While completing my Master’s in Data Science at the University of Pennsylvania, I have a strong interest in AI and blockchain.

Education

Master of Science in Engineering (MSE) in Data Science

University of Pennsylvania
Sept 2024-Present

Bachelor of Science in Statistics and Data Science

University of California, Santa Barbara
Sept 2022- Jun 2024
Technical Strengths
  • Excel
  • R
  • Python
  • SQL
  • JavaScript
  • Java
  • Typescript
  • Angular
  • Node.js
Experiences

Northern Light Venture Capital – Analyst Intern

Early staged venture capital focusing on health and consumer/enterprise tech
Shanghai, CN
-
Mar 2024~Aug 2024
  • Conducted comprehensive financial analysis and valuation of legally compliant cryptocurrency exchanges (Coinbase and Robinhood), as well as AI-data platform, Palantir. This involved examining company prospectuses, analyzing earnings reports from the past eight quarters, and assessing product/user data, ultimately outperforming the QQQ index by over 30%.
  • Conducted detailed research on ARK Invest, analyzing their unique investment strategies and performance. This included evaluating their technology-driven methodologies and comparing them to traditional investment approaches.
  • Sourced, researched and wrote concise informational one-pagers for more than 20 companies in the categories of ABC (AI, Crypto, and Battery.
  • Utilized R and RSQL to analyze over 2,000 lines of user data from Excel for Temu's E-commerce platform, contributing to the projection and valuation of its parent company, PDD.

Keo Technology Group – CEO & Data Head

Ed-tech startup building no-code, interactive, digital infrastructure for the education sector.
Shanghai, CN
-
Nov 2019~Dec 2023
  • Raised $590,000 USD Seed investment from US-based venture capital firm, AMINO Capital, and HK-based private equity firm, Mandra Capital
  • Spearheaded product management, design, and growth from zero to one achieving 5.5x MoM user growth (Jan-May 2022), 2.5MM+ total users, 50k daily active users, and 30% user weekly retention
  • Managed a cross-cultural 20-person team across software development, product design, and marketing and operation functions
  • Led digital media strategy, constructing dashboards and key review metrics to quantitatively assess marketing results, achieving 100+ MM cross-platform engagement, ≈$0.02 CAC, 250k+ social media followers
  • Implemented efficient management frameworks and software (OKR, Notion, Slack, Jira) to structurally reduce miscommunication and improve execution for onshore and offshore employees
  • Responsible for financial forecasts and budgeting from daily accounting to financial models and reports for investors.
  • Constructed an entire B2B SaaS pricing and sales funnel and signed 1.2 million users with notable clients spanning from public universities to private institutions, to Bytedance (parent-company of Tiktok)
  • Demonstrating team’s work to different audiences using multiple visualiatoin tools

EY-Parthenon –  Data Analyst Intern

Remote
-
12/2022~01/2023
  • Conducted market research on Chinese e-commerce and short video industries, gathered and analyzed data from various sources.
  • Synthesized research findings into a 40 page comprehensive report on the current state of the market and future trends.
  • Worked with 2500 lines of client excel data to summarize and analyze user data through using SQL and R
  • Conducted hypothesis testing to identify any real significance of different payment and product cart pages for mobile app to improve conversion rates.
Works

Keo Space

Cloud Workspace for productivity-hacking and relationship-building.
Helping millions of individuals improve productivity, fight loneliness, and escape unsuitable working environments.
2.5m+
Trusted
total users
60m+
Platform
focused minutes
100m+
Cross-media
impressions
~3x
User
growth MoM

Keo Plus AI LMS

AI powered content creation
Building courses has never been easier. Work with your AI assistant to source, generate, and structure your course in minutes. Supercharging millions of creators and educators to curate the knowledge of the world.
The aim of this project is to build machine learning models to predict the forest cover type based on cartographic variables.

Machine Learning Project

Predicting Tree Cover Type
Employing advanced machine learning techniques, this project adeptly predicts forest cover types using cartographic variables, leveraging a rich Kaggle dataset to address key ecological and conservation questions with a blend of KNN, Multinomial Regression, Random Forest, and Boosted Trees models
Download report
Social Media Portfolio