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






Machine Learning Platform Engineer
Sep 2021 – Present California
Work on big data machine learning pipelines and infrastructure.
Software Engineer Intern
Sep 2021 – Feb 2022 California
Worked on landing page. Tech stacks inclde JavaScript, React, Gatsby.js, Tailwind CSS, Node.
GIS Data Analyst/Research Assistant
Aug 2018 – Aug 2021 California
Worked on data collection, wrangling and analysis of GIS data for College of Chemistry and City Planning Department.



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