Hi, I'm
Haoli Yin
I build systems that learn to see.
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Writing
7
Essays
2023
Since
Data Pruning at Scale
CurationAI Insights — a paper-summary newsletter I ran in college
4 postsNow
Building
Data curation and eval infrastructure at Datology. Mostly quality scoring, dedup, and mixture optimization right now.
Exploring
Robotics. What changes when perception has physical consequences. Also: how much of AI research itself can be automated.
Practicing
Viola. Working on Romanze, Op.85 (Bruch, Max). Relearning intonation.
Cooking
Lately: the chipotle chicken packets from Costco, rice-steamer one-pot meals, and hosting hot pot dinner nights.
Work
Experiment Harness
Automated orchestration that compressed month-long research cycles to weekends. Built because I was tired of babysitting runs.
VLM Eval Stack
Migrated VLM evaluation from HuggingFace to vLLM-based inference. 10x faster eval cycles. Built because the gap between running an experiment and knowing whether it worked was too long.
VLM Data Curation Pipeline
Curation at multi-billion sample scale for CLIP pretraining. Curated data matched uncurated performance 10x faster, 2x faster than CLIPScore filtering.
UniCat
Showed that training sensor modalities independently and concatenating at inference beats joint fusion for multimodal re-identification. The result that made me take data-level decisions seriously as a research variable.
Seeing
Upstream.
Perception.
Aliveness.
Robotics and embodied AI pull on this thread. Perception with physical consequences is different from perception for retrieval or classification. When your next action depends on what you see, the cost of misperception is immediate. I want to understand where that changes the design.
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