Email: smnair@uwaterloo.ca

Linkedin: https://www.linkedin.com/in/saeejith

Currently, I am working on efficient deep learning methods for embedded systems with application on autonomous driving cars, traffic monitoring and healthcare. 

My PhD thesis topic was about introducing a “Randomly-connected Non-Local Conditional Random Fields” to address long-range spatial connectivity while maintaining computational complexity. 

My master thesis was focused on A Novel Conditional Random Field Framework For Object Tracking supervised by Prof. Zohreh Azimifar.

He has obtained a Master of Technology in Automation and Computer Vision from Indian Institute of Technology, Kharagpur, India and a Ph.D. degree in pattern analysis and machine intelligence from Systems Design Department of University of Waterloo. Prior to starting his Ph.D, Akshaya worked for two years and six months for Read-Ink Technology Pvt. Ltd to develop machine learning and pattern recognition algorithms for online hand written character recognition engine.

After completing his Ph.D. Akshaya worked for one year for Tornado Medical Systems, where he developed algorithms for reconstruction and analysis of Optical Coherence Tomography (OCT) signals. Recently, Akshaya has joined Miovision Technologies Pvt. Ltd to work in the areas of intelligent traffic data analysis and management. Akshaya’s interest includes but are not limited to document image analysis, medical image reconstruction and analysis with a focus on Optical Coherence Tomography signals, traffic data analysis and management, quantitative image quality assessment and modeling and optimization of data of any kind.

Email: akmishra@engmail.uwaterloo.ca

Email: psiva@eecs.qmul.ac.uk

Personal Website: http://www.psiva.ca

We have introduced the concept of evolutionary deep intelligence, where we evolve deep neural networks over multiple generations to become more efficient yet smart. The ‘DNA’ of each generation of deep neural networks is encoded computationally and used, along with simulated environmental factors such as those encouraging computational and energy efficiency through natural selection, to ‘give birth’ to its offspring deep neural networks, with the process repeating generation after generation. These ‘evolved’ offspring deep neural networks will naturally have more efficient, more varied architectures than their ancestor deep neural networks (due to natural selection and random mutations) while achieving powerful cognitive capabilities. 

Experimental results from a study using the MSRA-B and HKU-IS datasets demonstrated that the synthesized offspring deep neural networks can achieve state-of-the-art F-beta scores while having network architectures that are significantly more efficient, with a staggering ~48X fewer synapses by the fourth generation compared to the original, first-generation ancestor network.   This level of performance was further reinforced by experimental results from a study using the MNIST dataset, which demonstrated synthesized offspring deep neural networks can achieve state-of-the-art accuracy (>99%) while having network architectures that are significantly more efficient, with a staggering ~40X fewer synapses by the seventh generation compared to the original, first-generation ancestor network. More remarkably, an accuracy of ~98% was still achieved by thirteen-generation offspring deep neural networks with an incredible ~125X fewer synapses compared to the original, first-generation ancestor network. 

The concept of evolutionary deep intelligence has won numerous awards, including a Best Paper Award at the NIPS Workshop on Efficient Methods for Deep Neural Networks, a Best Paper Award at the Conference on Computational Vision and Intelligence Systems, named by MIT Technology Review as one of the most interesting and thought-provoking papers on arXiv, and named on Reddit as one of the papers that demonstrate the beauty of deep learning.

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Supervision – Completed

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ph.D.

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