Hi, I'm Weihang Zheng.
A
Assertive and strategic thinker, valuing logic and efficiency, often driven by a clear vision and a desire for autonomy in their pursuits.
About
Weihang is a 4th year ECE student at the University of Toronto. In the industry, he has done internships at Cerebras, Huawei, and Splunk, working on implementing testing frameworks and big data speedups using LLVM compiler optimization. In Machine Learning (ML) Research, he has published papers in Neural Architecture Search on state-of-the-art models solving pathology and brain signal classification tasks. For his capstone, he is working on Reinforcement Learning to solve the Nash Equilibrium problem in an adversarial network. He has been recently dabbling in the startup space trying to acquire full stack skills.
- Languages: Python, Java, JavaScript, C, C++, HTML/CSS, Bash
- Databases: MySQL, PostgreSQL
- Libraries: NumPy, Pandas
- Frameworks: Flask, Tensorflow, PyTorch, Bootstrap
- Tools & Technologies: Git, Docker, AWS, Heroku, Firebase, JIRA
Looking for an opportunity to work in a challenging position combining my skills in Software Engineering, which provides professional development, interesting experiences and personal growth.
Experience
- Researched 5+ alternative ways of sending GitLab pipelines over to Splunk’s Automation Result Triage App, including manipulating: HTML/Javascript, Splunk Search Processing Language macros, and HTTP Collector environment variables.
- Implemented one of the above methods for the Splunk Trusted Applications and Add-ons testing workflow to reduce the time needed for full-time engineers to triage test failures by 10x+.
- Implemented a C++ class to hash any primitive, wrapper, and container data type in the C++ Standard and STL library, to cache parameters to speed up LLVM Just-in-time compilation-based database queries by 10%+.
- Archetyped an efficient workflow for building, invoking, retrieving TPC-DS query results on cluster servers using Jenkins; hosting Flask and Grafana Docker servers to store and visualize live query results.
Leveraged GPT-3.5-turbo-16k API calls in a multithreaded LOOCV pipeline to prompt engineer nuanced essay feedback. Created a pipeline combining GPT to translate natural language into a SQL query to return sets of relevant data from a search bar from a Heroku PostgreSQL database. Modeled solution to a pathfinding problem with known Algorithm, Data Structures, and techniques: K-Means Clustering, Dynamic Programming, Gradient Descent, Trie, Heap, and GloVe Embeddings. Utilized Firebase’s Realtime Database to create a secure account creation and login mechanism for users. Oversaw the tech stack of the Minimum Viable Product: containerized Docker Flask backend and the Svelte front end. Experimented with successfully deploying to Apple Store in the Swift UI prototype of our product.
- Leveraged GPT-3.5-turbo-16k API for nuanced essay feedback.
- Created a pipeline to translate natural language into SQL queries.
- Utilized various algorithms and data structures: K-Means Clustering, Dynamic Programming, Gradient Descent, Trie, Heap, and GloVe Embeddings.
- Implemented a secure account creation and login mechanism using Firebase's Realtime Database.
- Oversaw tech stack: Docker Flask backend and Svelte frontend for the Minimum Viable Product.
- Successfully experimented with deploying to the Apple Store using Swift UI prototype.
Skills
Languages and Databases
Python
HTML5
CSS3
MySQL
PostgreSQL
Shell Scripting
Libraries
NumPy
Pandas
matplotlib
Frameworks
Flask
Bootstrap
TensorFlow
PyTorch
Other
Git
AWS
Heroku
Firebase
Education
Toronto, Canada
Degree: BASc, Computer Engineering, minor in Artificial Intelligence and Business
Duration: September 2019 - May 2024
Accolades: Dean’s Honour List all semesters (6x); 3.74/4.0 cGPA, 85.2% average
Graduate Course: Algorithms and Data Structures
- Operating Systems
- Networking
- Compilers and Interpreters
- Software Engineering
- Machine Learning
- Math
Relevant Courseworks: