Hi! I am Zhiqiu (Zoe)! If you want to call me Zhiqiu, the pronunciation is like 🔊.

I am a postdoc researcher at CISPA Helmholtz Center for Information Security. My research interests comprise the topics of conversational AI, responsible data science, and trustworthy machine learning. I want to better understand AI in order to have more responsible, explainable, and robust applications and implications for society. I worked on projects about conversational AI for social good, human-computer interaction, big data and transportation analytics, natural language processing, and smart cities.

News

• 04-2023: Our CommunityBots paper was published online in 2023 ACM SIGCHI CSCW. A Kudos showcase is also available.
• 03-2023: Our ZEV paper examining consumers’ preferences and willingness to pay for zero-emission vehicle was accepted for publication in the Journal of Transport Policy!
• 11-2022: Our paper about designing, developing, and evaluating a multi-agent chatbot platform for public input elicitation was accepted for the proceedings of 2023 ACM SIGCHI CSCW!
• 04-2022: Our Driverless shuttles paper was accepted for publication in the Journal of Public Transportation!
• 02-2022: Our Mapping instability paper investigating the effects of the pandemic was accepted to the Environments By Design: Health, Wellbeing And Place Conference 2022.
• 07-2021: I defended my PhD dissertation at University of Virginia!
• 05-2021: I was awarded the CDS Postdoctoral Fellowship in the Department of Computer Science at UMass Amherst!

Recent Research Highlight

CommunityBots: Creating and Evaluating A Multi-Agent Chatbot Platform for Public Input Elicitation
Zhiqiu Jiang, Mashrur Rashik, Kunjal Panchal, Mahmood Jasim, Ali Sarvghad, Pari Riahi, Erica DeWitt, Fey Thurber, Narges Mahyar

Our recent work was accepted to CSCW 2023. We designed, developed, and evaluated CommunityBots — a multi-agent chatbot platform where each chatbot handles a different domain individually to elicit public input. To manage conversation across multiple topics and chatbots, we proposed a novel Conversation and Topic Management (CTM) mechanism that handles topic-switching and chatbot-switching based on user responses and intentions. Our evaluation demonstrates that CommunityBots participants were significantly more engaged, provided higher-quality responses, and experienced fewer conversational interruptions while conversing with multiple chatbots in the same session.
You can find the paper here.