Profile:

I am a machine learning scientist with 5+ years’ experience in real-time implementation of machine learning and digital signal processing algorithms. I possess updated skills, an insatiable intellectual curiosity, and the ability to mine hidden gems located within large sets of structured, semi-structured and unstructured data.

Skills

Programming Tools:

MATLAB, C++ STL [11,14,17], Python [scikit-learn, numpy, scipy]

Machine Learning:

Random Forest, Naïve Bayes, Hidden Markov Models, Long Short Term Memory Networks, Support Vector machine, Regression, Convolutional and Spiking Neural Networks

Signal Processing:

Image Processing, Wavelet Transform, FFT, STFT, Time-Series Analysis, Cepstrum Analysis

Experience

Research Scientist | STS Defence Limited | Gosport, UK | 03/2015 – Present

Responsible for real-time implementation of data mining, statistical machine learning and signal processing based feature extraction algorithms for various on-going projects.

• Successfully led and managed research and development of UK-Innovate project, IConIC (Intelligent Condition monitoring with Integrated Communications), with a budget of £1.03 millions and 8 industrial and academic stakeholders.
• Developed empirical techniques for mining vibration and speed data from a ship’s engine and devised a novel methodology to detect and predict engine failures by implementing wavelet decomposition and one-class support vector machines.

• Devised innovative application of machine learning and statistical methods on real-time heart-beat data to predict the time to potential heart-stroke in case of medical emergency.

   

Research Engineer | Printed Motor Works Limited | Alton, UK | 01/2013 – 02/2015

Provided research leadership in a team that designed and developed real-time fault-detection machine learning algorithm for next generation in-wheel motors.

• Provided data analytics, using advanced statistical and machine learning models, to mechanical engineering team for possible design shortcomings.
• Architected and implemented analytics and visualization components for device data analysis platform to predict hardware
• Developed current waveform extension models relying on decision trees, random forest, logistic regression and support vector machine.

Education

University of Portsmouth, UK

PhD (Part-Time) | 2016 - Continued

Thesis: Combining Machine Learning and Signal Processing for Next-Gen AI algorithms

University of Oxford, UK

PG Cert. | 2014

Major: Advance Project Management for Scientists and Engineers

London Metropolitan University, UK

MSc. Embedded Systems | 2011 - 2012

National University of Sciences and Technology, Pakistan

BEng(Hons.) Electronics Engineering | 20017 - 2011