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Education

University of California, Berkeley
B.A. Data Science; B.A. Statistics — Expected May 2027
GPA: 3.96

Major Coursework (expand)

Topics: Machine Learning; Artificial Intelligence; Natural Language Processing; Deep Neural Networks; Data Mining; Data Analytics; Computer Security Principles; Data Structures; Relational Databases; Combinatorial Algorithms; Computing in Data Science; Numeric Libraries; Experimental Design; Probability Theory; Random Processes; Reproducible Statistics; Discrete Mathematics; Linear Algebra; Multivariable Calculus; Numerical Methods; Mathematical Modeling

Coursework:

Computer Science

  • CS 270 — Combinatorial Algorithms + Data Structures
  • CS 188 — Introduction to Artificial Intelligence
  • CS 170 — Efficient Algorithms + Intractable Problems
  • CS 61B — Data Structures
  • CS 61A — Programming Structures

Data Science

  • DATA 145 — Statistical Inference and Decision Analytics
  • DATA 144 — Data Mining and Analytics
  • DATA 100 — Computational Data Analysis

Statistics

  • STAT 238 — Bayesian Statistics
  • STAT 159 — Reproducible Statistics and Data Science
  • STAT 158 — Experimental Design
  • STAT 133 — Concepts of Computing
  • STAT 20 — Introduction to Probability and Statistics

Electrical Engineering and Computer Science (EECS)

  • EECS 126 — Probability and Random Processes

Mathematics

  • MATH 104 — Introduction to Analysis
  • MATH 55 — Discrete Mathematics
  • MATH 54 — Linear Algebra + Differential Equations
  • MATH 53 — Multivariable Calculus
  • MATH 52 & 51 — Calculus

Applied Fields

  • PSYCH 162 — Human Happiness
  • ECON 119 — Behavioral Economics
  • ECON 100B — Macroeconomics
  • UGBA 103 — Intro to Finance
  • UGBA 102A — Financial Accounting
  • UGBA 101A — Microeconomics for Business Decisions
  • UGBA 10 — Intro to Business
  • ASTRO C12 — Planets
  • ECON 2 — Introduction to Economics

Skillset

Core

  • Languages: Python, Java, JavaScript, HTML/CSS, R, Lua, Scheme, Shell
  • Databases: Postgres, MySQL; SQL / NoSQL
  • Frameworks / Tools: Node.js, React, JUnit, Git, Docker, Kubernetes, Jenkins
  • ML / Data: PyTorch, TensorFlow, JAX, Jupyter, pandas, NumPy, Matplotlib, scikit-learn, Alembic

Systems & Focus Areas

  • Deep Learning, Neural Networks
  • Data Analysis (Geospatial, Exploratory)
  • Distributed Systems, Networking

Exposure

C++, CUDA, Microservices, AWS, Go, Kafka, Cassandra, Elasticsearch, Kibana

Experience

  • Machine Learning Engineer - Pointspace Inc. (Apr 2024 - Present)
  • Undergraduate Researcher — Social Psychology and Business Lab (Aug 2024 – Present)
  • Research Intern (Data Science, Software Engineering) — Haas School of Business (Apr 2025 – Feb 2026)
  • Coach / Mentor — Berkeley Chess School (Jan 2024 – Present)

Publications

  • White Paper: ML models predicting risk of business closure (SMBs); built on prior Random Forest study (~90% accuracy) — Apr 2025
  • Research Paper (Coauthor): Boom or Bust: COVID-19 Impact on Bay Area Businesses; presented at Honors Research Symposium, Stanford University — May 2023

Leadership & Organizations

  • President — Statistics Undergraduate Student Association (SUSA), Berkeley, CA
  • Mentor — Computer Science & Data Science, Berkeley, CA