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

• Developing internal Python libraries and tools for machine learning model training and testing as part of synthetic data generation workflows

• Developing a Python package for augmenting event-camera datasets for object recognition

• 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

• 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

• 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

• 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]

• 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

• 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!