I am a first-generation graduate from the University of California, Berkeley with degrees in Data Science and Cognitive Science, and a minor in Computer Science. Over the past four years at Cal, I have dedicated myself to mastering backend development, data science, and machine learning, both through academic coursework and hands-on projects. My work ranges from developing cross-platform mobile apps and optimizing machine learning algorithms to conducting advanced research in neuroscience and creating data-driven solutions for real-world problems. Scroll down for a detailed look at my course work, skills, and projects. For resume or general inquiries, don't hesitate to reach out via email. I look forward to hearing from you!
These are the technical courses I took during my time at Berkeley, including ones I audited.
CS 10: The Beauty and Joy of Computing
CS 61A: The Structure and Interpretation of Computer Programs
CS 61B: Data Structures
CS 61C: Great Ideas of Computer Architecture (Machine Structures)
CS 70: Discrete Mathematics and Probability Theory
CS 161: Computer Security
CS 170: Efficient Algorithms & Intractable Problems
CS 188: Introduction to Artificial Intelligence (Audited)
CS 189: Introduction to Machine Learning
EECS 127: Optimization Models in Engineering (Audited)
COGSCI 1: Introduction to Cognitive Science
COGSCI C126: Perception
COGSCI C127: Cognitive Neuroscience
COGSCI 131: Computational Models of Cognition
COGSCI 132: Rhythms of the Brain: From Neuronal Communication to Function
COGSCI 144: Cognitive Science of Language
ELENG 198: Introduction to Neurotechnology
MCELLBI C61: Brain, Mind, and Behavior
MCELLBI 166: Biophysical Neurobiology (Planned)
PHILOS 3: The Nature of Mind
Data C8: Foundations of Data Science
Data 88E: Economic Models
Data C100: Principles & Techniques of Data Science
Data C102: Data, Inference, and Decisions
Data C104: Human Contexts and Ethics of Data
Data C140: Probability for Data Science
Data 198: Directed Group Studies for Advanced Undergraduates
Data C104: Human Context and Ethics of Data
Math 54: Linear Algebra and Differential Equations
Math 91: Special Topics in Mathematics
A feed-forward fully connected network and a convolutional neural network built using plain NumPy. The CNN, built with a LeNet architecture, was trained on the MNIST dataset and achieved a validation accuracy of 96-97%.
ViewA backend system built with Flask using the Fast F1 Python library to collect and preprocess Formula 1 race data, enabling users to get predictions based on inputs and historical data alongside interactive visualizations to display predicted outcomes alongside historical trends.
ViewA comprehensive analysis of EEG data from individuals undergoing dTMS for severe depression, developing and implementing advanced signal processing techniques for artifact removal, feature extraction, and time-frequency analysis and a classification model to identify patterns associated with treatment response and symptom improvement.
ViewI predicted housing prices in Cook County based on a variety of factors. In this project, I implemented a data processing pipeline using Pandas and used scikit-learn to build and fit linear models.
ViewWritten in Go, I utilized cryptographic library functions to design a secure file sharing system, which will allow users to log in, store files, and share files with other users, while in the presence of attackers.
ViewAs part of CDSS, I was in charge of creating a survey for a mentorship program. The backend stored data on Google Sheets, and a Python model was run on the data to create the best mentor/mentee matches.
ViewA program written in Java to simulate the Enigma machine used in WWII. The simulator has the ability to take initial configurations for the machine as well as encode and decode a message.
ViewA version-control system written in Java that mimics some of the features of Git. Gitlet supports committing, checking out, logs, branches, and merges. Objects used in this project are blobs, trees, and commits.
ViewA Python project that replicates the Hodgkin-Huxley model. It contains a simulated voltage-clamp experiment and comparison of Euler and Runge-Kutta integrations to solve the HH equation.
ViewDisclaimer: I work on refining projects all the time. Projects listed here are subject to change. I am unable to provide code for course-related projects publicly due to academic integrity and copyright.