Research Projects
Long-Form Video-Language and Audio-Visual Social Understanding
Undergraduate Research Assistant, 2025.12 - 2026.05, Champaign, IL
- Proposed memory-efficient streaming video-language models with temporal transformer blocks for long-form video understanding beyond 30-minute sequences, boosting zero-shot accuracy by 17.4% and cutting peak memory by 58.2% compared to full-sequence attention.
- Developed multimodal fusion pipelines to combine facial action units, body pose, and prosody through cross-modal attention and infer social intent and conversational role from raw recordings, achieving 89.1% accuracy on speaker-role classification over a 2,108-turn bootstrap.
- Scaled end-to-end annotation pipelines for full-length clinical sessions with automatic transcription and labeling, processing recordings 1.4x to 1.7x faster than real time and increasing speaker-role inversion accuracy to 90.0%.
Multi-agent Research Synthesis Engine
Undergraduate Research Assistant, 2025.11 - 2026.05, Champaign, IL
- Orchestrated eight specialized agents across a 12-step workflow for literature synthesis to cover planning, retrieval, drafting, reflection, and safety review in a single loop with LLM-as-judge evaluation, scoring 0.955 overall and 0.925 on relevance, safety, and clarity.
- Developed concurrent multi-source retrieval across Semantic Scholar and Tavily with production-grade fallback handling for API failures, cutting query latency from 8.2 s to 4.9 s, a 40.2% reduction.
- Designed customized Model Context Protocol servers to centralize governed tool access behind unified interfaces over academic databases, code repositories, and document stores, cutting serving latency by 87.5%.
Links: Project Page
Realistic Neural Style Transfer Architecture
Undergraduate Research Assistant, 2025.01 - 2025.08, Champaign, IL
- Proposed style transfer frameworks to maintain photorealistic results under highly abstract styles with VGG perceptual losses and edge-preserving constraints, improving SSIM by 77.0% and MS-SSIM by 49.0% compared to TensorFlow's NST.
- Optimized multi-layer Gram matrix losses with adaptive layer weighting to suppress texture and chromatic artifacts, boosting SSIM by 4.4x and MS-SSIM by 6.4x compared to ChatGPT-4o.
- Formulated Sobel edge and total variation losses to keep contours sharp and suppress high-frequency noise, decreasing LPIPS by 23.0% and DISTS by 17.0% compared to TensorFlow's NST.
Links: GitHub
Anime Statistics and Analysis Platform, ASAP
Undergraduate Research Assistant, 2025.02 - 2025.06, Champaign, IL
- Deployed interactive R Shiny analytics platforms to surface anime popularity trends and market opportunities with live Jikan REST API data on 18,000 titles and predictive analysis on shinyapps.io.
- Developed R data pipelines to fetch, clean, and store 18,000 anime titles from the Jikan REST API in SQLite, adding genre, studio, and producer fields for five interactive analysis and recommendation modules.
- Trained random forest models to predict anime scores from engagement features, decreasing test RMSE by 90.1% and raising R² by 3.7% compared to linear regression.
Links: GitHub