Builders & open source
Sameer Singh
Professor, Department of Computer Science, University of California, Irvine, UC Irvine
- Professor, Department of Computer Science, University of California, Irvine1
- Cofounder/CTO, Spiffy AI1
Sameer Singh is a Professor of Computer Science at the University of California, Irvine, working primarily on robustness and interpretability of machine learning algorithms and on models that reason with text and structure for natural language processing. Singh was a postdoctoral researcher at the University of Washington from 2013 to 2016 and an Allen Fellow at the Allen Institute for AI from 2021 to 2023, and lists a current role as co-founder and CTO of Spiffy AI. With Marco Tulio Ribeiro and Carlos Guestrin, Singh co-authored the paper that proposed LIME, a technique for explaining the predictions of any classifier, and Singh is a co-author of Beyond Accuracy: Behavioral Testing of NLP models with CheckList.123
At a glance
- Current roles
- Professor, Department of Computer Science +1 more
- Sector
- University
- Education
- UW
- Work listed
- 2
- Honors
- 6
- Sources cited
- 4
- Profile reviewed
Career
Sameer Singh received a PhD from the University of Massachusetts Amherst, with internships at Microsoft Research, Google Research and Yahoo! Labs, and was a postdoctoral researcher at the University of Washington from 2013 to 2016. Singh joined UC Irvine as an assistant professor of computer science in 2016, became an associate professor in 2021 and a full professor in 2024, with affiliations in Linguistics and EECS.1
From 2021 to 2023 Singh was an Allen Fellow at the Allen Institute for AI, and Singh's site lists a current role as co-founder and CTO of Spiffy AI.1
Research
Singh's research concerns the robustness and interpretability of machine learning and models that reason over text and structure. LIME, introduced in 2016 with Marco Tulio Ribeiro and Carlos Guestrin, explains an individual prediction of any classifier by fitting an interpretable model locally around it. A related 2016 paper on model-agnostic interpretability received the workshop best paper award at the ICML Workshop on Human Interpretability in Machine Learning.124
CheckList (2020), again with Ribeiro and others, proposed behavioral testing of NLP models with a matrix of linguistic capabilities and test types, and received the best paper award at ACL 2020. Later award-winning work from Singh's group includes FRUIT, a NAACL 2022 task on updating text to reflect new information.34
Work and contributions
LIME (Why Should I Trust You? Explaining the Predictions of Any Classifier) Published research
Co-author of the 2016 paper proposing LIME, which explains an individual prediction by learning an interpretable model locally around it.2
Beyond Accuracy: Behavioral Testing of NLP models with CheckList Published research
Co-author with Marco Tulio Ribeiro, Tongshuang Wu, and Carlos Guestrin of the 2020 CheckList paper on behavioral testing of NLP models.3
Timeline
- Began a postdoctoral position at the University of Washington.1
- Joined UC Irvine as an assistant professor of computer science.1
- Co-authored the LIME paper on explaining the predictions of any classifier.2
- Co-authored CheckList, a behavioral testing method for NLP models.3
- Became an associate professor at UC Irvine and an Allen Fellow at the Allen Institute for AI.1
- Became a professor of computer science at UC Irvine.1
Other documented roles
Roles the sources document that are not confirmed as current.
- Allen Fellow (2021–2023), Allen Institute for AI1
- Postdoctoral Researcher (2013–2016), University of Washington1
Honors and recognition
- DARPA Riser · DARPA, 20154
- Best Paper Award, ACL 2020, for CheckList · Association for Computational Linguistics, 20204
- Hellman Fellowship · Hellman Family Foundation, 20204
- NSF CAREER Award · U.S. National Science Foundation, 20214
- Best Task Paper Award, NAACL 2022, for FRUIT · North American Chapter of the Association for Computational Linguistics, 20224
- Kavli Fellow · National Academy of Sciences (Kavli Frontiers of Science), 20234
Where they work
Related people
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- Ian R. McKenzieEvaluation and benchmarks · Language models
- Emmanuel DupouxLanguage models · Evaluation and benchmarks
- Yulia TsvetkovLanguage models · Evaluation and benchmarks
- Margaret MitchellEvaluation and benchmarks · Language models
- Sarthak JainLanguage models · Interpretability
- Samuel R. BowmanEvaluation and benchmarks · Language models
- Kate SouleLanguage models · Evaluation and benchmarks
- Su Lin BlodgettEvaluation and benchmarks · Language models
- Ethan PerezEvaluation and benchmarks · Language models
Suggest a correction or removal
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References
4 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026