Xi Ding

Xi Ding

CMU Research Intern

ANU Master's Student

Welcome to Xi Ding’s Personal Website 👋

Code ⟇ Create ⟇ Chill
From Code to Creation, with a touch of Chill.

About Me 🏄‍♂️ Machine Learning Researcher | Trustworthy & Interpretable AI

I am currently a research intern at Carnegie Mellon University (CMU) working with Prof. Min Xu on trustworthy LLM/LVLM and AI4healthcare/biomedicine. Prior to CMU, I was a visiting scholar at the Australian Research Council Research Hub (ARC) working with Prof. Yongsheng Gao on interpretable machine learning and computer vision. Previously, I worked at the TIME Lab at Australian National University (ANU) as a research assistant advised by Dr. Lei Wang, where I conducted research on video understanding tasks. I completed my master’s degree in Machine Learning at ANU in 2025 and my bachelor’s degree in Economics at China Agricultural Univeristy (CAU) and University of Colorado Denver (CU Denver) in 2023.

My research centers on building reliable, interpretable, and generalizable machine learning frameworks and applications in healthcare and biomedicine. My work spans multimodal learning, graph-based learning, trust-aware domain adaptation, temporal reasoning, and uncertainty-guided kernel methods for structured data. Informed by my interdisciplinary background, I am especially attentive to how modern AI interfaces with human understanding and how their deployment impacts society.

With an interdisciplinary background bridging economics and machine learning, I bring a unique analytical perspective to my research, enabling me to approach complex AI problems with both quantitative rigor and structural reasoning. I have published multiple first-author papers, including one at Advances in Neural Information Processing Systems (NeurIPS), one at the AAAI Conference on Artificial Intelligence (AAAI), and two in the Companion Proceedings of The Web Conference (WWW), where I received the Best Paper Award 🏆. Beyond publishing, I contribute actively to the academic community as a reviewer for conferences like ICLR, AAAI, ICME, and AVSS, and served as a Workshop Coordinator at WWW 2025.

Outside of research, I enjoy basketball, badminton, traveling, and playing guitar. These activities keep me curious, creative, and balanced in both work and life.

I welcome discussions on research problems and am open to collaborations. Feel free to reach out at darcyddx [at] gmail [dot] com.

Short Bio 🧑‍🔬

Xi Ding is a researcher specializing in trustworthy and interpretable AI. He is currently a research intern at Carnegie Mellon University and has previously conducted research at the Australian Research Council Research Hub and the TIME Lab at the Australian National University. His work spans interpretable machine learning and computer vision, multimodal large language models, graph learning, kernel and tensor methods, and domain adaptation. Xi has published in top venues, including NeurIPS, AAAI, and received a Best Paper Award at a WWW workshop. Also, he actively contributes to the research community as a reviewer and coordinator.

News 📪

  • 2025-11-08 — Learning Time in Static Classifiers was accepted at AAAI 2026.
  • 2025-10-21 — Joined the Xu Lab as a research intern at CMU.
  • 2025-10-11 — Received the NeurIPS 2025 Scholar Award.
  • 2025-09-18 — Graph Your Own Prompt was accepted at NeurIPS 2025.
  • 2025-07-08 — Delivered an invited talk Echoes in the Model: When Features Reflect Predictions at the Data61/CSIRO ICVG Reading Group.
  • 2025-03-17 — Appointed as an ARC Research Hub visiting scholar.
  • 2025-03-16 — The Journey of Action Recognition won the Best Paper Award at the Companion Proceedings of the ACM Web Conference 2025.
  • 2025-01-28 — The Journey of Action Recognition was accepted for Oral Presentation at the Companion Proceedings of the ACM Web Conference 2025.
  • 2025-01-28 — Do Language Models Understand Time? was accepted for Oral Presentation at the Companion Proceedings of the ACM Web Conference 2025.
  • 2024-11-13 — Joined the TIME Lab as a research assistant at ANU.

✨ This site is a place to share my research, projects, and experiences!
💡 Whether you’re here to learn about my work or explore my interests, I hope you find something interesting and useful!