I am a fourth-year PhD student in the department of Computer Science at the University of Texas at Austin advised by Adam Klivans. My interests lie at the intersection of Theory and Machine Learning. I am specifically interested in understanding what guarantees we can give for learning deep neural networks.

Prior to this, I received my Bachelors degree form Indian Institute of Technology (IIT) Delhi majoring in Computer Science and Engineering. My bachelor thesis was advised by Parag Singla and Chetan Arora.


  1. Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps. Simon Du, Surbhi Goel. Manuscript 2018. [pdf]
  2. Learning One Convolutional Layer with Overlapping Patches. Surbhi Goel, Adam Klivans, and Raghu Meka. ICML 2018 (Long talk). [pdf]
  3. Learning Neural Networks with Two Nonlinear Layers in Polynomial Time. Surbhi Goel, Adam Klivans. Short version: NIPS Deep Learning: Bridging Theory and Practice Workshop 2017. [pdf]
  4. Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks. Surbhi Goel, Adam Klivans. NIPS 2017. [pdf]
  5. Reliably Learning the ReLU in Polynomial Time. Surbhi Goel, Varun Kanade, Adam Klivans, and Justin Thaler. Short version: NIPS OPTML Workshop 2016 (Oral Presentation). Full version: COLT 2017. [pdf]
  6. Exploiting Sum of Submodular Structure for Inference in Very High Order MRF-MAP Problems. Ishant Shanu, Surbhi Goel, Chetan Arora and Parag Singla. Manuscript 2015. [pdf]