Ansaf Salleb-Aouissi is interested in machine learning, data science, and artificial intelligence in general. She has done research on frequent patterns mining, rule learning, and action recommendation and has collaborated with industry on data science projects including knowledge discovery in geo-spatial data and machine learning for the power grid. Her current research interest includes crowd sourcing, medical informatics, and data science for education.
Salleb-Aouissi’s specific and recent research interest is interdisciplinary and consists in leveraging advanced machine learning methods and large amounts of data to study medical problems, such as premature birth and infantile colic. Salleb-Aouissi cares about education and works toward advancing research on online self-learning and building advanced tools for auto-grading, self-testing, and providing support to students in computer science and mathematics. She has published several peer-reviewed papers in top quality venues including JMLR, TPAMI, ECML, PKDD, COLT, IJCAI, ECAI and AISTAT.
- 2004-2005: Postdoctoral research fellow, French National Institute of Computer Science and Control (INRIA) Rennes, France. INRIA, 2004-2005
- Lecturer in discipline of computer science, Columbia University, 2015-
- Associate research scientist, Center for Computational Learning Systems (CCLS) Columbia University, 2006-2015
- Adjunct assistant professor of computer science, Columbia University, 2014-2015
- Adjunct assistant professor and researcher, Computer Science Department and Laboratoire d’Informatique Fondamentale d’Orléans (LIFO), University of Orléans, 2002-2004
- Teaching assistant, Computer Science Department and Laboratoire d’Informatique Fondamentale d’Orléans (LIFO), University of Orléans, 1999-2002
- Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja, Axinia Radeva, Ashwath Rajan, Alex Rybchuk, Thomas Koch, Yiwen Huang, Hatim Diab, Ashish Tomar, and Ronald Wapner. Using Kernel Methods and Model Selection for Prediction of Preterm Birth. Machine Learning for Healthcare 2016 JMLR conference track proceedings.
- Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Daniel Cassard QuantMiner for Mining Quantitative Association Rules. Journal of Machine Learning Research Open source software. 14(Oct):3153-3157, 2013.
- R. Trepos, Ansaf Salleb-Aouissi, M-O. Cordier, V. Masson and c. Gascuel Building actions from classification rules Knowledge and Information Systems: Volume 34, Issue 2 (2013), Page 267-298.
- Cynthia Rudin, David Waltz, Roger Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome Machine Learning for the New York City Power Grid," in the IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 328-345, Feb. 2012.
- B. Duval, Ansaf Salleb-Aouissi, C. Vrain. On the Discovery of Exception Rules: A Survey. Reviewed Book chapter in "Quality Measures in Data Mining" book. In F. Guillet and Howard J. Hamilton editors Springer's Lecture Notes in Artificial Intelligence, Volume 43/2007 pp. 77- 98. Springer January 2007.
- Rebecca J. Passonneau, Vikas Bhardwaj, Ansaf Salleb-Aouissi and Nancy Ide. Multiplicity and Word Sense: Evaluating and Learning from Multiply Labeled Word Sense Annotations. Language Resources and Evaluation 46(2): 219-252 (2012).
- Application of Sentiment and Topic Analysis to Teacher Evaluation Policy in the U.S. Educational Data Mining (EDM) conference 2015.
- Learning Characteristic Rules in Geographic Information Systems. The 9th International Web Rule Symposium (RuleML) 2015.
- Ilia Vovsha , Ashwath Rajan , Ansaf Salleb-Aouissi, Anita Raja , Axinia Radeva, Hatim Diab, Ashish Tomar and Ronald Wapner Predicting preterm birth is not elusive: machine learning paves the way to individual wellness. AAAI Spring Symposium – Big Data Becomes Personal: Knowledge into Meaning - For Better Health, Wellness and Well-Being., 2014.
- Faiza Khan Khattak and Ansaf Salleb-Aouissi Robust Crowd Labeling using Little Expertise Proceedings of the Sixteenth International Conference on Discovery Science DS 2013, LNAI 8140, pp. 94-109, 2013.