About

Rajesh Ranganath
I am an Assistant Professor at the Courant Institute at NYU in Computer Science and at the Center for Data Science (affiliate). I am also part of the CILVR group. My research interests center on easy-to-use probablistic inference, understanding the role of randomness and information in model building, and machine learning for healthcare. Before joining NYU, I completed my PhD at Princeton working with Dave Blei and my undergraduate at Stanford both in computer science. I have also spent time as a research affiliate at MIT’s Institute for Medical Engineering and Science.

Papers

  • Identifying potentially induced seismicity and assessing statistical significance in Oklahoma and California. Mark McClure, Riley Gibson, Kit‐Kwan Chiu, and Rajesh Ranganath. Journal of Geophysical Research: Solid Earth 2017. [html]

  • Variational Inference via Chi Upper Bound Minimization. Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, and David M. Blei. NIPS 2017. [pdf]

  • Hierarchical Implicit Models and Likelihood-Free Variational Inference. Dustin Tran, Rajesh Ranganath, and David M. Blei. NIPS 2017. [pdf]

  • Correlated Random Measures. Rajesh Ranganath and David M. Blei. JASA 2017. [fulltext] [arxiv]

  • Automatic Differentiation Variational Inference. Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei. JMLR 2017. [pdf]

  • Operator Variational Inference. Rajesh Ranganath, Jaan Altosaar, Dustin Tran, and David M. Blei. NIPS 2016. [pdf]

  • Deep Survival Analysis. Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. MLHC 2016. [pdf]

  • Hierarchical Variational Models. Rajesh Ranganath, Dustin Tran, and David M. Blei. ICML 2016. [pdf]

  • Variational Tempering. Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, and David M. Blei. AISTATS 2016. [pdf]

  • The Variational Gaussian Process. Dustin Tran, Rajesh Ranganath, and David M. Blei. ICLR 2016. [pdf]

  • The Population Posterior and Bayesian Modeling on Streams. James McInerney, Rajesh Ranganath, and David M. Blei. NIPS 2015. [pdf]

  • Automatic Variational Inference in Stan. Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, and David M. Blei. NIPS 2015. [pdf]

  • Dynamic Poisson Factorization. Laurent Charlin, Rajesh Ranganath, James McInerney, and David M. Blei. RecSys 2015. [pdf]

  • The Survival Filter: Joint Survival Analysis with a Latent Time Series. Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. UAI 2015. [pdf]

  • Risk Prediction for Chronic Kidney Disease Progression Using Heterogeneous Electronic Health Record Data and Time Series Analysis. Adler Perotte, Rajesh Ranganath, Jamie Hirsch, David M. Blei, and Noemie Elhadad. JAMIA 2015. [html]

  • Deep Exponential Families. Rajesh Ranganath, Linpeng Tang, Laurent Charlin, and David M.Blei. AISTATS 2015. [pdf] [supplement]

  • Hierarchical Topographic Factor Analysis. Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, and David M. Blei. IEEE Xplore, 4th International Workshop on Pattern Recognition in Neuroimaging. [pdf]

  • Bayesian nonparametric Poisson factorization for recommendation systems. Prem Gopalan, Francisco JR Ruiz, Rajesh Ranganath, and David M. Blei. AISTATS 2014. [pdf]

  • Black Box Variational Inference. Rajesh Ranganath, Sean Gerrish, and David M. Blei. AISTATS 2014. [pdf] [supplement]

  • Topographic Factor Analysis: a Bayesian model for inferring brain networks from neural data. Jeremy R. Manning, Rajesh Ranganath, Kenneth A. Norman, and David M. Blei. Plos One 9(5) 2014. [pdf]

  • An Adaptive Learning Rate for Stochastic Variational Inference. Rajesh Ranganath, Chong Wang, David M. Blei, and Eric P. Xing. ICML 2013. [pdf]

  • Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Rajesh Ranganath, Dan Jurafsky, and Daniel A. McFarland. Computer Speech and Language. vol. 27, no. 1, pp. 89-115, 2013, doi:10.1016/j.csl.2012.01.00. [fulltext]

  • Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. Communications of the ACM, vol. 54, no. 10, pp. 95-103, 2011. (Research Highlights) [pdf]

  • It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates. Rajesh Ranganath, Dan Jurafsky, and Dan McFarland. EMNLP 2009 [pdf]

  • Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation. Dan Jurafsky, Rajesh Ranganath, and Dan McFarland. NAACL HLT 2009 (Research Highlights) [pdf]

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng. ICML 2009 (Best paper award: Best application paper) [pdf]

Teaching

Contact

NYU Courant Institute of Mathematical Science
Computer Science
60 Fifth Ave
New York, NY 10011
rajeshr at cims dot nyu dot edu