Our paper “ClioQuery:Interactive Query-oriented Text Analytics for Comprehensive Investigation of Historical News Archives” received the best paper runner-up award at TiiS 2022.
Authors: Abram Handler, Narges Mahyar, and Brendan O’Connor.
Link to the news.
Dr. Ali Sarvghad and Dr. Narges Mahyar along with Kara M Smith (MD) were awarded the PIT@UMass faculty fellowship.
You can read more here.
Prof. Dr. Narges Mahyar has been named a 2023-24 Harvard Radcliffe Fellow.
You can read more here.
Our paper “Investigating the Visual Utility of Differentially Private Scatterplots” was accepted to IEEE TVCG 2023.
Authors: Liudas Panavas, Tarik Crnovrsanin, Jane L. Adams, Johnathan Ullman, Ali Sarvghad, Melanie Tory, Cody Dunne
Our paper on “Building Community Resiliency through Immersive Communal Extended Reality (CXR)” was recently accepted to MDPI Multimodal Technologies and Interaction 2023.
Authors: Sharon Yavo-Ayalon, Swapna Joshi, Yuzhen (Adam) Zhang, Ruixiang (Albert) Han, Narges Mahyar, Wendy Ju.
You can find the paper here.
Our paper “Who Do We Mean When We Talk About Visualization Novices?” received the best paper award at CHI 2023. Congratulations team!
You can find the paper here.
Sheshera Mysore presented our paper, “Exploring how Data Scientists Review Scholarly Literature” at CHIIR 2023
You can find the presentation slides here.
“Exploring how Data Scientists Review Scholarly Literature” was accepted to CHIIR 2023
Authors: Sheshera Mysore, Mahmood Jasim, Haoru Song, Sarah Akbar, Andre Kenneth Chase Randall and Narges Mahyar
You can find the paper here.
“Investigating Perceptual Biases in Icon Arrays” was accepted to CHI 2022.
Authors: Cindy Xiong, Ali Sarvghad, Cagatay Demirlap, Jake M. Hofman, Deniel G. Goldstien
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.
System Overview
This figure presents CommunityBots’ system overview. We use an example to guide through the process of user interactions with multiple chatbots: 1) The Household chatbot asks the user questions about their family life; 2) The user responds that they want to skip the current topic; 3) The Household chatbot receives user’s response “Skip this topic”; 4) The Household chatbot forwards the user’s response to Juji’s NLU module; 5a) Juji uses NLU to identify the user’s engagement level; 5b) NLU determines that the user doesn’t want to talk about the current topic and passes this conclusion to CommunityBot’s Topic-Switch Mechanism; 6) Topic-Switch Mechanism determines which topic to change the conversation to; 7) Since there are no remaining topics for the Household chatbot to converse, the Topic-Switch Mechanism asks the Chatbot-switch mechanism to switch from the Household chatbot to the next chatbot in queue; 8) Chatbot-switch mechanism determines that the next chatbot to converse with the user is the Work chatbot; 9) Juji notifies the Chatbot-switch mechanism about the Work chatbot invocations; 10) The Chatbot-switch mechanism fetches the questions related to the Work chatbot; 11a) The Chatbot-switch mechanism “wakes up” the Work chatbot on user’s screen and passes the next question to be asked; 11b) At the same time, the Chatbot-switch Mechanism puts the Household chatbot in a inactive state; 12) The question asked by the Work chatbot is displayed on user’s screen; 13) The user proceeds to talk to the new chatbot.
You can find the paper here.