I am an incoming PhD student in Computer Sciences at the University of Wisconsin–Madison. Previously, I was a research intern at Carnegie Mellon University (CMU), where I worked with Prof. Min Xu on trustworthy LLMs/LVLMs and AI4Healthcare/Biomedicine. I also worked as a visiting scholar at the Australian Research Council Research Hub (ARC) with Prof. Yongsheng Gao on interpretable machine learning, and as a research assistant at the TIME Lab at the Australian National University (ANU), advised by Dr. Lei Wang, where I conducted research on video understanding.

My research centers on building reliable, interpretable, and generalizable AI/ML frameworks. My work spans representation learning, graph-based learning, trust-aware domain adaptation, temporal reasoning, uncertainty-guided kernel methods, video understanding, and LLMs.

Research Passion: I am deeply motivated by the question of how we can design AI systems that not only perform well, but also align with human understanding, operate transparently, and remain robust in high-stakes settings.

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 NeurIPS, one at ICLR, one at 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.