Dwayne Fernandes

I have over 5 years of programming experience in languages like Python, Java, JavaScript, C++, and its various frameworks. As a Computer Scientist,  I've extended my understanding of mathematics in  Algebra, Calculus, trigonometry, Binary Maths, Statistics, Discrete Maths, etc. Most of my critical and algorithmic thinking needed to solve a problem programmatically is encompassed around maths. I graduated with first-class honors in my degree. Apart from specializing in Artificial Intelligence, I have over 2 years of work experience in software development, specifically machine learning and artificial Intelligence.

To me, programming is beyond just learning about a language's syntax. It is more about developing critical and analytical thinking in order to solve a problem at hand algorithmically. Alongside learning the fundamentals of coding, I would like to emphasize good coding practices in order to write high-quality, readable, concise, robust code that is necessary to develop real-world applications.

Software Engineer - Computer Vision Team | Xoriant Solutions, London July 2021 – August 2022 

- Envision Body: IOS app for predicting body transformation using artificial intelligence

-  Led the design and development of pose detection and body parts segmentation models using C++ and

Pytorch to accurately transform individual body parts

-  Optimised the algorithm in C++ to perform high-speed detections on iPhone at over 20 fps

-  A survey resulted in 82% of the people favoring in opting for a paid subscription for the app

• Identification of crop phenology for farms from geo-satellite images

-  Performed analysis and denoising of data obtained from Sentinel-2, using Tableau and Python to have

enhanced detection of phenology stages from the NDVI time series data

-  This assisted FMC to recommend the most ideal fertilizer, thus, increasing their sales by 15%

Machine Learning Intern - AI Team | Cenaura Technologies April 2020 – June 2021

• Automation of cultivation and Yield Enhancement for Semi-Hydroponic cropping using AI

-  Led the development of a reinforcement learning algorithm to autonomously cultivate hydroponic crops

by maintaining the optimality in nutrient supply and controlling the environment

-  Developed a CNN model in TensorFlow to detect up to 30 fungal diseases with over 93% accuracy

-  Implemented a real-time monitoring and interactive dashboard using Plotly, Python, and AWS

-  Resulted in reduced labor costs and product wastage, with a quicker yield of lettuce in 6 weeks

University of St Andrews, September 2022- Present (MSc in Artificial Intelligence)  

-Coursework: AI principles and practice, Machine Learning, NLP, Data-intensive systems, Signal processing

St. Francis Institute of Technology, July 2017 - June 2021 (B.Engg in Computer Science)

• CGPA: 9.49 / 10
• Published two research papers in machine learning at the conference of IEEE and ICDAM
• Technical Head of the College Council Team and the Codex Technical Committee 
• Conducted seminars & workshops on Machine Learning and Django in Python