|
Hi! 👋 I'm Leon, a first-year Computer Science Ph.D. student at Stanford University. I am fortunate to work with Prof. Nick Haber, Prof. Ludwig Schmidt, and Prof. Serena Yeung for the rotations. Before Ph.D., I did my undergrad and worked at Nanyang Technological University, advised by Prof. Ziwei Liu. I've also worked with Prof. Alan Yuille and Dr. Zongwei Zhou at Johns Hopkins University. My research focuses on developing scalable frameworks for training and deploying AI agents. I work on multimodal reasoning agents, data-efficient training methodologies, data collection pipelines, and practical architectures that enable agents to perform complex reasoning tasks. My interests span from foundational model development to building robust pipelines for agent training and evaluation. I'm passionate about bridging the gap between research and real-world applications of reasoning systems. Whether you're working on agent architectures, exploring collaborative research opportunities, or developing practical reasoning applications, I'd love to connect and discuss potential synergies. You can schedule a meeting. Email / Google Scholar / Semantic Scholar / Github / Linkedin |
|
|
|
|
Summary: The first open-source model trained on public reasoning data to match DeepSeek-R1-Distill's performance through 1000+ systematic data curation/synthesis experiments. |
|
Summary: A large categorized dataset with 24+ million biomedical image-text pairs across multiple domains with expert-guided annotations. |
|
Summary: Prioritizing global data diversity over local instance quality for instruction data selection using k-means clustering iteratively. |
|
Summary: A challenging long-context multi-modal document understanding benchmark with 1,062 expert-annotated questions requiring cross-page reasoning. |
|
Summary: The first benchmark evaluating AI agents on evolving real-world websites with 1,050 human-written multihop tasks requiring long-range reasoning. |
|
Summary: An open-source multilingual model fine-tuned on human-reviewed safety instructions aligned with the Biden-Harris AI safety executive order. |
|
Summary: An extensive benchmark systematically analyzing generative data across visual recognition tasks with a novel CLER Score metric. |
|
Summary: The first large-scale multi-modal instruction tuning dataset with 2.8 million instruction-response pairs derived from images and videos. A model is trained on this dataset to achieve state-of-the-art performance on vision-language tasks. |
|
Summary: The first multimodal agent study to use natural language as communication medium for LLMs to coordinate multiple vision-language models for complex reasoning. |
|
Summary: Extended scene graph generation from static images to dynamic videos with unified panoptic understanding of objects and stuff. |
|
Summary: A systematic approach to solving the cold start problem in vision active learning using self-supervised contrastive features without labels. |
|
Summary: Modeling spatial and visual relationships among calcifications using graph convolutional networks for breast cancer diagnosis. |
|
Summary: A unified open-source framework specifically designed for model-based reinforcement learning research with modular components. |
|
|
|
|