressing fundamental theoretical questions in AI while maintaining practical relevance. Her work demonstrates increasing focus on the interplay between theoretical guarantees and practical implementation, particularly in generative models and reinforcement learning systems. Notable themes include non-asymptotic convergence analysis, robustness guarantees, communication efficiency in distributed settings, and bridging theoretical insights with real-world applications. Among her distinguished recognitions are the Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House, the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award, the SIAM Activity Group on Imaging Science Best Paper Prize, and the IEEE Signal Processing Society Young Author Best Paper Award. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures. Additional honors include young investigator awards from NSF, ONR, and AFOSR, and she has been named a Goldsmith Lecturer by IEEE Information Theory Society (2021), a Distinguished Lecturer by IEEE Signal Processing Society (2022-2023), and a Distinguished Speaker by ACM (2023-2026). Professor Chi has mentored an impressive cohort of PhD students and postdoctoral researchers, many of whom have received prestigious fellowships and awards. Her advisees have secured positions at leading institutions including Johns Hopkins University, MIT, UNC Chapel Hill, and major technology companies like Meta, Apple, and Google. Her group has produced numerous award-winning papers and dissertations, including the IEEE SPS Best PhD Dissertation Award. She has successfully secured significant research funding from federal agencies and industry partners to support her interdisciplinary research program. Professor Chi leads a vibrant research group at Yale that maintains active collaborations across multiple disciplines and institutions. Her group's work spans theoretical analysis, algorithm development, and practical implementation, with emphasis on both foundational understanding and real-world applicability. The group has developed several influential frameworks including Robust Gymnasium, a unified modular benchmark for robust reinforcement learning, and has made significant contributions to understanding the theoretical properties of modern AI systems.

Xiao Yuمشاهده پروفایل

پژوهشگر

• LLMs Data Governance and Evaluation
• Intelligent Software Engineering
• Software Security and Reliability
• +۲ مورد دیگر

Xiao Yu is a Research Fellow (Assistant Research Professor) at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Previously, they were a Postdoctoral Researcher at Huawei under Prof. Xin Xia. They hold dual PhD degrees: from Wuhan University's School of Computer Science (December 2020) supervised by Prof. Jin Liu, and from City University of Hong Kong's Department of Computer Science (March 2021) supervised by Prof. Qing Li and Prof. Jacky Wai Keung. Research focuses on three interconnected domains: LLMs Data Governance and Evaluation addressing hallucination phenomena and task-specific LLM evaluation in software engineering; Intelligent Software Engineering leveraging deep learning for code generation, annotation, and maintenance; and Software Security and Reliability investigating vulnerability detection, log anomaly identification, and security bug classification. Their work bridges theoretical advancements with industrial applications, particularly in blockchain and data security contexts. Recent publications demonstrate strong trends in realistic LLM evaluation (RealisticCodeBench), vulnerability detection using semi-supervised learning, and industrial anomaly detection. Key thematic areas include effort-aware defect prediction, code smell detection, and the practical application of large language models in software engineering tasks, with increasing emphasis on data quality and privacy considerations. Xiao Yu actively contributes to the academic community through extensive service roles including journal reviewing for ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Dependable and Secure Computing, and serving on program committees for major conferences like APSEC 2025 and ASE 2025. They have supervised numerous graduate students as evidenced by authorship patterns in publications. Based at Zhejiang University's State Key Laboratory of Blockchain and Data Security, their research operates at the intersection of academic rigor and industrial relevance, with strong collaborations spanning multiple institutions including Huawei, Wuhan University, and City University of Hong Kong.