Welcome to Manali Sharma's home page

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I am a Ph.D. candidate in the Computer Science department at Illinois Institute of Technology. My research interests are in the broad areas of data mining and machine learning.

I work on active learning, where a machine learning algorithm carefully chooses instances for annotation by an expert to save time and resources, while not sacrificing from prediction accuracy.

Specifically, I work on making the active learning process more transparent, where the active learning algorithms generate in-depth and relevant explanations about the underlying data and current set of possible hypotheses, and the experts provide rich feedback while annotating instances. The explanations and rich feedback aim to reduce the time, cost, and effort of annotation for the human expert, while improving the performance of active learner.



  • Manali Sharma, Kamalika Das, Mustafa Bilgic, Bryan Matthews, David Nielsen, Nikunj Oza. "Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation" In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, 2016.[PDF][DOI]
  • Manali Sharma and Mustafa Bilgic. "Towards Learning with Feature-Based Explanations for Document Classification" In IJCAI Workshop on BeyondLabeler - Human is More Than a Labeler, 2016.[PDF]
  • Manali Sharma and Mustafa Bilgic. "Evidence-based uncertainty sampling for active learning" In Data Mining and Knowledge Discovery, 2016.[PDF][DOI]
  • Maria E. Ramirez-Loaiza, Manali Sharma, Geet Kumar, Mustafa Bilgic. "Active learning: an empirical study of common baselines" In Data Mining and Knowledge Discovery, 2016.[PDF][DOI]


  • Manali Sharma, Di Zhuang, Mustafa Bilgic. "Active Learning with Rationales for Text Classification" In North American Chapter of the Association for Computational Linguistics Human Language Technologies, 2015.[PDF]


  • Manali Sharma and Mustafa Bilgic. "Most-Surely vs. Least-Surely Uncertain" In IEEE 13th International Conference on Data Mining (ICDM), 2013.[PDF]

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