Brandon Wang

I am currently a graduate of the University of Texas at Austin where I majored in Applied Learning and Development. Previously, I was a student at the Univeristy of Texas at Dallas studying Computer Science. Although I love all aspects of teaching, my interest in programming led me to pursue further education through online courses and obtain certifications in various areas of software engineering, such as full-stack development and machine learning. My passion and drive to learn in regards to programming has now led me to pursue a career as a Software Engineer.

The first step is always the hardest.

Skills and Activities

Some of the things I've picked up or done over the years

Proficiencies

Java, C++, HTML, JavaScript, React, Python, GameMaker, Unity, CSS, Git, Mars Mips, COCO2dx, Flask, Mandarin

Relevant Coursework

Calculus 1 & 2, CS I , II & III, Discrete Mathematics for Computing I and II, Computer Architecture, C++ Programming in a UNIX Environment, Physics I, Probability and Statistics in CS, Algorithm Analysis and Data Structures, Software Engineering, Harvard CS50p, FCC Responsive Web Design Certification, FCC Machine Learning with Python Certification, FCC Data Analysis with Python Certification

Student Organizations

  • Association for Computing Machinery Projects(2019-2020): Member
  • Computer Security Group(2019-2020): Member
  • UTD Blockchain(2019-2020): Member of the Surge Mentorship Program
  • Taiwanese American Student Association(2020-Current): Member
  • University Fencing Club(2020-Current): Member
  • Texas Crew(2021-2022): Team Captain

Professor Led Research Lab Assistant Experience

  • Fall 2019: Scaffolded Training Environment for Physics Problem-solving
  • Spring 2020: Research on Educational Games and computer graphics
  • Fall 2020: Research on Mobile Computing and Sensor Networking

Work Experience

  • Sharetea: Manager
  • BB.Q Chicken: Manager
  • AID(Assisting Individuals with Disadvantages): Volunteer
  • Computer Science Outreach Center: Teacher

Hobbies and Misc.

  • Best Project for ACM Projects 2019
  • 2018 Junior Olympics Qualifier
  • 2021 ACRA and SIRA B Finalist Winner
  • NASM and NCCA Certified Personal Trainer

RPEasy

RPEasy is an IOS app designed to help fitness enthusiasts track the intensity and quality of their workouts in real time. Built with SwiftUI for WatchOS, RPEasy takes heart rate data from an Apple Watch and uses an algorithm to calculate a user's Rate of Perceived Exertion (RPE) on a scale of 1-10. The RPE is displayed as a number at the top of the screen, while a real-time graph of the workout set is displayed below.
I created RPEasy as a passion project after hitting a plateau in my own fitness journey and needed additonal data to track the quality and intensity of my workouts. In creating the RPE calculation algorithm, I used my knowledge as a certified personal trainer as well as feedback from members of the Longhorn Powerlifting Team to calculate as accurate an RPE as possible

SentiSummary: Sentiment Analysis on YouTube Video Summaries

During the Covid-19 pandemic, my classes relied heavily on YouTube videos for remote learning, which often required lengthy annotations. In response, I created a Chrome extension using Flask and the YouTube transcript API to simplify the process by providing a transcript of the videos. However, I took it a step further and implemented Hugging Face's AI summarization pipeline based on the distilbart-cnn-12-6 model to streamline the transcript. As I took a government class that involved watching a lot of news channels, I decided to add rudimentary sentiment analysis using the TextBlob API and its NLP capabilities. This allowed me to gain insights into the emotions conveyed by news channels, providing me with a deeper understanding of the content presented.

Location Optimizer

During my senior year, my friends and I planned a weekend trip to New York City. While planning, I found comparing the prices and distances of potential hotels to be an extremely tedious task. To tackle this challenge, I utilized my backend development skills and integrated Google API to build a program that takes in the trip itinerary and outputs a list of optimal hotels. The program divides a rough circle around the locations into cells and checks for available hotels. Based on the travel time and price, the hotel information is then sorted and returned as a list to help users easily make an informed decision.

To my dismay, friends completely disregarded the optimized suggestions and chose Feather Factory which is both expensive and an hour away from anything in our itenerary.

Pocket Partner

Pocket Partner is a chatbot that utilizes the OpenAI API to provide fitness-related information to users. To optimize its responses with relevant and accurate information, I fine-tuned the Davinci model with my experience working with clients and their questions.
While working as a personal trainer, I noticed that many of my clients lacked knowledge about their body structures, experience levels, and time commitments, and that generic search results didn't always cater to their specific needs. I created Pocket Partner to make information more easily accessible and encourage safe exercise practices.

ACM-Climate

A semester long team project using an agile development cycle to create an interactive game using the COCO2dx engine and C++ that educates the public on climate change and its effects. As the SCRUM master, I was responsible for the entire development cycle, including Critical Design Review, deliverables and product backlogs, storyboarding, prototyping, and competitive analysis. Beacause of our team efforts, we won ACM Best Projects 2019!

Get in touch

I would love to hear from you!