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Amarjot Singh

Amarjot Singh

Founder & CEO, Skylark Labs, pioneering adaptive, brain-inspired AI systems

Joined January 2025

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Summary

Amarjot Singh is a visionary leader and entrepreneur in artificial intelligence, founding Skylark Labs Inc. to develop and commercialize self-learning, brain-inspired AI systems. His company focuses on on-device, real-time adaptive AI for critical applications in aerospace, defense, public safety, and smart transportation, demonstrating a commitment to creating impactful technological solutions. skylarklabs+2
He possesses a strong academic and research background, holding a PhD in Deep Learning from the University of Cambridge and a MASc in Biomedical Image Processing from Simon Fraser University. His doctoral work involved creating ScatterNet Hybrid Deep Networks for edge devices, informed by studies at Harvard, MIT, and Brown University, and his postdoctoral research at Stanford University focused on continuous learning AI for DARPA's Lifelong Learning Machines Program. skylarklabs+5
Amarjot is a recognized innovator, named an MIT Technology Review Innovator Under 35. His work reflects a deep sense of social responsibility, evidenced by his development of CENSER for rescuing trafficked children and a face recognition system for Syrian refugee family reunification with the Red Cross. He also led efforts to use AI-enabled drones for COVID-19 enforcement in India, demonstrating a consistent application of his technological expertise for societal benefit. innovatorsunder35+1

Work

Education

Projects

Writing

Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

January 1, 2018

Introduces a real-time drone surveillance system that uses Feature Pyramid Network to detect humans from aerial images and identifies violent individuals in public areas.

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Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

January 1, 2017

Addresses the challenging problem of disguised face identification using a deep learning framework that detects facial key-points to improve identification accuracy.

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Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification

January 1, 2017

Introduces a novel ScatterNet that uses a parametric log transformation with Dual-Tree complex wavelets to extract translation invariant representations for object classification.

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Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet

January 1, 2017

Proposes a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) that replaces early CNN layers with a parametric log based DTCWT ScatterNet to extract edge-based invariant representations, improving network training.

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ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification

January 1, 2017

Introduces the ScatterNet Hybrid Deep Learning (SHDL) network which extracts invariant and discriminative image representations for object recognition, featuring a multi-layer ScatterNet front-end, an unsupervised learning middle, and a supervised learning back-end module.

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Automatic segmentation of trophectoderm in microscopic images of human blastocysts

January 1, 2014

Presents a method for accurate assessment of embryo viability by automatically segmenting the trophectoderm in microscopic images of human blastocysts.

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Wavelet based image fusion for detection of brain tumor

January 1, 2013

Proposes an efficient wavelet-based algorithm for brain tumor detection that utilizes complementary and redundant information from CT and MRI images for accurate size and location detection.

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A survey of OCR applications

January 1, 2012

A comprehensive review of Optical Character Recognition (OCR) applications, highlighting its wide use in various fields.

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