Kenny Chowdhary
Postdoctoral Researcher
Sandia National Laboratories
Phone: (732) 619 – 7319
E-mail: kennychowdhary at gmail
Website: www.kennychowdhary.me
EDUCATION:
Brown University, Providence, RI, USA
Ph.D., Applied Mathematics, May 2012
Sc.M., Applied Mathematics, May 2007
New York University, New York, NY, USA
B.A., Mathematics (magna cum laude), May 2004
RESEARCH INTERESTS:
Probability, statistics, machine learning, uncertainty quantification, scientific computing: Bayesian inference, maximum entropy methods, MCMC techniques, sparse covariance estimation, principle component analysis, imputation, compressed sensing, numerical optimization, high-dimensional integration.
COMPUTATIONAL SKILLS:
Operating Systems: Debian GNU/Linux, Mac OSX, Windows
Software/ Programming: Python, MATLAB, C++.
WORK/ RESEARCH EXPERIENCE:
Sandia National Laboratories, Livermore, CA, USA, July 2012 – Present
Postdoctoral Researcher
• Bayesian Inference with Missing Data: Developing probabilistic algorithms for the inference of missing data from indirect measurements.
• Covariance Estimation and Principle Component Analysis (PCA): Researching and implementing techniques for the estimation of covariance matrices for high-dimensional random fields with limited sample size.
• Propagating Uncertainties in Climate Models: Developing and implementing fast and accurate techniques for the quantification of uncertain cloud physics in atmospheric climate models.
Brown University, Providence, RI, USA, Sept 2008 – May 2012
Brown Graduate Researcher
• Uncertainty Quantification: Exploring robust techniques to model systems with mixed degrees of uncertainties, in order to evaluate bounds on quantities of interest, e.g. failure probabilities.
• Compressed Sensing: Applying sparse signal/image processing techniques and convex optimization algorithms for inverse problems in medical imaging.
Raytheon BBN Technologies, Newport, RI, USA, June 2009 – Dec 2010
Brown Graduate Intern
• Implementation and design of computational wave propagation models using high-order, spectral method solvers.
PUBLICATIONS:
Journal Articles
• K. Chowdhary, H. Najm. Bayesian inference with Processed Data Products. In prepa- ration, 2013.
• K. Chowdhary, P. Dupuis. Distinguishing and integrating aleatoric and epistemic variation in uncertainty quantification. http://arxiv.org/abs/1103.1861. Accepted to ESAIM:Mathematical Modelling and Numerical Analysis, 2012.
• K. Chowdhary, J. Hesthaven, and E. Walsh. Compressed Sensing for fMRI with Fourier Edge Detection Constraints. In preparation, 2013.
Conference Papers and Reports
• M. Brake, J. Massad, R. Smith, K. Chowdhary, et al. Uncertainty enabled design of an acceleration switch. IMECE 2011 Conference Proceedings.
• K. Bongiovanni, K. Chowdhary, and M. Soyka. Infrasound Broadband Pulse Propagation in a Moving Inhomogeneous Medium. Raytheon BBN Technologies Technical Report, 2010.
TEACHING:
Division of Applied Mathematics, Brown University
• Instructor for Operations Research: Deterministic Methods, Sept – Dec 2010
• Instructor for Four week short course on Scientific Computing, July 2010
PRESENTATIONS:
• SIAM Computational Science & Engineering, Boston, MA, March 2013
Bayesian Inference with Processed Data Products
• Massachusetts Institute of Technology (MIT), Cambridge, MA, April 2012
Aleatoric and Epistemic Variation in Uncertainty Quantification
• Naval Undersea Warfare Center, Newport, RI, July 2011
Aleatoric and Epistemic Variation in Uncertainty Quantification
Brown University, Providence, RI, USA, Feb – Nov 2011
• Introduction to Compressed Sensing: Sparse Signal Reconstruction
• Aleatoric and Epistemic Variation in Uncertainty Quantification
• Raytheon BBN Technologies, Newport, RI
Introduction to Compressed Sensing: Sparse Signal Reconstruction
AWARDS AND GRANTS:
Brown University
• David Gottlieb Memorial Prize for excellence in graduate studies, May 2012
Brown University
• Division of Applied Mathematics, Graduate Fellowship, Sept 2006 – May 2007
New York University
• Perley Lenwood Thorne Award for outstanding scholarship in mathematics, May 2004
