Activities

• Inspiring, innovative and pioneering research in 2025 by Tobii
  My previous work Do you need help? Identifying and responding to pilots’ troubleshooting through eye-tracking and Large Language Model has been featured in Tobii’s 2025 Annual Research Report. I’m glad to see my works getting recognized, such little surprises in life keep motivating me 😁😁😁

• I’m excited to share some recent progress in my research at February HART TG Monthly Memebr Conversations
  I’ll be sharing my review work on GenAI techniques in Autonomous Driving HCIs through the lens of Situation Awareness (SA). This is such a rapidly evolving and fascinating area, and I hope my perspective can contribute some useful insights to the ongoing conversations. I’m sure there are important aspects I haven’t fully captured or considered. I’d love to hear your thoughts, questions, and different perspectives during and after the talk! Your feedback and insights would be incredibly valuable in helping me, and all of us, better understand these emerging technologies and their human-centered applications. Looking forward to a great discussion with everyone!🚗🤖
  Zoom Meeting
  Meeting ID: 971 6234 9704
  Passcode: HART26

• I’m deeply honored and thrilled to share that I’ve received the 2025 IEA/Kingfar Student Award for Research in Human Factors and Ergonomics!
  

This recognition means so much to me as it celebrates the work completed during my PhD journey.

I’m energized to continue my research in Human Factors, Human-AI Teaming, and related areas, and I look forward to contributing to this vibrant research community.

• I have joined the Hybrid Intelligence (HI) Lab directed by Mengyao Li on 15 Sep
  I’m thrilled to be part of a group dedicated to advancing human-AI collaboration and understanding how we can achieve better outcomes together than either humans or machines can alone. I look forward to this exciting new chapter in my academic journey.🚀 (Chinese cuisines here in Atlanta are amazing as these team fellows 😆)

• Do you need help? Identifying and responding to pilots’ troubleshooting through eye-tracking and Large Language Model
  In this study, we introduce an innovative approach that tokenizes eye-tracking data into Visual Attention Matrices (VAMs) and integrates them with Large Language Models to identify and respond to pilots’ troubleshooting activities in real-time. This represents one of the early works combining eye-tracking data with LLMs for adaptive aviation support systems. The method addresses two key challenges: capturing the complex troubleshooting behaviors of actively engaged (In-the-Loop) pilots, and effectively processing non-semantic eye-tracking data using LLM technology.