David Kahdian
David Kahdian
Computer Engineering & Applied Mathematics @ UCLA

Education

University of California, Los Angeles
Sept. 2024 - June 2028
Computer Engineering B.S., Applied Mathematics B.S. | Los Angeles, CA
  • GPA: 3.982
  • Honor Societies: Corporate Chair, Tau Beta Pi; Candidate, Upsilon Pi Epsilon and Eta Kappa Nu
  • Relevant Coursework: Software Construction, ODE's, PDE's, Game Theory, Analysis, Probability Theory

Technical Skills

Python Development

C/C++, Python, JavaScript(JS), TypeScript(TS), Java, SQL, Bash

Linux Tools & Platforms

Linux, VMware, VSCode, Jupyter, MATLAB, LaTeX, Markdown

PyTorch Libraries & Frameworks

PyTorch, pandas, NumPy, Matplotlib, Selenium

Jane Street Problem Solving

Solved July 2025 Jane Street puzzle; preparing for 2025 Putnam exam

Experience

AI Algorithms Researcher
Sept. 2025 – Present
UCLA StarAI Lab | Los Angeles, CA
  • Analyzing 12+ AI compilation languages for succinctness, and query and transform tractability
  • Developing graphical Knowledge Compilation Map visualizer website summarizing 20+ existing papers
  • Extending research knowledge of probabilistic circuit optimization techniques with planned publications
Risk Analysis Research Intern
June 2025 – Sept. 2025
UCLA Garrick Institute for Risk Sciences
  • Saved 100+ hours of recurring data entry tasks by automating conversion of 4 proprietary data types
  • Validated risk data for Diablo Canyon Nuclear Power Plant (provides 23% of California's carbon-free energy)
  • Improved statistical models in Fault Trees and Bayesian Networks, merging into production codebase via GitLab
  • Collaborated with 4 interns using C++, Selenium, Django, React, and CI/CD pipelines to streamline development
Data Analyst
Oct. 2024 - Dec. 2024
Harman International (JBL) & UCLA Epicenter Co-op
  • Built predictive model for 2025 digital marketing allocation in a team of 8, informing regional budget strategy
  • Optimized revenue to 11x marketing spend across 12 regions with psychographic segmentation
  • Built Excel-based quantitative model predicting 11% revenue increase with Lagrange multiplier techniques

Projects

Machine Learning Portfolio Optimizer
Python PyTorch Pandas Jupyter
  • Developed a neural network powered portfolio optimizer using Modern Portfolio Theory and deep learning
  • Mined 135 quarters of forward-looking macroeconomic indicators to train PyTorch neural network
  • Achieved 80% R² accuracy with 33 quarters of out-of-sample test data to prevent look-ahead bias
  • Optimized stock-bond ratio recommendation, published model to Jupyter Notebook
Heston Monte Carlo Simulator
Python NumPy JavaScript C WebAssembly
  • Created interactive web-based option pricing tool using Monte Carlo simulation techniques
  • Utilized Milstein discretization for 1000-step time series analysis, with comparison to Black-Scholes
  • Compiled C script into WebAssembly to 10x speed for Stochastic Differential Equation (SDE) computation
  • Accelerated simulations to 1000+ per second with only 100MB RAM and no parallelism
BruinPlan
Svelte TypeScript Puppeteer Cytoscape.js Python
  • Built web+mobile application enabling UCLA quarterly planning & prerequisite tracking
  • Developed Puppeteer script to collect 14,000+ courses and 130 majors of data
  • Created Python + Gemini API pipeline to convert raw data into JSON
  • Utilized Cytoscape.js and topological sort to create graph-based planning interface
FactCheckSpeech
JavaScript Selenium OpenAI API
  • Collaborated with three other students to create speech transcribing application
  • Produced Chrome extension capable of translating and fact-checking 100+ languages of speech
  • Awarded 2nd place in Congressional App Challenge by Congressman Adam Schiff