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
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.
PaperPaper: AppendixSlides
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.
PaperSlides
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.
PaperSlides
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