Affiliation
IBM Research, T.J. Watson Research Center
Research Staff Member

Research Interest
My current research focus is healthcare informatics - primarily, applications of machine learning and AI to real-world and big health data. I am particulary interested in applying AI/ML for interpretable and scalable models for population and precision health. Some of my core research interests are temporal data mining, data assimilation/augmentation, and multi-source fusion. My Ph.D. thesis, advised by Dr. Naren Ramakrishnan, was on multivariate temporal data mining in the presence of weakly correlated signals towards public health surveillance

Contact Me
| Email | Linkedin | Find me on Github

Activities

News
  • I am co-organizing KDD workshop on Machine Learning for Medicine and Healthcare (MLMH 2018)
  • PC member for IJCAI 2018
  • Technical Talk at P. Chakraborty, "Data science made easy in Jupyter notebooks using PixieDust and InsightFactory", JupyterCon 2017. Talk
Patents
  • M. Marwah, M. Arlitt, P. Chakraborty, N. Ramakrishnan, "Predicting near-future photovoltaic generation", Sept. 28 2012. US Patent App. 13/631,480
Invited Talks
  • P. Chakraborty, "Data Driven Model for Disease Forecasting", Invited Talk, BCDE 2014. Slide

Selected Publications

  • P. Chakraborty, et al., (2017). "A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure", Presented to NIPS ML4H 2017, US, December, 2017.
    Paper

  • S. Ghosh, P. Chakraborty, et al., (2017). "Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks", Scientific reports 7, 40841.
    Paper

  • S. Ghosh, P. Chakraborty, et al., (2017). "GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources", In Proc. of KDD 2017, CAN, August, 2017, pp. 1477-1485.
    Paper

  • F. Tabataba, P. Chakraborty, et al., (2017). "A framework for evaluating epidemic forecasts", BMC infectious diseases 17 (1), 345
    Paper

  • P. Chakraborty, et al., (2016). "Hierarchical Quickest Change Detection via Surrogates", arXiv preprint arXiv:1603.09739.
    Paper

  • Z. Weng, P. Chakraborty, et al., (2015). "Dynamic Poisson Autoregression for Influenza-Like-Illness Case Count Prediction", In Proc. of KDD 2015, AUS, August, 2014, pp. 1285–1294.
    Paper Paper: Appendix Slides

  • P. Chakraborty, N. Ramakrishnan, et al., (2014). "Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions", In Proc. of SDM 2014, USA, April, 2014, pp. 262–270.
    Paper Slides

  • P. Khadivi, P. Chakraborty, R. Tandon, N. Ramakrishnan, (2015). " Time Series Forecasting via Noisy Channel Reversal", In Proc. of MLSP 2012, USA, 2015.
    Paper

  • P. Chakraborty, N. Ramakrishnan, et al., (2012). "Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble", In Proc. of AAAI 2012, CA, July, 2012.
    Paper Slides

  • P. Butler, P. Chakraborty, N. Ramakrishnan, (2012). "The Deshredder: A visual analytic approach to reconstructing shredded documents", In Proc. of VAST 2012, CA, July, 2012.
    Paper
All Publications