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vamchowdary72[at]gmail

Education

  1. UCSB

    PhD

    • Understanding and designing DNN architectures from approximation theoretic perspective, with focus on efficincy and provable generalization.
    • Advisor: Prof. Shivkumar Chandrasekaran

  2. UCSB

    Masters

    • Thesis: A study of generalization in deep neural networks
    • GPA: 4.0/4.0

  3. IIT Roorkee

    Bachelors in ECE

    • Thesis: Low-cost display devices using nanoparticles
    • Advisors: Prof. Brijesh Kumar and Prof. Sanjeev Manhas
    • GPA: 8.09/10.0

Experience

  1. Cartesia AI

    Researcher

  2. Amazon AGI

    Applied Scientist Intern

    • Trained a novel Mixture of Experts (MoE) architecture to reduce inter-node communication costs.

  3. Stealth startup

    ML Researcher

    • Part of the founding team, I led groundwork and architectural setup for ML based solutions, developing Vision, NLP, and speech-based models for tasks in the supply chain industry.

  4. Apple

    Software Intern

    • Developed physics based algorithms to improve the Fall Detection feature. Created data processing pipelines to efficiently handle hundreds of hours of time series data.

  5. UCSF

    Grad Researcher

    • Developed Medviz - an AWS web portal and visualization tool for deploying machine learning models for processing of large PET/MRI datasets.

  6. Briteseed

    ML Intern

    • Trained CNNs on hyperspectral image data from surgical tools to detect tissues.

  7. Samsung Research

    Engineer

    • Music Information Retrieval (MIR).

  8. VIOS Medical

    Embedded Intern

    • Characterized different wireless modules for energy consumption and connectivity.
    • Developed software packaging and Linux distribution tools.

I completed my Ph.D. at the University of California, Santa Barbara (UCSB), where I was advised by Shiv Chandrasekaran. I am now a researcher at Cartesia AI.

My dissertation focused on approximation-theoretic approaches to designing neural network architectures that are efficient and have robust generalization. My recent work includes efficient neural network-based solvers for PDEs.

Posts

Learning LU Factorization using gradient descent

Learning LU Factorization using gradient descent

December 27, 2025

Formulating LU factorization of linear operators as a neural network training problem by representing L and U as structured weight matrices.

[Code]

Publications

MNO: A Multi-modal Neural Operator for Parametric Nonlinear BVPs

MNO: A Multi-modal Neural Operator for Parametric Nonlinear BVPs

Vamshi C Madala, N Govindarajan, S Chandrasekaran

AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), 2026

[Paper] [Code]
CNNs Avoid the Curse of Dimensionality by Learning on Patches

CNNs Avoid the Curse of Dimensionality by Learning on Patches

Vamshi C Madala, S Chandrasekaran, J Bunk

IEEE Open Journal of Signal Processing, vol. 4, pp. 233-241, 2023

[Paper] [Code]
Understanding and Visualizing Generalization in UNets

Understanding and Visualizing Generalization in UNets

A Rajagopal, Vamshi C Madala, T.A Hope, P Larson

Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, 2021

[Paper] [Code]
Predicting Generalization in Deep Learning via Local Measures of Distortion

Predicting Generalization in Deep Learning via Local Measures of Distortion

A Rajagopal, Vamshi C Madala, S Chandrasekaran, P Larson

arXiv preprint arXiv:2012.06969, 2020

[Paper]
A Study of Generalization in Deep Neural Networks

A Study of Generalization in Deep Neural Networks

Vamshi C Madala

Master's Thesis, University of California, Santa Barbara, 2021

[Paper]