Software Engineer Intern
Deep Learning Intern at AMII
Machine Learning Engineer (NRC CAN & CCG Ualberta)
Teaching Assistant at University of Alberta
Research Assistant at Compilers Design & Optimization Lab, UAlberta
Co-founder at Arcane Lost
Hey! It's Vedant Vyas and I'm a fourth year Honors Computer Science student studying at University of Alberta. I am passionate about mathematics, machine learning & software development. I am happy to collaborate on any interesting software or research project. Feel free to reach out to me for collaboration.
Feel free to contact me here.
This is a meal planner app, which lets users organize the ingredients at home, add recipes, plan meals and has an automated shopping list, which is created based on the meal plan for the week.We used google firebase to store the user data (which is linked with their login credentials). App was primarily written in Java using the Android Studio framework. To understand the functioning of the app better, read this.
This project aims to tackle the poor quality of text to speech apps when dealing with latex equations. It is deployed and hosted on Google Cloud. I used ANTLR4 to build my own lexer and parser to map latex equations to english text. Website supports text, png and jpeg, and uses Google cloud text to speech to convert parsed text to audio.
Website lets the user record audio and then uses google’s app engine to convert speech to text .Pydub adds a funny vine boom sound effect every time there is a sus trigger word in speech .Used flask for backend, google app engine for deployment, and React for frontend
This is a movie and cast member data processing app that allows users to manage a database of movies, search for all the
movies using keywords, add movies, update info related to movies and some other operations.
Used pymongo(mongodb) for query processing and database management, python for preprocessing.
This project is a dialect written for the IBM Power 10 MMA unit in MLIR. Dialects are essential in the MLIR ecosystem, allowing the creation of new attributes, operations, and types for a wide range of abstractions, from arithmetic to pattern rewrites. They are a fundamental and versatile aspect of MLIR's structure. As part of writing this dialect in MLIR, I wrote tensor operations like outer product, matrix multiplication etc in MLIR. Objective of the project was to provide an end to end compiling solution of tensorflow/pytorch graphs via MLIR to IBM MMA unit.
In this project I perform tweet sentiment analysis on tweets related to coronavirus. One of the objectives I was exploring was the prevalence of zipf’s distribution in such a dataset. If hashtags in such a dataset follow zipf's distribution. Other objectives were comparing multiple approaches including statistical methods for sentiment classification and fake news classification.
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