I am a student at the University of California, Davis studying Computer Science and Engineering. My concentrations are Software Engineering and Computer Systems.. I aspire to advance my understanding of these fields to the highest level through industry experience and research opportunities.
Served as a Fullstack Software Engineer Intern for the Adobe eCommerce Platform. I created an internal testing framework which allowed Adobe Checkout team developers to test/validate any new, existing, or error scenarios using mock data. This ultimately sped up ideation and development time for new products and features as their behavior and stablity were now able to be tested. Technologies I used throughout the tenure of my internship were TypeScript, Java, GraphQL, Redis, React.js, Node.js, AWS (S3, Labmda, EC2, Kubernetes), Jest, Splunk, and Docker.
Software Engineer for a student-run start up at UC Davis. My team and I are working on a cross platform roommate matching application which will serve the whole UC Davis community. I focus primarily on backend development such as creating and managing APIs and databases. Technologies I am working with include Java Springboot, Typescript, React-native, DynamoDB, various AWS tools, and Docker.
Serve as a Computer Science Tutor for the CS department at UC Davis. Tutor for courses such as general programming, object-oriented programming, data structures, and algorithm design and analysis. I also host midterm and final review session for these courses. Additonaly, I keep track of tutee performence and understanding to created more specific and ideal tutoring strategies.
Assisted Dr. Tianxi Cai and Dr. Aaron Sonabend with research within data science. I was introduced to the concept of machine and deep learning, and specifically convolutional neural networks (CNN). We focused on the practicality of these practices and their applicability to our daily lives. I explored CNNs through the Tensorflow and Keras frameworks and also delved deeper into the mathematics of the processes, such as linear algebra. For a final project, I collaborated with a team and built a successful self-driving car. The car was built by our team from scratch and we then programmed a CNN that associated different sets of images to distinct actions. The CNN was trained with about 5000 pictures and was then implemented into a Rasperry Pi that was mounted and connected to the car. The car had a camera which would feed what it is currently seeing into the model, and engage in the action (foward, reverse, left, right) that returns the highest probability.
Focus on STEM Subjects
Noticing a lack of and strong need of a organized and streamlined UC Davis athletics website, I created a new website for the UC Davis Athletics department which is completely student facing. I started off by creating a backend MongoDB database which held all user data. User data included personal information (hashed passwords), student information, and ticket information. Created an API which scrapes live data from the UC Davis atheltics website. From then, the frontend was created using Flask with a large calendar displaying all current, past, and future events. Implemented log-in system integrataed with OAuth. Designed a visual ticket interface. Sent text message notifications to users based on interactions with website, and implemented Geolocation to check into events. Website is currently under consideration by UC Davis Athletics.
To further explore deep learning, I made a deep learning model based on the MNIST database. The MNIST database is a set of 60,000 images of handwritten numbers from zero to nine. The model I created predicts and identifies which handwritten number is being shown. Using TensorFlow and Keras, I trained the model by implementing a convolutional neural network with various layers. Prior to training the model, I used Pandas and NumPy to apply statistical analysis to ensure the dataset is effectively organized and shuffled, and to one-hot encode the data from the model. After training, I attained a prediction accuracy of 98%. I then created a graphical user interface that allows users to draw numbers from zero to nine and receive a live prediction from the model. Overall, this project encorporates a deep learning model to a graphical user interface and allows users to interactively see how a deep learning model can work, where it can be improved, and how it can be useful.
For a final project in ECS 36C: Data Structures and Algorithms, I generated a simulation that found the fewest number of days certain populations will be completely infected with Covid-19. I designed a shortest path algorithm that was based on the standard principles of Dijkstra’s Shortest Path algorithm. Given a list of people within the population, how often they meet each other, and how many other people they each meet, I created a weighted graph with the population as nodes and the days they meet each other as the weights of the edges. Through this setup, I was able to create an efficient algorithm that successfully found the fewest number of days the population would be infected.