About me


I am a fifth-year PhD candidate in the Computer Science Department of UT Austin. I am fortunate to be advised by Prof. Inderjit S. Dhillon and Prof. Sujay Sanghavi. Broadly speaking, I am interested in developing provably better optimization algorithms and generalization-improving techniques for machine learning. My research spans adaptive optimization algorithms, knowledge distillation, differentially private training, and federated learning. I will be a student researcher at Google Research in the Algorithms and Optimization team through the summer of 2024. Before this, I have interned at Google Research in the Algorithms and Optimization team, Google DeepMind Princeton, Google Research in the Learning Theory and Federated Learning teams, and Amazon.

Previously, I was a dual degree (combined bachelor’s and master’s degree) student in the Department of Electrical Engineering, Indian Institute of Technology (IIT) Bombay. At IIT Bombay, I worked under the guidance of Prof. Subhasis Chaudhuri. I was awarded the Undergraduate Research Award (URA-03) for exceptional work in my final thesis.

You can check out my outdated CV here. My email is rdas(at)utexas(dot)edu.

I am seeking industry research positions in the 2024-25 cycle. Feel free to reach out if you think I will be a good fit.


  • Retraining with Predicted Hard Labels Provably Increases Model Accuracy” - Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi and Peilin Zhong.

    Preprint. Download here.

  • Towards Quantifying the Preconditioning Effect of Adam” - Rudrajit Das, Naman Agarwal, Sujay Sanghavi and Inderjit S. Dhillon.

    Preprint. Download here.

  • Understanding the Training Speedup from Sampling with Approximate Losses” - Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal and Sujay Sanghavi.

    ICML 2024. Download paper here.

  • Understanding Self-Distillation in the Presence of Label Noise” - Rudrajit Das and Sujay Sanghavi.

    ICML 2023. Download paper here.

  • On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data” - Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu and Tong Zhang.

    TMLR. Download preprint here.

  • Beyond Uniform Lipschitz Condition in Differentially Private Optimization” - Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang and Sujay Sanghavi.

    ICML 2023. Download paper here.

  • Differentially Private Federated Learning with Normalized Updates” - Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi and Inderjit S. Dhillon.

    Download preprint here. Short version presented in OPT2022 workshop of NeurIPS 2022; download here.

  • Faster Non-Convex Federated Learning via Global and Local Momentum” - Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon and Ufuk Topcu.

    UAI 2022. Download paper here and preprint here.

  • On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization” - Abolfazl Hashemi, Anish Acharya^, Rudrajit Das^, Haris Vikalo, Sujay Sanghavi and Inderjit Dhillon (^ denotes equal contribution).

    IEEE Transactions on Parallel and Distributed Systems. Download paper here and preprint here.

  • On the Convergence of a Biased Version of Stochastic Gradient Descent” - Rudrajit Das, Jiong Zhang and Inderjit Dhillon.

    NeurIPS 2019 Beyond First Order Methods in ML workshop. Download paper here.

  • On the Separability of Classes with the Cross-Entropy Loss Function” - Rudrajit Das and Subhasis Chaudhuri.

    Preprint. Download here.

  • Nonlinear Blind Compressed Sensing under Signal-Dependent Noise” - Rudrajit Das and Ajit Rajwade.

    IEEE International Conference on Image Processing (ICIP) 2019. Download paper here.

  • Sparse Kernel PCA for Outlier Detection” - Rudrajit Das, Aditya Golatkar and Suyash Awate.

    IEEE International Conference on Machine Learning and Applications (ICMLA) 2018 Oral. Download paper here.

  • iFood Challenge, FGVC Workshop, CVPR 2018 - Parth Kothari^, Arka Sadhu^, Aditya Golatkar^ and Rudrajit Das^ (^ denotes equal contribution).

    Finished $2^{nd}$ in the public leaderboard and $3^{rd}$ in the private leaderboard (Team name: Invincibles). Leaderboard Link. Invited to present our method at CVPR 2018 (slides can be found here).


  • Student Researcher at Google Research (Remote) (November ‘23 - March ‘24)
    Host: Alessandro Epasto
    • Working on improving label differential privacy (DP) using ideas from self-distillation with theoretical analysis.
  • Student Researcher at Google DeepMind, Princeton, NJ, USA (June ‘23 - October ‘23)
    Host: Naman Agarwal
    • Derived new theoretical results to quantify the preconditioning effect of the Adam optimizer, and empirically benchmarked several optimization algorithms based on Adam.
  • Research Intern at Google (Remote) (June ‘21 - August ‘21)
    Hosts: Zheng Xu, Satyen Kale, and Tong Zhang
    • Clipped gradient methods are commonly used in practice for differentially private (DP) training, e.g., DP-SGD. However, a sound theoretical understanding of these methods has been elusive. We provide principled guidance on choosing the clipping threshold in DP-SGD and also derive novel convergence results for DP-SGD in heavy-tailed settings.
  • Applied Scientist Intern at Amazon Search (Remote), Berkeley, CA, USA (May ‘20 - August ‘20)
    Mentor: Dan Hill, Manager: Sujay Sanghavi
    • Worked on customer-specific query correction by leveraging the “session data” (i.e. previous searches of the customer) using SOTA Transformer models. Our model generated better candidates than the production system.
  • Institute for Biomechanics, ETH Zürich, Zürich, Switzerland (May ‘17 - July ‘17)
    Guide : Dr. Patrik Christen and Prof. Dr. Ralph Müller, D-HEST
    • Proposed a stable linear model (with closed-form solution) and a fuzzy boolean network for bone remodeling. Also developed an automated 2D-3D image registration framework for histology images from scratch.