Scholar
Yingtao Luo
Google Scholar ID: g_MmNEoAAAAJ
PhD Candidate, Carnegie Mellon University
Citations & Impact
Citations
877
H-index
11
i10-index
11
Publications
20
Co-authors
13
list available
Publications
Resume (English only)
Academic Achievements
- No specific information provided about publications, awards, patents, or projects.
Research Experience
- Worked as a research intern at Damo Academy of Alibaba and Microsoft Research Asia. Research work involves methodological innovation in sequential decision-making agents with real-world applications in heart transplantation decision-making.
Education
- Third-year Ph.D. student in the joint Machine Learning and Public Policy program (with Heinz College and Machine Learning Department) at Carnegie Mellon University, primarily advised by Prof. Rema Padman, and guided by Prof. Reza Skandari from Imperial College London, Prof. Bryan Wilder from CMU, and Dr. Arman Kilic from MUSC. Before joining CMU, worked mainly with Prof. Jun Zhu at Tsinghua University and Prof. Qiang Liu at CASIA.
Background
- Research interests include decision-making and foundation models, with a focus on the intersection of machine learning and public policy. Mainly focuses on designing intelligent agents capable of adaptive reasoning, self-evolution through past failures, and meaningful collaboration with clinical experts in high-stakes environments; advancing the use of large language and multimodal models to extract, integrate, and reason over diverse biomedical data, enabling more personalized, context-aware, and evidence-grounded decision support.
Miscellany
- Interested in the real-world impact of research and how people’s lives can be directly improved. Skillset includes core ML (statistics, convex optimization, graphical model, reinforcement learning, etc.) and coding with Pytorch and CUDA, but also enjoys learning human decision bias and healthcare informatics to aid his research.
