Category Archives: News

“From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization” was accepted to TVCG 2022

Authors: Alyxander Burns, Christiana Lee, Thai On, Cindy Xiong, Evan Peck, Narges Mahyar

You can find the paper here.

“How Data Scientists Review the Scholarly Literature” was accepted to CHIIR 2023

Authors: Sheshera Mysore, Mahmood Jasim, Haoru Song, Sarah Akbar, Andre Kenneth Chase Randall, Narges Mahyar

You can find the paper here.

“CommunityBots: Creating and Evaluating A Multi-Agent Chatbot Platform for Public Input Elicitation” was accepted to CSCW 2023

Authors: Zhiqiu Jiang, Mashrur Rashik, Kunjal Panchal, Mahmood Jasim, Ali Sarvghad, Pari Riahi, Erica DeWitt, Fey Thurber, Narges Mahyar.

In recent years, the popularity of AI-enabled conversational agents or chatbots has risen as an alternative to traditional online surveys to elicit information from people. However, there is a gap in using single-agent chatbots to converse and gather multi-faceted information across a wide variety of topics. Prior works suggest that single-agent chatbots struggle to understand user intentions and interpret human language during a multi-faceted conversation. In this work, we investigated how multi-agent chatbot systems can be utilized to conduct a multi-faceted conversation across multiple domains. To that end, we conducted a Wizard of Oz study to investigate the design of a multi-agent chatbot for gathering public input across multiple high-level domains and their associated topics. Next, we designed, developed, and evaluated CommunityBots — a multi-agent chatbot platform where each chatbot handles a different domain individually. 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. We conducted a between-subject study comparing CommunityBots to a single-agent chatbot baseline with 96 crowd workers. The results from our evaluation demonstrate that CommunityBots participants were significantly more engaged, provided higher quality responses, and experienced fewer conversation interruptions while conversing with multiple different chatbots in the same session. We also found that the visual cues integrated with the interface helped the participants better understand the functionalities of the CTM mechanism, which enabled them to perceive changes in textual conversation, leading to better user satisfaction. Based on the empirical insights from our study, we discuss future research avenues for multi-agent chatbot design and its application for rich information elicitation.

You can find the paper here.

Professional Activities

I will be serving as:

  • Technical Program Committee for ACM International Conference on Interactive Surfaces and Spaces (ISS) 2016
  • Program Committee for Graphics Interface (GI) 2016
  • Organizing Committee for BELIV 2016 (Beyond Time And Errors: Novel Evaluation Methods For Visualization), in conjunction with IEEE VIS 2016
  • Organizing Committee for HCI Curriculum Renewal Workshop, in conjunction with Graphics Interface (GI) 2016