Anuroop Sriram

Anuroop Sriram

Founding AI Research Scientist, Prometheus

I build AI foundation models, with a focus on using them to accelerate science and engineering. I am a founding AI Research Scientist at Prometheus, where we are building the Artificial General Engineer: AI systems that change how the physical world is designed and engineered.

Before Prometheus, I led research teams at Meta FAIR for eight years, working across materials, medical imaging, and speech. Most recently, my work in materials spanned foundation models and generative modeling (FlowLLM, FlowMM, UMA), large-scale training infrastructure (Graph Parallelism, enabling billion-parameter interatomic potentials), and community-standard datasets (Open Catalyst, Open DAC, OMC). I also created FastCSP, which cuts crystal structure prediction from weeks to hours, and helped build the fairchem library.

I also led fastMRI, a collaboration between Meta and NYU Langone to accelerate MRI using deep learning. fastMRI delivers up to 4× faster scans, and methods based on it have been deployed in clinical MRI systems worldwide.

Earlier, I built and led the speech team at Meta FAIR, where we trained some of the first billion-parameter speech models and built highly efficient ASR systems that were deployed to billions of users. Before that, at Baidu, I co-created Deep Speech 2, the first end-to-end model to achieve human-level speech recognition in both English and Mandarin.

I hold a Master's in Language Technologies from Carnegie Mellon University.

Selected Publications

  • FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
  • FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
    Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, et al.
    NeurIPS 2024
    ARXIV CODE
  • UMA: A Family of Universal Models for Atoms
  • UMA: A Family of Universal Models for Atoms
    Brandon M. Wood, Misko Dzamba, Xiang Fu, et al.
    NeurIPS 2025
    ARXIV CODE MODELS
  • Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
  • Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
    Anuroop Sriram, Abhishek Das, Brandon M Wood, et al.
    International Conference on Learning Representations (ICLR) 2022
    ARXIV CODE
  • FlowMM: Generating Materials with Riemannian Flow Matching
  • FlowMM: Generating Materials with Riemannian Flow Matching
    Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, et al.
    International Conference on Machine Learning (ICML) 2024
    ARXIV CODE
  • End-to-end variational networks for accelerated MRI reconstruction
  • End-to-end variational networks for accelerated MRI reconstruction
    Anuroop Sriram, Jure Zbontar, Tullie Murrell, et al.
    International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020
    ARXIV CODE
  • Open catalyst 2020 (OC20) dataset and community challenges
  • Open catalyst 2020 (OC20) dataset and community challenges
    Lowik Chanussot, Abhishek Das, Siddharth Goyal, et al.
    ACS Catalysis
    ARXIV CODE DATA
  • fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning
  • fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning
    Florian Knoll, Jure Zbontar, Anuroop Sriram, et al.
    Radiology: Artificial Intelligence
    ARXIV CODE DATA
  • Cold Fusion: Training Seq2Seq Models Together with Language Models
  • Cold Fusion: Training Seq2Seq Models Together with Language Models
    Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, et al.
    Interspeech 2018
    ARXIV
  • Deep speech 2: End-to-end speech recognition in english and mandarin
  • Deep speech 2: End-to-end speech recognition in english and mandarin
    Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, et al.
    International Conference on Machine Learning (ICML) 2016
    PAPER