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Director: Professor Matthew Lease ACM Distinguished Member • AAAI Senior Member Publications • Datasets & Software • Slides 1616 Guadalupe Ste 5.202 |
Overview: Our research spans artificial Intelligence (AI) modeling and human-computer interaction (HCI) design. We create novel datasets, build AI models, and evaluate model performance and end-user impacts. When automated AI falls short, we design human-in-the-loop approaches supported by AI model explanations and creative user interfaces. To promote fair AI, we design better ways to annotate data without bias and modeling techniques to mitigate dataset biases. We conduct fundamental research, applied to real-world problems that matter, as part of UT Austin's Good Systems Grand Challenge to design responsible AI technologies. A theme of ongoing work is content moderation: automated, human-in-the-loop, and human-safe practices to curb disinformation, hate speech, and polarization online.
Research Areas:
Crowdsourcing & Human Computation (HCOMP) •
Fair & Explainable AI •
Information Retrieval (IR) •
Natural Language Processing (NLP)
Some recent publications and talks:
Other recent awards 🏅
Ripon Saha, Matthew Lease, Sarfraz Khurshid, and Dewayne Perry.
Improving Bug Localization using Structured Information Retrieval.
In IEEE/ACMConference on Automated Software Engineering (ASE), pages 345-355, 2013.
ASE 2024 Most Influential Paper Award (selection among papers from 2012-2014).
Postdocs
PhD Students
Selected Research & Demos
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Wei Sun and Matthew Lease. Robust Human-AI Collaboration with Bandit Feedback. The Conference on Information Systems and Technology (CIST), 2022. Best Student Paper award. [conference-website]
Li Shi, Nilavra Bhattacharya, Anubrata Das, Matthew Lease, and Jacek Gwizdka. The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims. In Proceedings of the 7th ACM SIGIR Conference on Human Information, Interaction and Retrieval (CHIIR), pages 315--320, 2022. [ bib | pdf | demo | sourcecode | video | poster | tech-report ]
Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), pages 33--42, 2020. [ bib | pdf | demo | blog-post | sourcecode | video | slides ]
Mucahid Kutlu, Tyler McDonnell, Tamer Elsayed, and Matthew Lease. Annotator Rationales for Labeling Tasks in Crowdsourcing. Journal of Artificial Intelligence Research (JAIR), 69:143--189, 2020. Award Winning Papers Track. [ bib | pdf | blog-post | data | conference-website ]
Soumyajit Gupta, Mucahid Kutlu, Vivek Khetan, and Matthew Lease. Correlation, Prediction and Ranking of Evaluation Metrics in Information Retrieval. In Proceedings of the 41st European Conference on Information Retrieval (ECIR), pages 636--651, 2019. Best Student Paper award. [ news | bib | pdf | data | sourcecode | slides | tech-report ]
An Thanh Nguyen, Aditya Kharosekar, Aditya Kharosekar, Saumyaa Krishnan,
Siddhesh Krishnan, Elizabeth Tate, Byron C. Wallace, and Matthew Lease.
Believe it or not: Designing a Human-AI Partnership for
Mixed-Initiative Fact-Checking.
In Proceedings of the 31st ACM User Interface Software and
Technology Symposium (UIST), pages 189--199, 2018.
[ bib |
pdf |
demo |
sourcecode |
video |
slides ]
Tyler McDonnell, Matthew Lease, Mucahid Kutlu, and Tamer Elsayed. Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments. In Proceedings of the 4th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), pages 139--148, 2016. Best Paper Award. [ news | bib | pdf | blog-post | data | slides ]
Hyun Joon Jung and Matthew Lease. A Discriminative Approach to Predicting Assessor Accuracy. In Proceedings of the 37th European Conference on Information Retrieval (ECIR), pages 159--171, 2015. Samsung Human-Tech Paper Award: Silver Prize in Computer Science. [ bib | pdf | news ]
Selected Talks: Videos and Slides (videos link is most current content)
Selected News
Alumni
Ph.D. Students
Md. Mustafizur Rahman (LinkedIn), 2021, Microsoft
An Thanh Nguyen, 2020
Ye Zhang, 2019, Google
Hyunjoon Jung, 2015, Adobe
Masters Students
Utkarsh Mujumdar (iSchool), 2023, Autonomize AI. Thesis: Designing a Multi-Perspective Search System Using Large Language Models and Retrieval Augmented Generation. Winner: Dean's Choice Award.
Yian Wong (Computer Science), 2023, CenterPoint Energy. Thesis: Exploring Multiple Perspectives to Mitigate Cognitive Biases through an Integrated Interface to Language Model.
Sooyong Lee (Computer Science), 2023, UMass CS PhD Program. Thesis: Multi-Task Learning for Hate Speech Detection.
Yiheng (Sam) Su (Computer Science), 2023, iSchool PhD Program. Thesis: Wrapper Boxes for Increasing Model Interpretability via Example-based Explanations.
Sukanya Thapa (iSchool), 2023, Lazaza. Thesis: Enhancing Worker Management and Supporting External Tasks in Crowdsourced Data Labeling.
(2019-2022 to be posted...)
Vivek Pradhan, 2018, Earthmetry
Tyler McDonnell, 2017, SparkCognition
Ivan Oropeza, 2015, Google
Haofeng Zhou, 2015, Facebook
Shruti Bhosale, 2014, Facebook
Hohyon Ryu, 2012, OXOpolitics
Aashish Sheshadri, 2014, PayPal
Donna Vakharia, 2014, PayPal
Undergraduate Students (selected)
Didi Zhou, 2022, Google. Thesis: Leveraging Annotator Rationales for Active Learning with Transformers. See also: CS Dept. news article (Dec. 14, 2022).
Qiwei (Renee) Li, 2019, Michigan PhD Program. Thesis: Clickbait and Emotional Language in Fake News
