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Hi, I'm Travis.

 

Introduction

A little bit about myself.

I'm a student at McMaster University studying Engineering Physics with a background in Electronics and Machine Learning. I'm currently a Deep Learning Software Engineer Intern at Montreal based AI Startup, Deeplite.


I've got a passion for Space Exploration, Robotics and AI. In my free time I enjoy playing chess, basketball, guitar, and working on side projects in the software and hardware domain!


I'm currently looking for Summer 2022 co-op/internship opportunities. Feel free to contact me at ratnahat@mcmaster.ca for business inquiries or just to chat.

Projects

A few projects I've worked on over the years. Check them out at GitHub.

Im.Primo: Wireless 3D Scanning & 3D Printing at your fingertips


Im.Primo allows users to scan objects with their phone and 3D Print it with the click of a button. The application leverages the Lidar scanner in an iPhone coupled with SLAM algorithms to scan an object and grenerate a mesh file. This mesh file is then sent to a backend server which filters it and converts it into a 3D Printable STL file. This STL file is then sent to a 3D printer running Octoprint on a Raspberry Pi to enable wireless printing.



Built using Python, Swift, Django, StandardCyborg API, & OctoPrint API


Check it out: Youtube, DevPost, GitHub

Print.ology: Interactive 3D Print Previews in Augmented Reality


Print.ology allows users to preview 3D models they'd like to print in AR to get insights into how their print would turn out. The application starts off with the user uploading thier desired 3D models onto a server. The backend of the server processes the uploaded STL file and converts it into a USDZ file to enable compatibility with AR view. Then the mobile app connects to the server to browse a catalogue of the models which they have uploaded. From here users can select their model and view it in AR and then send it to the server to 3D print it wirelessly. This application was built during my first hackathon and actually placed in the top 3 out of over 600 contestants.



Built using Node.js, Swift, ARKit, & OctoPrint API


Check it out: GitHub, DevPost

SmartDrone: Autonomous Mini-Quadcopter


Programmed a DJI Tello drone to recognize the user’s face and autonomously track them while maintaining a set distance. This project gave me an introduction to applied computer vision and was also presented at McMaster’s IBM design showcase.



Built using Python, OpenCV, and DJI Tello


Check it out: GitHub

Ultrasonic Rangefinder


Designed and built a breadboard prototype of an instrument to provide a readout of the distance of an object from an ultrasonic sensor with a maximum range of 1 meter. This was done without the aid of any micrcontroller or microprocessor, and it was designed from scratch incorporating all my knowledge from digital and analog electronics.



Built using NI Multisim, 400PT160 40kHz Ultrasonic Transducers, SN74HC112N J-K Flip-Flops, TLC555 Timers, Op Amps, and more.


Check out the full detailed report: Report

Education

A brief summery of my academic journey with notable courses I've gained valuable knowlegde from.

iB Diploma Programme: Father Michael McGivney Catholic Highschool


My highschool education helped me prepare for the STEM field by taking post-secondary level courses including HL Chemistry, SL Math, and SL Physics, and SL French. I finished and obtained the iB diploma with 38/42 points and graduated with honours and early acceptance to engineering at McMaster.


Engineering Physics Co-op, B.Eng, Minor in CS: McMaster University


My undergraduate programme has helped me to learn how to work under pressure by taking some of the most rigorous courses offered by my university. The Engineering Physics program has allowed me to gain applicable knowledge in the engineering domain by giving me the freedom to take courses ranging from the Software Engineering field to the Electrical Engineering field with an additional fundamental knowledge in the underlying Physics used in Engineering Systems.

From: 2019-2024

Machine Learning & Data Science Bootcamp: Zero to Mastery Academy


This was an extensive bootcamp I took out of interest to learn the basic concepts of Machine Learning Alogrithms and also the applied theory through projects involving Pandas, Numpy, Matplotlib, Scikit-learn, and Tensorflow 2.


From: 2020

Tensorflow 2 and Keras Deep Learning Bootcamp: Pierian Data


This bootcamp covered the fundamentals of Neural Networks in Deep Learning applications including ANNs, CNNs, RNNs, NLP, AutoEncoders, and GANs. This involved creating projects using Pandas, Numpy, Matplotlib, Keras API, and Tensorflow 2.


From: 2019

Pytorch for Deep Learning and Computer Vision: Udemy


This course was the next step for me to further specialize in my interest of applying machine learning to computer vision. From this course I was able to build neural networks from scratch using Pytorch for applications such as Image Recognition, Transfer Learning, and Style Transfer.


From: 2021

Skills

Some notable technical skills I've acquired over the years.

Programming

The languages and tools that I'm most familiar with:

  • Git/GitHub:

    I use GitHub extensively in my software projects and doing so has allowed me to leverage Git version control to enable me to work efficiently in development teams.

  • Python:

    As the first programming language I learned, I've thoroughly enjoyed using Python across multiple domains and find it very useful for rapid prototyping.

  • Java:

    This programming language was used in my data structures and algorithms course and was very helpful in helping me understand advanced algorithmic concepts.

  • C:

    As a firmware engineer I use C heavily when working with embedded systems such as FPGAs, Microprocessors, and Microcontrollers.

  • Matlab:

    I've learned to become comfortable with using Matlab for scientific programming through undergraduate courses such as Engineering Numerical Methods and extracurricular clubs such as Formula SAE.

  • HTML/CSS:

    I picked up this skill to enable me to learn web development, and I had so much fun that I used it to build this website!

Machine Learning

The frameworks and libraries I use the most in my Machine Learning workflow:

  • Numpy:

    A fundamental and powerful tool for scientific calculations that I use on datasets.

  • Matplotlib:

    An excellent library I use frequently for data visualisation

  • Pandas:

    This is an essenital tool I use for preparing data for a variety of models.

  • Scikit-Learn:

    Due to its compatibility with common ML libraries such as Pandas, I've found Scikit-Learn to be an excellent tool for deploying standard ML algorithms.

  • Tensorflow:

    An extremely powerful tool I use to build and implement Deep Learning models.

Hardware

The skills and tools that help me the most for projects in the hardware domain:

  • Analog & Digital Ciruit Components:

    I've enjoyed using a variety of anolog components and IC chips to apply theory from the classroom to my breadboarding projects.

  • Oscilloscopes & Multimeters:

    I've spent countless hours in labs debugging and I've learned that these are essential tools for ensuring the functionality of any electronics project.

  • FPGAs, Micrprocessors, Microcontrollers:

    I've worked heavily on the FPGA and softcore Microprocessor on board the NEUDOSE Cube Satellite's Payload, and I continue to gain an understanding of microcontrollers through electronics projects and coursework.