Education

University of California, Los Angeles (UCLA)

B.S. Applied Mathematics & Statistics/Data Science with a Specialization in Computing (GPA: 3.81)

Relevant Coursework:

  • Python with Applications (PIC 16A, 16B)
  • Applied Numerical Methods (Math 151A, 151BH)
  • Linear Algebra (Math 115A, 115B)
  • Algorithms (Math 182)
  • Mathematical Modeling (Math 142)
  • Data Analysis and Regression (Stats 101A)
  • Design and Analysis of Experiment (Stats 101B)
  • Statistical Models and Data Mining (Stats 101C)
  • Computational Statistics with R (Stats 102A)
  • Computation and Optimization for Statistics (Stats 102B)
  • Monte Carlo Methods (Stats 102C)
  • Text Mining Using R (Stats 133)
  • Practice of Statistical Consulting (Stats 140XP & 141XP)
  • Machine Learning for Physical Sciences (AOS C111)
  • Mathematics for Life Scientists (LifeSci 30A)
  • Linear Models (Stats 100C)
  • Introduction to Research in Statistics (Stats 143)

Work Experience

Intern, The Stack

  • Wrote data-driven articles guided by multiple visualizations on topics relevant to the community
  • Utilized JavaScript libraries (i.e. Chart.js and Leaflet) to construct visualizations

Undergraduate Teaching Assistant, Learning Assistant Program

  • Handled the logistics of the program that aims to foster collaborative and inclusive learning in STEM (science, technology, engineering and mathematics) classrooms through the use of Learning Assistants
  • Implemented a JavaScript program using Google Apps Script and Google Forms to streamline the feedback process for the Learning Assistants

Project Associate, Follett Corporation

  • Implemented a program to scan for adopted books and managed inventories of educational resources

Reader, UCLA Math Department

  • Provided valuable feedback and graded students’ homework based on both completion and accuracy
  • Kept an organized record of over 1000 students’ grades across 6 different mathematics courses

Research

Efficient Hyperparameter Tuning for Resource-Constrained Environments

  • Optimized the performance of deep neural networks by proposing a framework for hyperparameter tuning that significantly reduces computational burden
  • Developed a framework using Latin Hypercube Sampling of initial points, surrogate models such as Gaussian Processes, and early-stopping criteria, and compared it to traditional methods like grid search

Projects

Major League Baseball (MLB) Pitcher Analysis

  • Conducted an investigation to determine whether a pitcher’s spin rate predicts the risk of injury as part of a consulting project
  • Main Components used: Multiple Linear Regression, Logistic Regression, Model Diagnostics

Classifying Soccer Tweets

  • Analyzed tweets from 7 European soccer clubs to examine various sentiments and predict tweet origin based on textual content
  • Main Components used: Quanteda, tf-idf, Bigrams, Sentiment Analysis, Latent Dirichlet Allocation, Random Forest

Airbnb Landscape in LA

  • Provided insights to Los Angeles Airbnb hosts on increasing profitability and created an interactive dashboard to help guests find the right listings
  • Main Components used: Tableau, Logistic Regression, Topic Modeling

Stellar Classification

  • Determined an astronomical object based on its spectral characteristics using classification models
  • Main Components used: Support Vector Machine, Adaptive Boosting, Artificial Neural Network

Effects of Various Substances on Memory

  • Identified the quantitative impact of various substances on memory cognition through statistical analysis, providing valuable insights into the ongoing discourse on psychoactive substances
  • Main Components used: ANOVA, G*Power, ggplot

Visualizing Zillow Homes

  • Assisted users in understanding and visualizing the distribution of homes for sale on Zillow, as well as estimating home prices based on their characteristics using machine learning
  • Main Components used: Zillow API, Flask, plotly, scikit-learn, Regression Analysis

Relevant Experience

Workshop Lead, National Student Data Corps at UCLA

  • Led workshops related to data visualization/machine learning by creating tutorials on Google Colab

Research Member, Bruin Sports Analytics

  • Worked on a yearlong research project analyzing player trends in the NBA (National Basketball Association) using deep learning

Finalist, ASA DataFest at UCLA

  • Participated in a 48-hour data analytics hackathon looking for any trends in the dataset regarding an educational interactive software called CourseKata


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