Rada Mihalcea

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
Born
Cluj-Napoca, Romania
EducationTechnical University of Cluj-Napoca (1992), Southern Methodist University (1999,2001), Oxford University (2010)
OccupationProfessor at University of Michigan
Known for

Career

Mihalcea has a Ph.D. in Computer Science and Engineering from Southern Methodist University (2001) and a Ph.D. in Linguistics, Oxford University (2010).[1] In 2017 she was named Director of the Artificial Intelligence Laboratory at University of Michigan, Computer Science and Engineering. In 2018, Mihalcea was elected as new VP for the Association for Computational Linguistics (ACL). 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.[2]

Mihalcea has published over 220 articles since 1998 on topics ranging from semantic analysis of text to lie detection.[3]

In 2008, Mihalcea received the Presidential Early Career Award for Scientists and Engineers (PECASE)[4] She is an ACM Fellow (since 2019) and AAAI Fellow (since 2021).

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 behaviour, to include student life, personality and background outside of the classroom.[5] 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.[6][7][8]

Awards

Research

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.[13] In a study of video clips of high profile court cases, a computer was more accurate at detecting deception than human judges.[14][15][16]

Mihalcea's lie-detection software uses machine learning techniques to analyze video clips of actual trials.[17] 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.[18] 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.[18][19] 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.[20] She notes there are still improvements to be made — in particular to account for cultural and demographic differences.[18] 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.[21]

In 2018, Mihalcea and her collaborators 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%.[22]

Publications

Books

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
  • R Mihalcea (2007). "Using wikipedia for automatic word-sense disambiguation". Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference. CiteSeerX 10.1.1.74.3561. - see also Word-sense disambiguation
  • 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

Personal life

Mihalcea was born in Cluj-Napoca, Romania, where she attended the Technical University of Cluj-Napoca.

She can speak Romanian, English, Italian, and French.

Mihalcea has two children - Zara (b. 2009) and Caius (b. 2013). They were both born in Dallas, Texas.

She is married to an associate professor of engineering at the University of Michigan–Flint - Mihai Burzo. They met while they were both completing Ph.D.s at Southern Methodist University in 2001[23] and have often collaborated on research,[24] such as the 2015 study on lie detection.[20]

References
  1. "The Language of Humor, PhD Dissertation". Oxford University. Retrieved 2021-02-13.
  2. "Language Information and Technologies". lit.eecs.umich.edu. Retrieved 2019-03-07.
  3. "Rada Mihalcea". Semantic Scholar. Retrieved 2017-08-30.
  4. "President Honors Outstanding Early-Career Scientists". National Science Foundation. Retrieved 2017-08-30.
  5. "U Michigan MIDAS Program Backs Student Success Research". Campus Technology. Retrieved 2016-06-23.
  6. "Girls Encoded". girlsencoded.eecs.umich.edu. Retrieved 2019-03-07.
  7. "Making a difference for women in academia". University of Michigan EECS. Retrieved 2019-03-07.
  8. "A champion for women in computer science". University of Michigan EECS. Retrieved 2019-03-07.
  9. 2019 ACM Fellows Recognized for Far-Reaching Accomplishments that Define the Digital Age, Association for Computing Machinery, retrieved 2019-12-11
  10. "Sarah Goddard Power Award". The University Record. Retrieved 2019-03-07.
  11. "Carol Hollenshead Award | Center for the Education of Women | University of Michigan". www.cew.umich.edu. Retrieved 2019-03-07.
  12. "President Honors Outstanding Early-Career Scientists | NSF - National Science Foundation". www.nsf.gov. Retrieved 2019-03-07.
  13. "Researchers Develop New Lie-Detecting Software". Topnews.in. Retrieved 2015-12-16.
  14. "Can you spot a liar? Fail safe ways to determine if someone is telling the truth". New Zealand Herald. Retrieved 2017-01-30.
  15. "New Developed Software can detect lie with %75 success – Baltimore News". Albany Daily Star. Retrieved 2016-08-17.
  16. "To spot a liar, look at their hands". Quartz. 12 December 2015. Retrieved 2015-12-12.
  17. "Courtroom fibs used to develop lie-detecting software". Gizmag. 2015-12-12. Retrieved 2015-12-12.
  18. "University professors create new software to detect lies". Michigan Daily. 10 December 2015. Retrieved 2015-12-11.
  19. "Liar, Liar Pants On Fire: 6 Signs Computers Use To Spot Liars With 75% Accuracy". Medical Daily. 2015-12-15. Retrieved 2015-12-16.
  20. "5 Ways to Tell If Someone is Lying to You". Yahoo! Health. 15 December 2015. Retrieved 2015-12-15.
  21. "New software analysis words, gestures to detect lies". Jagran Post. Retrieved 2015-12-11.
  22. "Fake news detector algorithm works better than a human". University of Michigan News. 2018-08-21. Retrieved 2019-03-26.
  23. "Episode 31: From Romania – Immigrant Computer Scientists Podcast". Retrieved 2023-03-19.
  24. "Mihai Burzo's research works | University of Michigan".
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