• Signals from deep space: WVU students develop AI to detect fast radio bursts (article, video)
  • XSEDE GPU Resources Help WVU Scientists Create AI Package to Make Processing Thousands of Fast Radio Burst Candidates Manageable by Human Experts (article)
  • AI Makes Deluge of Fast Radio Burst Data Manageable (article)
  • In a nearby galaxy, a fast radio burst unravels more questions than answers (article)


  • Detecting Cosmic Flashes with Artifical Intelligence, WVU planetarium (link)
  • The Petabyte FRB Search Project, FRB2020 meeting (link)
  • The Petabyte FRB Search Project, NANOGrav 2020 meeting (link)
  • Comprehensive Search and Analysis tools for FRBs, FRB2021 meeting (link)
  • Search, analysis and localization of FRBs (link)
  • Population synthesis study of MSP spectral indices (link)


Recently, Devansh and I led bi-weekly sessions of chapters from the book Dive into Deep Learning for Machine Learning Tokyo. We discussed the basic concepts and reimplemented selected models from scratch in PyTorch. All the slides, notebooks and videos of the sessions are linked below and are available on Github:

Chapter   Slides    Notebook    Video
Multilayer Perceptrons   Slides Notebook Youtube
Convolutional neural networks    Slides Notebook Youtube
Recurrent Neural Networks Slides Notebook Youtube
Attention Mechanisms 1 Slides Notebook Youtube
Attention Mechanisms 2 Slides Notebook Youtube