I'm Ashish Basetty, a computer engineering student at UCLA who enjoys working on interesting projects and exploring new technologies and tools. When I'm offline, I also enjoy going to concerts, trying new cuisines, and learning to dance to fun choreos.
- 👨🏾💻 Coding / Software Development: Java, C++, C, Rust, OpenMP, Python, Git, Linux/Unix Systems, Docker, Robot Operating System (ROS)
- 👷🏾♂️ Webdev/Backend: JavaScript, TypeScript, Flask, Node.js, MongoDB, Firebase, OAuth, SQL, SQLAlchemy, React.js, WebSockets
- 👨🏾🔬 Data Science / ML: Scikit-learn, Pandas, Numpy, PyTorch, OpenCV
- 🤖 Machine Learning: PyTorch, OpenCV, YOLO detector
- 🏭 Engineering: MATLAB, Verilog, Digital Design
You can reach out or learn more about me through the below links:
An overview of a couple of my projects posted on my github. Feel free to take a look at some of my work so far!
For my senior capstone project, I and a team of 3 others are currently building a hands-free cooking assistant, which helps with recipe suggestion, ingredient management, and guiding users through the cooking project. A more hardware focused project, we have integrated bluetooth devices and computer vision to automatically detect temperature requirements and inventory management. My work focuses on building the backend systems and the LLM integration, including the Flask server and SQL database for inventory, recipe, and user management, as well as working with LLMs to handle natural user language. I am currently working on optimizing the LLM integration, using embeddings and vector databases, and potentially LangChain and RAG models to improve the natural language capabilties of our language model. It's a work in progress, but I'm proud of the work we've done so far!
I and a friend entered the GTVO hackathon, focused on increasing voter participation and eduction. Our specific project was a civic education app, with integrated live quizzes and ranked matchmaking. Gamifying the learning experience, PoliProof empowers users to test their knowledge, compete with others, and earn a credibility score—called the Ethos Meter—that reflects their understanding of critical issues. I specifically focused on the backend, building out the MySQL database and Node backend, combining WebSockets to allow for live multiplayer and integrated it with a fault-tolerant matchmaking system. I also worked on dynamic quiz generation, automatic difficulty scoring based on an ELO system (and the overall ELO system), category-based filtering, and the user personalization system. In the end, we placed as Top 6 Finalists in the competition! Watch our hackathon submission demo here.
As a project lead of 15 engineers and designers, I led the development of a webcam-based productivity analyzer, using computer vision and machine learning to provide users statistics on their study habits and long-term trends. Taking on a more management-centered role, I had to deal with sprint planning, user research, and key feature decisions, while also contributing to overall system design and tech stack. In the end, I found the experience an interesting combination of the technical and management aspects of engineering project leadership, and our demo day was met with positive feedback! Watch our demo video here.
For my networking class, I and a team wrote TCP-style server and client programs to implement reliable and encrypted transport with UDP sockets. It was an interesting exploration into the networking protocols that underlie modern applications, and dealt with effectively handling issues of reliability even security. and I created handlers for timeouts, packet buffering, flow control, and packet processing in C++, and our final project handled all these issues effectively.
I and a team of four other engineers and many more designers and product leads built a chrome extension that automatically analyzes skincare ingredients lists. We extensively used ManifestV3 and API calls, as well as scanned webpages to identify ingredients lists and automatically load highlighted ingredients. We created a clean and detailed user interface and were able to reliably analyze ingredients based on the EWG database, allowing users to gain insights into the skincare products they were interested in. In the end, our product was published to the Chrome Web Store1
Fungex: the fun rust-based regex engine! As a unique hackathon project, I and a small team worked to build our own (basic) regex engine and visualizer using Rust. I had never worked with Rust before, but thought it would be a fun way to understand its unique memory management functionality and apply some of the concepts I learned in automata theory. I worked on converting an input regex string into a a string easily representing an NFA, which was then processed and used to conduct a pattern-matching search. In the end, I learned a lot and got a good intro to Rust as well as applied some more algorithms-focused CS knowledge.
For fun over summer 2023, I taught myself OpenCV and how to build and train a YOLOv8 model through tutorials. I built a simple model that could translate sign langauge in real-time from a video, and trained it using data from my room. While it could be more accurate with hand pose detection or more advanced hardware than my PC's GPU, I am satisfied with the results and seems to work somewhat effectively given my dataset. Watch a real-time demo here.
In my class CS35L, I worked with a team to build a detailed event posting social app, using React.js, Material-UI, node.js, and MongoDB Atlas. I built out various features including secure login, maps and location search functionality, friends, likes, and a detailed suggestion algorithm that takes into a account filters and preferences. I ended up learning a lot of interesting technologies, including server-client functionality, Google Maps API, and password hashing and encryption. Here is a link to our team's product demo, thanks for checking it out!
For my musical analysis project, I used data science and a set of around 60 songs to train a basic model to distinguish between rap, acoustic, and EDM music. It turned out to have a decent accuracy of around 75% for classifying new music from one of the three genres, and I also built a basic ML model to try and classify data. While the ML model didn't prove effective likely due to overfitting, it was a good experience in applying data science to a practical application.
With my hackathon team at LA Hacks, we built a site that allowed users to customize and use alternating study timers, with a social design and a simple backend. I applied google authentication and firebase document models to store user study hours and create unique user accounts, as well as work on various pages on the site. Here is a demo we made for the hackathon.
At my first hackathon, we built a basic website to host listings for free food around campus. While we were not able to completely refine the site, learned to apply vanilla React.js as well as connected a Firebase backend to create a dynamic site.
Using a tutorial, I was able to build a CNN based classifier for a set of common objects from scratch using PyTorch. I learned how to implement neural networks and the thought that goes into choosing network layers, and learned how to use the inbuilt batch training and hardware acceleration. While this is the most recent repo by date, I actually completed this project early on but failed to upload it to github until this summer.