|Education||Technical University of Cluj-Napoca (1992), Southern Methodist University (1999,2001), Oxford University (2010)|
|Occupation||Professor at University of Michigan|
Rada Mihalcea is a professor of computer science and engineering at the University of Michigan. Her research focuses on natural language processing, multimodal processing, and computational social science.
Rada Mihalcea has a Ph.D. in Computer Science and Engineering from Southern Methodist University (2001) and a Ph.D. in Linguistics, Oxford University (2010). In 2017 she was named Director of the Artificial Intelligence Laboratory at University of Michigan, Computer Science and Engineering. In 2018 Rada Mihalcea was elected as new VP for the Association for Computational Linguistics (ACL). Currently she is a Professor of Computer Science and Engineering at the University of Michigan, where she also leads the Language and Information Technologies (LIT) Lab.
Mihalcea has published over 220 articles since 1998 on topics ranging from semantic analysis of text to lie detection. President Barack Obama granted her the Presidential Early Career Award for Scientists and Engineers in 2008.
Mihalcea is an outspoken promoter of diversity in computer science. She also supports an expansion of the traditional analysis of educational success, which tends to focus on academic behavior, to include student life, personality and background outside of the classroom. Mihalcea leads Girls Encoded, a program designed to develop the pipeline of women in computer science as well as to retain the women who have entered into the program.
Sarah Goddard Power Award, 2019.
Carol Hollenshead Award, 2018.
Honorary Citizen of Cluj-Napoca, Romania, 2013.
Presidential Early Career Award for Scientists and Engineers (PECASE), 2009. Awarded by President Barack Obama.
Rada Mihalcea is known for her research in natural language processing, multimodal processing, computational social sciences.
In a collaboration she leads at the University of Michigan, Mihalcea has created software that can detect human lying. In a study of video clips of high profile court cases, a computer was more accurate at detecting deception than human judges.
Mihalcea's lie-detection software uses machine learning techniques to analyze video clips of actual trials. In her 2015 study, the team used clips from The Innocence Project, a national organization that works to reexamine cases where individuals were tried without the benefit of DNA testing with the aim of exonerating wrongfully convicted individuals. After identifying common human gestures, they transcribed the audio from the video clips of trials and analyzed how often subjects labeled deceptive used various words and phrases. The system was 75% accurate in identifying which subjects were deceptive among 120 videos. That puts Mihalcea’s algorithm on par with the most commonly accepted form of lie detection, polygraph tests, which are roughly 85 percent accurate when testing guilty people and 56 percent accurate when testing the innocent. She notes there are still improvements to be made — in particular to account for cultural and demographic differences. A possibly unique advantage of Mihalcea's study was the real world, high stakes nature of the footage analyzed in the study. In laboratory experiments, it is difficult to create a setting that motivates people to truly lie.
In 2018 Rada Mihalcea and her collaborators also worked on an algorithm-based system that identifies linguistic cues in fake news stories. It successfully found fakes up to 76% of the time, compared to a human success rate of 70%.
Freakonomics Radio Live: “We Thought of a Way to Manipulate Your Perception of Time.”
Panel examines strategies for detecting, regulating fake news. The Michigan Daily.
2016 MIDAS Symposium. Learning Analytics with a Personal Touch.
- Rada Mihalcea and Dragomir Radev, Graph-based Natural Language Processing and Information Retrieval, Cambridge U. Press, 2011.
- Gabe Ignatow and Rada Mihalcea, Text Mining: A Guidebook for the Social Sciences, SAGE, 2016.
Journals and conferences
- Textrank: Bringing order into text. R. Mihalcea, P. Tarau. Proceedings of the 2004 conference on empirical methods in natural language processing. 2004
- Corpus-based and knowledge-based measures of text semantic similarity. R. Mihalcea, C. Corley, C. Strapparava. AAAI 6, 775-780. 2006
- Wikify!: linking documents to encyclopedic knowledge. R. Mihalcea, A. Csomai. Proceedings of the sixteenth ACM conference on Conference on information and information management. 2007
- Learning to identify emotions in text. C. Strapparava, R. Mihalcea. Proceedings of the 2008 ACM symposium on Applied computing, 1556-1560. 2008
- Semeval-2007 task 14: Affective text. C. Strapparava, R. Mihalcea. Proceedings of the Fourth International Workshop on Semantic Evaluations. 2007
- Learning multilingual subjective language via cross-lingual projections. R. Mihalcea, C. Banea, J. Wiebe. Proceedings of the 45th annual meeting of the association of computational linguistics. 2007
- Graph-based ranking algorithms for sentence extraction, applied to text summarization. R. Mihalcea. Proceedings of the ACL Interactive Poster and Demonstration Sessions. 2004
- Falcon: Boosting knowledge for answer engines. S. Harabagiu, D. Moldovan, M. Pasca, R. Mihalcea, M. Surdeanu, Razvan Bunescu, Roxana Girju, Vasile Rus, Paul Morarescu. TREC 9, 479-488. 2000
- Measuring the semantic similarity of texts. C. Corley, R. Mihalcea. Proceedings of the ACL workshop on empirical modeling of semantic equivalence and entailment. 2005
- Using wikipedia for automatic word sense disambiguation. R Mihalcea. Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference. 2007
- Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. R. Sinha, R. Mihalcea. International Conference on Semantic Computing (ICSC 2007), 363-369. 2007
- "Language Information and Technologies". lit.eecs.umich.edu. Retrieved 2019-03-07.
- "Rada Mihalcea". Semantic Scholar. Retrieved 2017-08-30.
- "President Honors Outstanding Early-Career Scientists". National Science Foundation. Retrieved 2017-08-30.
- "U Michigan MIDAS Program Backs Student Success Research". Campus Technology. Retrieved 2016-06-23.
- "Girls Encoded". girlsencoded.eecs.umich.edu. Retrieved 2019-03-07.
- "Making a difference for women in academia". University of Michigan EECS. Retrieved 2019-03-07.
- "A champion for women in computer science". University of Michigan EECS. Retrieved 2019-03-07.
- "Sarah Goddard Power Award". The University Record. Retrieved 2019-03-07.
- "Carol Hollenshead Award | Center for the Education of Women | University of Michigan". www.cew.umich.edu. Retrieved 2019-03-07.
- "President Honors Outstanding Early-Career Scientists | NSF - National Science Foundation". www.nsf.gov. Retrieved 2019-03-07.
- "Researchers Develop New Lie-Detecting Software". Topnews.in. Retrieved 2015-12-16.
- "Can you spot a liar? Fail safe ways to determine if someone is telling the truth". New Zealand Herald. Retrieved 2017-01-30.
- "New Developed Software can detect lie with %75 success – Baltimore News". Albany Daily Star. Retrieved 2016-08-17.
- "To spot a liar, look at their hands". Quartz. Retrieved 2015-12-12.
- "Courtroom fibs used to develop lie-detecting software". Gizmag. Retrieved 2015-12-12.
- "University professors create new software to detect lies". Michigan Daily. Retrieved 2015-12-11.
- "Liar, Liar Pants On Fire: 6 Signs Computers Use To Spot Liars With 75% Accuracy". Medical Daily. Retrieved 2015-12-16.
- "5 Ways to Tell If Someone is Lying to You". Yahoo! Health. Retrieved 2015-12-15.
- "New software analysis words, gestures to detect lies". Jagran Post. Retrieved 2015-12-11.
- "Fake news detector algorithm works better than a human". University of Michigan News. 2018-08-21. Retrieved 2019-03-26.