Before joining DeepMind, I was a PhD student at Gatsby Unit, CSML, University College London, working with Yee Whye Teh. Between 2008-10, I was a Masters student at Oregon State University, working with Raviv Raich as part of the Bioacoustics research group. During my undergrad, I was part of the Integrated Systems laboratory, where I worked on communication subsystems for the Anna University Microsatellite (ANUSAT) project.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel and Charles Blundell
Note: A version of this work was presented at the NIPS workshop on Bayesian deep learning, 2016.
Decision trees and forests: a probabilistic perspective
Ph.D. thesis, University College London, 2016
Distributed Bayesian learning with stochastic natural-gradient expectation propagation and the posterior server
Yee Whye Teh, Leonard Hasenclever, Thibaut Lienart, Sebastian Vollmer, Stefan Webb, Balaji Lakshminarayanan and Charles Blundell
Kernel-based just-in-time learning for passing expectation propagation messages
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic and Zoltan Szabo
Latent IBP compound Dirichlet allocation
Cedric Archambeau, Balaji Lakshminarayanan, and Guillaume Bouchard
TPAMI special issue on Bayesian Nonparametrics, 2015
[pdf] [code available upon request] [IEEE link]
Note: A short version of this work appeared at the NIPS workshop on Bayesian nonparametrics, 2011.
Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
Forrest Briggs, Balaji Lakshminarayanan, Lawrence Neal, Xiaoli Z. Fern, Raviv Raich, Sarah J. K. Hadley, Adam S. Hadley, and Matthew G. Betts
Robust Bayesian matrix factorization
Balaji Lakshminarayanan, Guillaume Bouchard, and Cedric Archambeau
[revised pdf] [code available upon request]
Note: the updates for a_n, c_m were wrong in the original version of the pdf. The increments ought to be ell_n and ell_m respectively instead of 1.
Probabilistic models for classification of bioacoustic data
M.S. thesis, Oregon State University, 2010
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Ph.D. in Machine Learning, University College London, London, UK
M.S., Oregon State University, Corvallis, USA
Sep 2015 - present: Research scientist at Google DeepMind, London, UK.
Summer 2009: Center for Advanced Research, PricewaterhouseCoopers, San Jose, USA.
Teaching assistant for Probabilistic and Unsupervised Learning course (2012) at Gatsby Unit, University College London
Teaching assistant for Statistical Machine Learning and Data Mining course (2014) at Dept of Statistics, University of Oxford