Zhengran Ji

I am currently a Master's student in Computer Sceince at Duke University in Durham NC, United States. My advisor is Prof. Boyuan Chen. I recived my BS in Mathematics and Computer Science from University of California, Irvine (UCI), my advisor was Prof. Huolin Xin.

I am currently working on Human-guided Reinforcemnet Learning and Multi-Agent Reinforcemnet Learning.

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Research

I'm interested in multi-agent and multi-robot systems. Specifically, how collabration is formed between agents and how to effectively train collabrative agents or robots.

We introduce GUIDE, a framework for real-time human-guided reinforcement learning that accelerates policy learning by integrating continuous human feedback into dense rewards. GUIDE also features a simulated feedback module that learns human feedback patterns, reducing the need for human input while enabling ongoing training. Tested on tasks with sparse rewards and visual observations, GUIDE achieves a 30% higher success rate with just 10 minutes of human feedback compared to standard RL methods. Our human study with 50 participants highlights the effectiveness of our approach.

We introduce HUMAC, a novel framework that enables multi-robot collaboration from single-human guidance. HUMAC leverages a novel human-robot interface that allows a single human to guide multiple robots simultaneously. We demonstrate the effectiveness of HUMAC in a variety of multi-robot tasks, including search and rescue, object retrieval, and multi-robot formation control.

We introduce a platform for Human-AI teaming research. CREW offers extensible environment design, enables real-time human-AI communication, supports hybrid Human-AI teaming, parallel sessions, multimodal feedback, and physiological data collection, and features ML community-friendly algorithm design.

We developed a deep learning model to decompose mixed Mn oxidation states from Mn L2,3 edges in EELS and XAS, without the need for reference spectra or calibration. Trained on a large synthetic dataset, the model achieves high accuracy and robustness, successfully validating against experimental Mn oxide data for automated analysis.

We propose a CNN-BiLSTM neural network to automate the detection and identification of core-loss edges in EELS spectra, overcoming challenges like low signal-to-noise ratios and weak edges in raw data. Trained on a synthesized spectral database modeled after real experimental data, the network achieves 94.9% accuracy, enabling fast, accurate core-loss edge recognition without manual preprocessing.

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