I am interested in nonparametric statistics and large-scale optimization methods for machine learning and statistical estimation problems. Prof. Ryan Tibshirani is my advisor.

Here is my CV.

Papers

• Additive Models with Trend Filtering
  Veeranjaneyulu Sadhanala, Ryan Tibshirani.
  To appear, Annals of Statistics.
• A Higher-order Kolmogorov-Smirnov Test
  Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan Tibshirani.
  Oral Presentation. To appear, International Conference on Artifical Intelligence and Statistics, 2019.
• Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods, Supplement
  Veeranjaneyulu Sadhanala*, Yu-Xiang Wang*, James Sharpnack, Ryan Tibshirani.
  Neural Information Processing Systems, 2017.
• Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
  Veeranjaneyulu Sadhanala*, Yu-Xiang Wang*, Ryan Tibshirani.
  Neural Information Processing Systems, 2016.
• Graph Sparsification Approaches for Laplacian Smoothing, Supplement
  Veeranjaneyulu Sadhanala*, Yu-Xiang Wang*, Ryan Tibshirani.
  International Conference on Artificial Intelligence and Statistics, 2016.
• Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms
  Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, and Eric Xing.
  International Conference on Machine Learning, 2016
• Scheduling of dataflow models within the reconfigurable video coding framework.
  Jani Boutellier , Veeranjaneyulu Sadhanala , Christophe Lucarz , Philip Brisk , Marco Mattavelli.
  IEEE Workshop on Signal Processing Systems, 2008.

Software

• R package glmgen

Previously...

I graduated from IIT Bombay, CSE department in 2009 and worked as a quantitative analyst with the interest rate trading desk at Morgan Stanley until 2013.