Projects
CS598HT: Consistency-Aware Graph Memory for Training-Free VLM Test-Time Adaptation Urbana, IL Spring 2026
Member of a three-person team
  • Framework: Proposed CAGM, a training-free test-time adaptation framework for vision-language models that combines dynamic memory with graph-based label propagation.
  • Consistency Score: Designed a Jensen-Shannon-divergence-based consistency score to measure agreement between zero-shot CLIP predictions and label-propagation predictions.
  • Adaptive Mechanism: Used the consistency score as a memory update gate, label-propagation edge weight, and adaptive prediction interpolation coefficient to reduce noisy pseudo-label propagation.
  • Evaluation: Implemented the full adaptation pipeline and evaluated the method on standard OOD and cross-domain benchmarks against recent memory- and graph-based TTA baselines.
CS543: Where Do Modern Monocular Depth Models Fail? A Cross-Domain Robustness Study Urbana, IL Spring 2026
Member of a three-person team
  • Evaluation Pipeline: Built a unified evaluation pipeline to study the robustness of modern monocular depth estimation models under domain shifts such as low light, transparent objects, and scene variation.
  • Model Setup: Set up and ran pretrained Depth Anything V2 and ZoeDepth models, including baseline inference and depth-map visualization.
  • Benchmarking: Compared Depth Anything V2, ZoeDepth, and Marigold using synthetic RGB-D samples with known ground-truth depth, and computed affine-aligned AbsRel as an initial sanity-check metric.
  • Analysis: Analyzed failure patterns across different visual conditions, showing that transparent and specular objects are consistently challenging for all three models.
CS546: Strategic Persuasion in LLMs: Multi-Turn Dialogue with Reinforcement Learning Urbana, IL Fall 2025
Member of a six-person team
  • Agent Design: Developed a planner-persuader LLM agent for multi-turn strategic persuasion, decoupling high-level persuasion strategy selection from natural language response generation.
  • RL Fine-Tuning: Fine-tuned Qwen3-4B-Instruct-2507 with GRPO using the rllm framework to improve strategy adaptation across dialogue turns.
  • Reward Design: Designed a composite RL reward integrating XML-format compliance, length control, strategy-following accuracy, and normalized attitude-shift improvement.
  • Evaluation: Conducted multi-model evaluation against GPT-4o, Qwen3-4B, Qwen2.5-7B, and LLaMA3.1-8B-Instruct persuadees, and analyzed how persuasion performance changes with conversation length.
Federated Knowledge-Augmented Generation ZJU-UIUC Institute, China Spring 2025
Advisor: Qiang Zhang
  • Goal: Built a privacy-preserving and personalized text generation framework by combining federated learning with knowledge-augmented generation.
  • Method: Designed a federated KAG pipeline that enables distributed training across clients without sharing raw data, while using retrieval-augmented knowledge to improve generation quality.
  • Contribution: Collected data, built the project codebase based on LightRAG, and implemented the core training and generation pipeline.
  • Link: Github Repo.
ECE445: Senior Design Laboratory ZJU-UIUC Institute, China Spring 2025
Member of a four-person team
  • Goal: Built a language-guided robotic grasping system that identifies and grasps target objects from spoken instructions.
  • Automatic Speech Recognition: Translated spoken instructions into text and extracted the target object label using Wave2Vec 2.0 followed by an NLP model.
  • Computer Vision: Used RGB images from the depth camera to detect the target object and estimate its pixel-level bounding box with YOLOE-V8L.
  • 6D Pose Estimation: Combined RGB and depth images to estimate the target object's 6D pose, including [x, y, z] coordinates and three Euler angles, using Open3D.
  • Motion Planning: Used the estimated 6D pose to choose an appropriate grasping position and map it to motor destination angles.
  • PCB Display: Display the name of the target object on a 0.96-inch OLED screen.
  • Code and report are available here.
ECE408: CUDA Optimization for LeNet Urbana, IL Fall 2024
Individual project
  • Streaming: Overlapped data transfer with kernel execution by dividing large vectors into segments and running kernels while copying data between device and host memory.
  • Kernel Fusion: Implemented convolution with matrix multiplication using three kernels, then fused them into a single kernel to reduce overhead and improve performance.
  • High-Level Libraries: Used Tensor Cores through Warp Matrix Functions and the cuBLAS library.
  • Other Optimizations: Applied constant memory for weights, the __restrict__ keyword, and loop unrolling.
ECE391: 391 OS System Urbana, IL Fall 2023
Member of a four-person team
  • Basic Functionalities: Implemented an operating system supporting scheduling, interrupts, system calls, exceptions, and file systems.
  • Self-Designed Features: Added ATA drivers for a writable file system, command history, customizable colors, and autocomplete.
  • Systems Programming: Built the kernel and device-driver stack in C and x86, including paging, interrupt handling, terminal support, and course-provided machine problem interfaces.
  • Project repository.
Awards