I am a Ph.D. student at the Center for Data Science at New York University, advised by Prof. Carlos Fernandez-Granda and Prof. Eero P Simoncelli. I am a member of the Laboratory for Computational Vision and Math and Data. I am broadly interested in computer vision, deep learning and signal processing.

Previously, I obtained my B.Tech in Electrical Engineering from Indian Institute of Technology Madras. Here I was advised by Prof. Kaushik Mitra with whom I worked on computational photography. I was also a research intern at Google (with Dr. Aamir Anis and Dr. Yeping Su), IST Austria (with Dr. Gasper Tkacik), and Flatiron Institute (with Dr. Dmitri Chklovskii and Prof. Cengiz Pehlevan). Here’s my CV.

Publications and Preprints

• Adaptive Denoising via GainTuning.
  Sreyas Mohan, Joshua L. Vincent,Ramon Manzorro, Peter A. Crozier, Carlos Fernandez-Granda, Eero P. Simoncelli
  [ Paper ]
• Unsupervised Deep Video Denoising
  Dev Sheth^*, Sreyas Mohan^*, Joshua L. Vincent,Ramon Manzorro, Peter A. Crozier, Mitesh Khapra, Eero P. Simoncelli, Carlos Fernandez-Granda
  Proc. IEEE International Conference on Computer Vision (ICCV), 2021
  [ Paper ] [ Website ]
• Developing and Evaluating Deep Neural Network-based Denoising for Nanoparticle TEM Images with Ultra-low Signal-to-Noise.
  Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
  Microscopy and Microanalysis, 27(S1), 262-264, 2021.
  [ Paper ]
• Perturbation CheckLists for Evaluating NLG Evaluation Metrics.
  Ananya B. Sai, Tanay Dixit, Dev Sheth, Sreyas Mohan, Mitesh Khapra
  Proc. Empirical Methods in Natural Language Processing (EMNLP), 2021
  [ Paper ] [ Website ] [ Code ]
• Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy.
  Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
  [ Paper ] [ Website ] [ Code and Dataset ]
• Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization.
  Aakash Kaku^*, Sreyas Mohan^*, Avinash Parnandi, Heidi Schambra, Carlos Fernandez-Granda
  [Paper]
• Robust and Interpretable Blind Image Denoising via Bias-free Convolutional Neural Networks.
  Sreyas Mohan^*, Zahra Kadkhodaie^*, Eero Simoncelli, Carlos Fernandez-Granda
  Proc. International Conference on Learning Representations (ICLR), 2020
  Solving Inverse Problems with Deep Networks Workshop, Neural Information Processing Systems (NeurIPS), Vancouver (Canada) 2019
  [ Paper ] [ Website ] [ Code ]
• Data-driven Estimation of Sinusoid Frequencies.
  Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda
  Proc. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver (Canada) 2019
  [ Paper ] [ Website ] [ Code ]
• Automatic Knee Segmentation using Diffusion Weighted MRI.
  Alejandra Duarte ^*, Chaitra Hegde ^*, Aakash Kaku ^*, Sreyas Mohan^*, Jose G Raya
  Medical Imaging Meets NeurIPS, Neural Information Processing Systems (NeurIPS), Vancouver (Canada) 2019 (Extended Abstract)
  [ Paper ] [ Code ]
• Data Driven Coded Aperture Design for Depth Recovery.
  Prasan Shedligeri, Sreyas Mohan, Kaushik Mitra
  IEEE International Conference on Image Processing (ICIP), Sep 2017, Beijing, China.
  [ Paper ] [ Website ]
• Blind Nonnegative Source Separation Using Biological Neural Networks.
  Cengiz Pehlevan, Sreyas Mohan, Dmitri B. Chklovskii
  Neural computation, vol. 29, no. 11, pp. 2925–2954, 2017
  COSYNE, Salt Lake City, Utah, 2017 (Extended Abstract)
  [ Paper ] [ Extended Abstract] [ Video Demo ]

Teaching

At NYU

• DS-GA 1013 Mathematical Tools for Data Science (Spring 2020) for Prof. Carlos Fernandez-Granda
• DS-GA 1011 Natural Language Processing with Representation Learning (Fall 2019) for Prof. Kyunghyun Cho
• DS-GA 1003 Machine Learning (Spring 2019) for Prof. Julia Kempe and Dr. David Rosenberg.
• CSCI-GA.1170 Fundamental Algorithms (Summer, Fall 2018) for Prof. Alexander Alekseyev

Elsewhere

• Kigali, Rwanda: NLP with Deep Learning at the African Institute of Mathematical Sciences, Rwanda. (March 2019, 2020).
• IIT Madras, India: EE5177 Machine Learning for Computer Vision (Spring 2017) for Prof. Kaushik Mitra
• Chennai, India: IIT for Villages - Volunteered to teach Physics and Mathematics for Higher Secondary School students from underprivileged sections of the society (2013-2017)

Service

• Reviewer for ICLR (2020, 2021), NeurIPS (2020, 2021), ICML (2021), CVPR (2020), and Journal of Electronic Imaging (JEI)