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
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Twitter
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Bifrost
AI Engineer Intern
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Developing internal Python libraries and tools for machine learning model training and testing as part of
synthetic data generation workflows
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Google Summer of Code
Open-source Developer
at International Neuroinformatics Coordinating Facility
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Developing a Python package for augmenting event-camera datasets for object recognition
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Frinks Digital Technologies
AI Developer Intern
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Developed and deployed real-time surveillance and monitoring computer vision solutions for industrial clients
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Created end-to-end pipelines for:
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Worker safety monitoring in manufacturing plants using object detection and multi-headed classification
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Identification and logging of vehicle movement in industrial facilities using object detection and optical character recognition
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Visual Intelligence Lab, Northeastern University
Research and Development Intern
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Developed an open-source PyTorch library - EzFlow - for
optical flow estimation using neural networks
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Designed and experimented with transformer-based neural networks for optical flow estimation
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Ethical Intelligence Lab, Harvard Business School
Research Affiliate
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Worked on psychophysics experiments to study human perception of the working of machine learning
models in the context of autonomous driving
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Empirically demonstrated discrepancies in human behaviour when presented with varied visualizations
of model functioning
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Carried out machine learning experiments to model human visual cognition of social interactions
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An Image Segmentation Based Approach Improves Photoacoustic Tomographic Image Reconstruction
Neelay Shah,
Manish Bhatt
International Conference on Biomaterials and Biomedical Engineering 2022
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KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
Het Shah,
Avishree Khare,
Neelay Shah,
Khizir Siddiqui
Pre-print |
[arXiv]
[Code]
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Deep Learning Aided Ultrasound Image Reconstruction | Code
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Developed an image segmentation approach for improving photoacoustic tomographic image reconstruction with an IoU of 0.89
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Irregularity Prediction in Satellite Navigation Signals | Code
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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
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Benchmarked machine learning models for classifying multi-path interference in satellite signals
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Estimating Urban Walkability using Machine Learning | Code
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Developed a machine learning approach to estimate walkability of streets in Indian cities and to identify
factors influencing walkability
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Achieved an accuracy of 0.68 using a method involving image segmentation and decision tree classification
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Spiking Neural Networks for Speech Classification | Code
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Implemented biologically plausible algorithms for speech classification in neuronal network simulation software
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Real-time Hand Segmentation | Code
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Trained and benchmarked a variety of segmentation models for real-time hand segmentation in RGB videos
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Achieved IoUs nearing 0.87 on custom test videos with inference times ranging between 40-70 ms on GPUs
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Compressed Neural Networks for Network Intrusion Detection | Code
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Developed compact neural networks using knowledge distillation to detect botnet activities in the Internet of Things (IoT)
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Trained compressed neural networks with a compression ratio of 2 to achieve similar performance to larger models
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Unsupervised Anomaly Detection in Chest Radiographs | Code
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Autoencoders to identify irregularities in chest X-rays and detect potential pneumonia cases
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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!
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