Work
Selected applied ML systems and research engineering work.
Applied ML
Industry systems
Production-oriented systems for foundation-model evaluation, multimodal data, and efficient inference.
Fine-Grained Benchmark Generation
An automated system for building grounded, technically demanding evaluation tasks from reference material.
Built OCR ingestion and a multi-agent designer/verifier workflow with repair, validation, and semantic deduplication.
Open-PMC-18M data pipeline
A large-scale extraction and preprocessing workflow for compound medical figures and their textual context.
Owned preprocessing and cut Qwen2.5-VL decoding from 4.0s to 1.2s and summary generation from 4.0s to 0.6s per sample.
Research engineering
Research systems and benchmarks
Post-training, interactive agents, robust evaluation, and visualization intelligence.
RL-Text2Vis
A GRPO framework that evaluates text-to-visualization outputs after code execution.
Improves code executability and chart quality over strong prompting and supervised baselines.
DashboardQA
A benchmark for agents answering questions through real interactive dashboards.
Co-created it to expose failures in visual grounding, planning, GUI interaction, and multi-view reasoning.
ChartQAPro
A challenging chart-question-answering benchmark designed around diverse real-world visualizations.
Co-created 1,341 charts and 1,948 questions used to evaluate 21 open- and closed-source VLMs.
Large and tiny VLM judges
Reliable and efficient automated evaluation protocols for chart models.
A 2B judge matched a 7B baseline while reducing inference latency.
Foundation
Earlier research
DataNarrative
An Actor–Critic-style workflow for planning, reflecting on, and revising data-driven stories with visualizations.
Introduced a benchmark of 1,449 real-world data stories for system and human evaluation.