I am a Senior Staff Research Scientist on the GenAI team at Google DeepMind, working on improving the capability and training and inference efficiency of large language models.
I received my PhD advised by Todd Zickler at Harvard University, and did my undergraduate studies at IIT Madras. Before joining Google, I was an assistant professor of computer science at WashU, a research assistant professor at the Toyota Technological Institute at Chicago, and a post-doctoral fellow at Harvard.
I am a Senior Staff Research Scientist on the GenAI team at Google DeepMind, working on improving the capability and training and inference efficiency of large language models.
I received my PhD advised by Todd Zickler at Harvard University, and did my undergraduate studies at IIT Madras. Before joining Google, I was an assistant professor of computer science at WashU, a research assistant professor at the Toyota Technological Institute at Chicago, and a post-doctoral fellow at Harvard.
Research
Analyzing Similarity Metrics for Data Selection for Language Model Pretraining
NeurIPS 2025
Dylan Sam, Ayan Chakrabarti, Afshin Rostamizadeh, Srikumar Ramalingam, Gui Citovsky, Sanjiv Kumar
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long context, and Next Generation Agentic Capabilities
arXiv 2025
Gemini Team
LatentCRF: Continuous CRF for Efficient Latent Diffusion
arXiv 2024
Kanchana Ranasinghe, Sadeep Jayasumana, Andreas Veit, Ayan Chakrabarti, Daniel Glasner, Michael S. Ryoo, Srikumar Ramalingam, Sanjiv Kumar
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs
arXiv 2024
Ankit S. Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, Rakesh Shivanna, Sashank J. Reddi, Aditya K. Menon, Rohan Anil, Sanjiv Kumar
SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection
arXiv 2024
Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation
CVPR 2024
Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
CVPR 2024
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar
Benchmarking Robustness to Adversarial Image Obfuscations
NeurIPS 2023
Florian Stimberg, Ayan Chakrabarti, Chun-Ta Lu, Hussein hazimeh, Otilia Stretcu, Wei Qiao, Yintao Liu, Merve Kaya, Cyrus Rashtchian, Ariel Fuxman, Mehmet Tek, Sven Gowal
Leveraging Redundancy in Attention with Reuse Transformers
arXiv 2021
Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation
arXiv 2021
Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit
Understanding Robustness of Transformers for Image Classification
ICCV 2021
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, Andreas Veit
Can Optical Trojans Assist Adversarial Perturbations?
AROW (ICCV) 2021
Adith Boloor, Tong Wu, Patrick Naughton, Ayan Chakrabarti, Xuan Zhang, Yevgeniy Vorobeychik
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
CVPR 2021
Zhihao Xia, Michaël Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints
SEC 2021
Ayan Chakrabarti, Roch Guérin, Chenyang Lu, Jiangnan Liu
Generating and Exploiting Probabilistic Monocular Depth Estimates
CVPR 2020 (oral)
Zhihao Xia, Patrick Sullivan, Ayan Chakrabarti
Basis Prediction Networks for Effective Burst Denoising with Large Kernels
CVPR 2020
Zhihao Xia, Federico Perazzi, Michaël Gharbi, Kalyan Sunkavalli, Ayan Chakrabarti
Protecting Geolocation Privacy of Photo Collections
AAAI 2020
Jinghan Yang, Ayan Chakrabarti, Yevgeniy Vorobeychik
Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising
WACV 2020
Zhihao Xia, Ayan Chakrabarti
Training Image Estimators without Image Ground-Truth
NeurIPS 2019 (spotlight)
Zhihao Xia, Ayan Chakrabarti
Backprop with Approximate Activations for Memory-efficient Network Training
NeurIPS 2019
Ayan Chakrabarti, Benjamin Moseley
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices
ICCAD 2019 + TCAD
Weidong Cao, Liu Ke, Ayan Chakrabarti, Xuan Zhang
Learning to Separate Multiple Illuminants in a Single Image
CVPR 2019
Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli, Aswin C. Sankaranarayanan
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
ICRA 2019
Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
NeuADC: Neural Network-Inspired RRAM-Based Synthesizable Analog-to-Digital Conversion with Reconfigurable Quantization Support
DATE 2019 + TCAD
Weidong Cao, Xin He, Ayan Chakrabarti, Xuan Zhang
Learning Privacy Preserving Encodings through Adversarial Training
WACV 2019
Francesco Pittaluga, Sanjeev J. Koppal, Ayan Chakrabarti
Stabilizing GAN Training with Multiple Random Projections
arXiv 2018
Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti
Jointly Optimizing Placement and Inference for Beacon-based Localization
IROS 2017
Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
Examining the Impact of Blur on Recognition by Convolutional Networks
arXiv 2017
Igor Vasiljevic, Ayan Chakrabarti, Gregory Shakhnarovich
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
NeurIPS 2016
Ayan Chakrabarti, Jingyu Shao, Gregory Shakhnarovich
Single-image RGB Photometric Stereo With Spatially-varying Albedo
3DV 2016 (oral)
Ayan Chakrabarti, Kalyan Sunkavalli
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
CVPR 2015
Ayan Chakrabarti, Ying Xiong, Steven J. Gortler, Todd Zickler
From Shading to Local Shape
PAMI 2015
Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J. Gortler, David W. Jacobs, Todd Zickler
Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
PAMI 2014
Ayan Chakrabarti, Ying Xiong, Baochen Sun, Trevor Darrell, Daniel Scharstein, Todd Zickler, Kate Saenko
Depth and Deblurring from a Spectrally-varying Depth-of-Field
ECCV 2012
Ayan Chakrabarti, Todd Zickler
Color Constancy with Spatio-Spectral Statistics
PAMI 2012
Ayan Chakrabarti, Keigo Hirakawa, Todd Zickler
Learning Object Color Models from Multi-view Constraints
CVPR 2011
Trevor Owens, Kate Saenko, Ayan Chakrabarti, Ying Xiong, Todd Zickler, Trevor Darrell
An Empirical Camera Model for Internet Color Vision
BMVC 2009
Ayan Chakrabarti, Daniel Scharstein, Todd Zickler
Effective Separation of Sparse and Non-sparse Image Features for Denoising
ICASSP 2008
Ayan Chakrabarti, Keigo Hirakawa
Super-Resolution of Face Images Using Kernel PCA-Based Prior
IEEE Transactions on Multimedia 2007
Ayan Chakrabarti, A.N. Rajagopalan, Rama Chellappa
Visual Inference with Statistical Models for Color and Texture
Ph.D. Dissertation, Harvard University, 2011
Ayan Chakrabarti
Teaching
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