Neelay Shah

Hello! I am Neelay Shah, a final year undergraduate student at Birla Institute of Technology and Science, Pilani. I'm working towards a bachelor's degree in Electronics and Instrumentation Engineering.

I'm broadly interested in machine learning and am particularly excited by its potential to aid in solving pressing real-world problems such as climate change among others. I enjoy making research in machine learning accessible by developing open-source software (check out my work).

Email  /  GitHub  /  GitLab  /  Semantic Scholar  /  Twitter

Work Experience
AI Engineer Intern

  • Developing internal Python libraries and tools for machine learning model training and testing as part of synthetic data generation workflows
Google Summer of Code
Open-source Developer
at International Neuroinformatics Coordinating Facility

  • Developing a Python package for augmenting event-camera datasets for object recognition
Frinks Digital Technologies
AI Developer Intern

  • Developed and deployed real-time surveillance and monitoring computer vision solutions for industrial clients
  • Created end-to-end pipelines for:
    • Worker safety monitoring in manufacturing plants using object detection and multi-headed classification
    • Identification and logging of vehicle movement in industrial facilities using object detection and optical character recognition
Research Experience
Visual Intelligence Lab, Northeastern University
Research and Development Intern

  • Developed an open-source PyTorch library - EzFlow - for optical flow estimation using neural networks
  • Designed and experimented with transformer-based neural networks for optical flow estimation
Ethical Intelligence Lab, Harvard Business School
Research Affiliate

  • Worked on psychophysics experiments to study human perception of the working of machine learning models in the context of autonomous driving
  • Empirically demonstrated discrepancies in human behaviour when presented with varied visualizations of model functioning
  • Carried out machine learning experiments to model human visual cognition of social interactions
Publications / Preprints
  • An Image Segmentation Based Approach Improves Photoacoustic Tomographic Image Reconstruction
    Neelay Shah, Manish Bhatt
    International Conference on Biomaterials and Biomedical Engineering 2022

  • KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
    Het Shah, Avishree Khare, Neelay Shah, Khizir Siddiqui
    Pre-print | [arXiv] [Code]
Open-source Projects
  • KD-Lib
    GitHub / Documentation / Preprint
    Co-creator and Maintainer

    A PyTorch model compression library containing knowledge distillation, pruning, and quantization methods

  • EzFlow
    GitHub / Documentation
    Co-creator and Maintainer

    A modular PyTorch library for optical flow estimation using neural networks

  • VFormer
    GitHub / Documentation
    Co-creator and Maintainer

    A modular PyTorch library for vision transformers models

Other Selected Projects
  • Deep Learning Aided Ultrasound Image Reconstruction | Code
    • Developed an image segmentation approach for improving photoacoustic tomographic image reconstruction with an IoU of 0.89

  • Irregularity Prediction in Satellite Navigation Signals | Code
    • Developed a deep learning pipeline to forecast irregularities in the ionosphere which cause fluctuations in satellite navigation signals which achieved an R2 score of 0.94
    • Benchmarked machine learning models for classifying multi-path interference in satellite signals

  • Estimating Urban Walkability using Machine Learning | Code
    • Developed a machine learning approach to estimate walkability of streets in Indian cities and to identify factors influencing walkability
    • Achieved an accuracy of 0.68 using a method involving image segmentation and decision tree classification

  • Spiking Neural Networks for Speech Classification | Code
    • Implemented biologically plausible algorithms for speech classification in neuronal network simulation software

  • Real-time Hand Segmentation | Code
    • Trained and benchmarked a variety of segmentation models for real-time hand segmentation in RGB videos
    • Achieved IoUs nearing 0.87 on custom test videos with inference times ranging between 40-70 ms on GPUs

  • Compressed Neural Networks for Network Intrusion Detection | Code
    • Developed compact neural networks using knowledge distillation to detect botnet activities in the Internet of Things (IoT)
    • Trained compressed neural networks with a compression ratio of 2 to achieve similar performance to larger models

  • Unsupervised Anomaly Detection in Chest Radiographs | Code
    • Autoencoders to identify irregularities in chest X-rays and detect potential pneumonia cases

PS: If you have some spare time and are interested, please visit and help amplify this website which lets people picture the impacts of climate change. Also, do take a look at these heart-wrenching climate change statistics. Thanks!

Much gratitude to Jon Barron for the template!