Xiaolong Luo

Xiaolong Luo

Ph.D. Student in Engineering Science at Harvard University

Welcome to my homepage! I am a Ph.D. candidate in Engineering Science at Harvard School of Engineering and Applied Sciences, fortunate to be advised by Prof. Michael Lingzhi Li. Prior to that, I obtained my Bachelor’s degree in Statistics from the University of Science and Technology of China (USTC), where I had the privilege of being advised by Prof. Yanran Wang and the opportunity to work with Prof. Atlas Wang. I was honored to receive the 41st Guo Moruo Scholarship, the highest honor for USTC undergraduates.

My research focuses on developing the foundations of next-generation artificial intelligence techniques to enhance its effectiveness and practical applications in healthcare. I am particularly interested in developing flexible diagnostic models with multi-task and multi-modal learning, aiming to improve medical assessments through the integration of various types of medical data.

You can reach me via email at xiaolongluo@g.harvard.edu. 🤝 I am actively seeking collaborations in healthcare-related domains! If you’re interested in AI applications in healthcare, medical imaging, clinical data analysis, or any related fields, feel free to reach out!

📋 Note: I am currently looking for summer 2026 internships! If you know of any suitable positions, I’d love your recommendations. Also always happy to chat over coffee!

🔬 Research Interests

  • Flexible Diagnostic Models with Multi-task and Multi-modal Learning: I work on developing advanced AI models that can simultaneously handle multiple diagnostic tasks while effectively integrating various types of medical data (imaging, clinical notes, lab results) to provide more comprehensive and accurate medical assessments.

  • Evaluating and Enhancing LLM Reasoning in Real-World Medical Settings: I work on developing evaluation frameworks and benchmarks to assess and improve how large language models reason over complex, temporally extended medical data. My goal is to enhance their fidelity, calibration, and robustness, ultimately enabling trustworthy and generalizable clinical AI systems.

🌱 Long-term Vision

I aspire to become a compound talent who deeply understands large-scale medical data, multi-modal learning, and end-to-end clinical applications. My long-term goal is to bridge the gap between cutting-edge AI research and real-world healthcare impact, driving both technical innovation and product commercialization. Ultimately, I hope to promote a more equitable distribution of medical resources, enabling better healthcare access and outcomes for all, and bringing broader benefits to society.

🔥 News

  • [2025.11] Our paper“The CRITICAL Records Integrated Standardization Pipeline (CRISP)” accepted by ML4H 2024 as Spotlight! Will also hold a poster session at Time Series for Health (TS4H) workshop. See you in December in San Diego!
  • [2025.11] Great experience presenting “Towards Interpretable, Sequential Multiple Instance Learning: An Application to Clinical Imaging” at AMIA 2025 in Atlanta! The conference was fantastic!
  • [2025.10] Successfully passed my qualification exam and officially became a Ph.D. candidate! 🎉
  • [2025.09] New paper on arXiv: “The CRITICAL Records Integrated Standardization Pipeline (CRISP)” - Check it out & open to collaboration!
  • [2025.06] Our paper accepted by AMIA 2025! See you in Atlanta this November!
  • [2025.05] Successfully completed all PhD coursework and earned CS Master’s degree en route to PhD!

📝 Publications

Authors with * contributed equally.

Journal Articles

  • AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans
    YR Wang, L Qu, ND Sheybani, X Luo, J Wang, KE Hawk, AJ Theruvath, et al.
    Radiology: Artificial Intelligence (IF: 22.5), Apr. 2023; [Link]

Conference Papers

Working Projects

  • Healthcare AI Safety and Reliability Research
    Working with the CRITICAL dataset on exciting healthcare applications. Also exploring LLM reasoning traces and addressing a fundamental challenge: when AI models are used in healthcare applications, either through single LLM calls or an agentic system, there is a strong need to fully understand when and how AI models fail, and how to fix those issues to prevent catastrophic consequences.

🎤 Invited Talks

  • “Towards Interpretable, Sequential Multiple Instance Learning: An Application to Clinical Imaging”
    INFORMS Annual Meeting, Seattle (2024)

📝 Professional Service

Reviewer

  • CVPR 2026 - Conference on Computer Vision and Pattern Recognition
  • ICLR 2026 - International Conference on Learning Representations
  • NeurIPS 2025 Workshop Imageomics - Imageomics Workshop
  • NeurIPS 2025 Workshop GenAI4Health - Generative AI for Health Workshop
  • ML4H 2025 - Machine Learning for Health Symposium
  • ICLR 2025 - International Conference on Learning Representations

📚 Education

  • Harvard University, Cambridge, Massachusetts (2022-Present)
    Ph.D. in Engineering Science

  • Harvard University, Cambridge, Massachusetts (2022-2025)
    S.M. in Computer Science

  • University of Science and Technology of China, Anhui, China (2018-2022)
    Bachelor of Technology in Statistics

🏆 Honors and Awards

Academic Awards

  • The 41st Guo Moruo Scholarship (Top 1%, highest honor at USTC) (2021)
  • National Scholarship (Top 1%, from Ministry of Education of China) (2019)
  • Outstanding Student Scholarship, Golden Award (Top 5%) (2020)
  • Chinese Mathematics Competitions, Anhui, The Second Prize (2019)

Leadership & Entrepreneurship

💻 Technical Skills

Check out my GitHub profile for code and projects.

👨‍🏫 Teaching

Head Teaching Fellow

AM101: Statistical Inference for Scientists and Engineers (Spring 2024)

  • Instructor: Prof. Rob Howe
  • Class size: 55 students

Teaching Fellow

ENG-SCI 139: Innovation in Science and Engineering: Conference Course (Fall 2025)

  • Instructor: Prof. David Ricketts
  • Class size: 67 students
  • Jointly offered with Graduate School of Design as SCI 6272

COMPSCI 1090B: Data Science 2: Advanced Topics in Data Science (Spring 2025)

  • Instructor: Prof. Pavlos Protopapas (SEAS) & Natesh Pillai (Statistics)
  • Class size: 277 students

NEURO 240: Biological and Artificial Intelligence (Spring 2025)

  • Instructor: Prof. Gabriel Kreiman
  • Class size: 142 students
  • Course Website

CS 182: Artificial Intelligence (Fall 2023)

  • Instructors: Prof. Stephanie Gil; Prof. Milind Tambe (Harvard SEAS)
  • Class size: 138 students

Stat 139: Introduction to Linear Models (Fall 2023)

  • Instructor: Prof. James Xenakis (Harvard GSAS)
  • Class size: 83 students

Probability Theory and Mathematical Statistics (Fall 2021)

  • Instructor: Prof. Canwen Hong (Applied Math, USTC)
  • Class size: 97 students

Differential Equation I (Fall 2020)

  • Instructor: Prof. Wuqing Ning (Applied Math, USTC)
  • Class size: 156 students

🌍 Visitors

📧 Contact

Last updated: Oct. 2025