Data Engineer | Data Scientist

University of California, Berkeley


My research interests lie in understanding our world in intersections of the environment, society and technologies. In particular, I’m interested in creating and enabling efficient solutions using the data science toolbox including but not limited to Machine Learning, Deep Learning, Causal Inference & Experimentation, Data Visualizations and Geographic Information System.
I see our complex world as an infinite continuum of connected events that we are constantly trying to understand in order to make informed decisions. As Griffiths Rev. in A world in a grain of sand puts it: ‘The advancing coastline pushed people inland, forcing local crowding, the mixing of cultures, and, most likely, causing conflict’.
My hobbies can be summed up in 3 words: trying new things. I do believe that life is like a box of chocolate, you never know what you’re going to get.

  • Big Data pipeline
  • Deep Learning
  • Network & Graph
  • Experimentation
  • Landscape & Environment
  • Calisthenics & Olympic Lifting
  • Master of Information & Data Science, 2021

    University of California, Berkeley

  • B.A in Geography, 2020

    University of California, Berkeley

  • A.A in Social Sciences & Humanities, 2018

    Rio Hondo College

  • Certificate in Automotive Technology, 2017

    Rio Hondo College






GIS Research Assistant
May 2018 – May 2019 California

Used GIS and data science skills to produce rigorous analysis on the nature of gentrification and displacement that support policymaking about equitable development. Used Python to wrangle and analyze urban and social media data, used ArcGIS to visualize results. Developed interactive, online maps from the results of data analysis and develop website content to present our research. Created Python course materials for Urban Data Analytics course to teach other students spatial and network analysis.

Responsibilities include:

  • Data Wrangling/Joining/Analysis
  • Interactive Map Building
  • Spatial & Network Analysis
  • Analytics Curriculum Building
GIS Research Assistant
Oct 2019 – May 2020 California

Geocoded California statewide traffic collision data for Transportation Injury Mapping System (TIMS) with the goals of reduce the amount of fatalities and serious injury on the roadways.

Responsibilities include:

  • Geocoding
  • Developing & Updating CA boundary layers
Data Analyst
May 2020 – May 2021 California

Discovered and collected large amounts of employment data of alumni via LinkedIn Sales Navigator to better engage UC Berkeley alumni and identify new donor prospects. Perform query-based requests and analyze publicly available data on candidates to assist supervisor on engagement. Analyze IPOs, recent mergers, companies that experienced unprecedented growth in recent years.

Responsibilities include:

  • Data Collection
  • Data Storage & Query
Software Engineer Intern
Sep 2021 – Feb 2022 California
Worked on landing page. Tech stacks: JavaScript, React, Gatsby.js, Tailwind CSS, Node.
Machine Learning Platform Engineer
Sep 2021 – Present California
Work on big data machine learning pipeline and infrastructure.



Deep Learning in the Cloud and at the Edge
Learn practical approaches to deploy deep learning models on the cloud as well as edge devices such as the NVIDIA Jetson NX. Learn microservice applications and network communications like Kubernetes and MQTT, GStreamer
Experimentation and Causal Inference
Design, implement, and analyze our own field experiment, A/B testing, quantify uncertainty using confidence intervals and statistical power calculations to answer causal questions
Machine Learning at Scale
Learn to build large scale end-to-end ML pipeline using Databricks. Learn MapReduce parellel computing in environments such as Spark and Hadoop. Develop algorithms such as decision tree, pagerank and gradient descent
Privacy Engineering
Learn different threat models: attribute disclosure, identity disclosure, membership disclosure. Learn privacy desgin frameworks such as K-anonymity, L-diversity, T-closeness, Delta-presence, Differential Privacy. Learn privacy-utility tradeoffs
Applied Machine Learning
Learn and participated in hands-on projects using supervised models KNN, Decision Trees, Regression, Gradient Descent, Neural Networks and unsurpervised models such as Cluster Analysis, Gaussian Mixture, Dimensionality Reduction, Recommender Systems.
Geographic Information System
Learn the theory and application of Geographic Information Science/Systems and spatial problems that can be identified and solutions generated. Hands-on laboratory using ArcGIS tools to explore and address contemporary geographical and planning issues.
See certificate