Hello! I am Hahyung Jung.
I graduated from Columbia University
with a major in Data Science.

I’ve experienced machine learning models at scale, data mining with big data, object‐oriented languages, full‐stack development, distributed systems, data structures & algorithms as well as statistics. I’ve been recognized by managers and peers as driven, reliable, and quick to learn, and am eager to do the same for a technology‐driven organization. I combine these traits with proven ability in communication and expand its boundaries even further.

• Resume

Recent Projects

Blockchain Development

Bulit a private blockchain for cryptocurrency with transactions, wallets, signatures, and mining, implementation in Go with REST API, CLI, HTML Blockchain Explorer, BoltDB, and Websockets P2P Network

• Github

K-Culture API & Web App

Deployed a website offering an extensive database of Korean movies, TV shows, and cast information by builiding a GraphQL API Server in Node.js and setting up Apollo Client for GraphQL integration into a React Application

• Website

• Github

Go&Stop Game (On App Store)

Published a card game on the App Store, which involved large decision trees using SpriteKit in Swift for iOS development; developed a computer player using an Artificial neural network based on Monte Carlo tree search; designed all its images

• App Store

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Music Therapy Health Research

Analyzed treatmentsʼ short-term effect on anxiety, including 86 participants, and utilized EEG brainwave monitoring the evaluate effects of classical music; ran hypothesis testing and presented data insights at a conference; selected for publication for civic scholar 2019 in the Phi Theta Kappa (PTK) organization conference

• Full Portfolio -- Github

Others

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• Full Portfolio -- Github

Publication & Research Papers

Semi-Supervised Learning Project (2022)

In this project, I studied the training method given most unsupervised datasets and a small number of labeled datasets, and it is called semi-supervised learning. Consistency regularization is one of the representative methods in semi-supervised learning. Therefore, I analyzed the effectiveness of consistency regularization by perturbating images differently In CIFARs, the well-known toy image datasets. Furthermore, I experimented by changing the model parameters to validate the effectiveness of semi-supervised learning by the model size.

• Read

Music Therapy Health Research (2019)

We analyzed treatmentsʼ short-term effect on anxiety, including 86 participants, and utilized EEG brainwave monitoring the evaluate effects of classical music. We ran hypothesis testing and presented data insights at a conference. Our project was selected for publication for civic scholar 2019 in the Phi Theta Kappa (PTK) organization conference

• Read

Skills

Data/Programming/Framework/Tool

C#, C++, Python, Go, Java, Swift, MATLAB, HTML, CSS, JavaScript, REACT.js, Node.js, Express.js, Django, Flask, FastAPI, Streamlit, ngrok, AWS, OpenCV, TensorFlow, Numpy, PyTorch, Selenium, Scikit-learn, NLTK, R, PostgreSQL, MongoDB, MySQL, Hadoop, Spark, Git, UNIX, Linux, Docker, Kubernetes, JIRA, Tableau, Jenkins, MS Office

Technical

Data Structures & Algorithms, Artificial Intelligence, Machine Learning Modeling, Data Mining, Regression Testing, Statistical Analysis, Data Visualization, Cloud Computing, Database management system (DBMS), SQL, NoSQL, Agile, NLP, OOP, Blockchain

Photos - Backpacking Africa & Alaska

Honors and Awards & Scholarship

Get In Touch

If you have any questions, feel free to contact me.