BIKRAMJEET (ROBBY) SINGH

• Email: bikramjeet_singh@sfu.ca
• LinkedIn: https://www.linkedin.com/in/singh-bikramjeet/
• GitHub: https://www.github.com/BikramjeetSingh
• Twitter: https://twitter.com/bikramjtsingh
• Blog: https://dev.to/bikramjeetsingh

SKILLS

• Programming Languages: Python, Java, Kotlin, JavaScript, SQL, HTML + CSS, C#
• Tools/Frameworks: PyTorch, TensorFlow, MATLAB, Django, Git, LaTeX, Unity
• Operating Systems: Windows, Linux, MacOS
• Backend Web Development & Mobile Development

EDUCATION

Simon Fraser University, Burnaby (2021 - Present)

• M.Sc in Professional Computer Science
• Visual Computing Concentration
• Overall GPA: 3.78
• Courses:
  • CMPT 726 (Machine Learning)
  • CMPT 742 (Practices for Visual Computing I)

Manipal University, Karnataka, India (2015 - 2019)

• B.Tech in Information and Communication Technology
• Overall GPA: 7.82
• Last 2 years CGPA: 8.19
• Courses:
  • ICT 4017 (Cloud Computing)
  • ICT 4012 (Neural Networks and Fuzzy Logic)
  • ICT 3201 (Distributed Systems)

WORK EXPERIENCE

Senior Software Engineer - HomeLane, Bangalore, India

• Time: April 2021 - September 2021
• Led migration of legacy PHP applications to Django.
• Developed the customer referrals functionality for the HomeLane website from the ground up.
• Reduced database query times by up to 20% by refactoring SQL statements.
• Technologies: Python, Django, MySQL

Software Engineer - Instamojo, Bangalore, India

• Time: July 2019 - March 2021
• Worked as a developer across backend and mobile.
• Implemented efficient, secure backend APIs using Django and Postgres.
• Developed core front-end features on the Android app using Java and Kotlin.
• Worked closely with the product, design and customer relations teams to create solutions that provide the best possible end user experience.
• Technologies: Python, Django, DRF, Java, Kotlin, Android

Intern - LiquidHub Analytics, Gurgaon, India

• Time: May 2018 - July 2018
• Developed a Textual Emotion Detection System, based on artificial neural networks, to classify a given text snippet into one of four emotions (Anger, Fear, Joy, Sadness) with an accuracy of up to 85% on test data.
• It also includes a mechanism for the user to manually flag incorrect results, based on which the system corrects itself automatically.
• In addition, a web API was developed for the system using Flask.
• Technologies: Python, Keras, TensorFlow, NLTK

PROJECTS

Image Superresolution WebApp

• Time: December 2021 - Present
• Developing a webapp to automatically perform image upscaling using artificial neural networks.
• Technologies: Python, PyTorch, NumPy, Django

U-Net Semantic Segmentation of Images

• Time: October 2021 - December 2021
• Developed a neural-network based system that performs semantic segmentation upon images in real-time using a U-Net based model.
• The system is deployed and run on the Huawei 200 DK edge computing device.
• Technologies: Python, TensorFlow, Huawei MindStudio, NumPy