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Myoungchul Kim
Myoungchul Kim
Myoungchul Kim

Myoungchul Kim

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Myoungchul Kim

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I am a Data Scientist and a Le Wagon graduate, passionate about solving real-world problems with data. Through the intensive and practical bootcamp curriculum, I have gained hands-on experience in Python, data extraction, manipulation, and visualization, machine learning, deep learning, and ML engineering. I am proficient in using Scikit-Learn, TensorFlow, and GCP for building comprehensive workfl

Ambition

In the future

I am a Data Scientist and a Le Wagon graduate, passionate about solving real-world problems with data. Through the intensive and practical bootcamp curriculum, I have gained hands-on experience in Python, data extraction, manipulation, and visualization, machine learning, deep learning, and ML engineering. I am proficient in using Scikit-

Le Wagon Tokyo3 months

Data Science & AI Bootcamp

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• Thorough study in Python for Data Science, with expertise in data extraction, manipulation, and visualization, backed by a strong foundation in statistics and linear algebra. • Delving into Machine Learning and Deep Learning, with practical application in building comp

  • Sound to Symphony (AI Music Generation)

    • Generates completely new music by Recurrent Neural Network (RNN) that can be easily customizable by musical software • Architectures RNN model for learning musical patterns from large classical music datasets that are expressed in numerical format. • Deplyed the project into the Streamlit by utilizing FastAPI • Built the connection between generated music and musical software Abelton

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千葉大学 ChibaUniversity7 years

融合理工学府・先進理化学専攻・物理学コース

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• Performed high-precision calibration for the radio-frequency antenna for an advanced research instrument. • Established scientific Python & C++ hybrid package, inspired by C++-based code, that extracts physics results from raw data which has led to wildly use by intern

  • Search for Ultra-high Energy Neutrinos from Askaryan Radio Array (ARA) by Template Method

    • Classified astronomical signal by statistical-oriented Principal Component Analysis (PCA), after obtaining features from 2 billion amounts (∼200 TB) of radio-frequency data measured below the South Pole. • Implemented automation solutions for utilizing large CPU & GPU clusters by building Python & C++ packages to streamline data analysis workflows and enhance productivity which has led to wildly use by international collaborators. • Implemented physics techniques, such as the Fast Fourier Transform (FFT), Interferometry, and the Matched Filter, into the package for feature extraction. • Optimized the PCA based on Frequentist Statistics and Pseudo Experiment. • Analyzed & quantified results by calculating statistical significance, including systematic uncertainty, and Monte Carlo simulation. • Performed high-precision calibration for the radio-frequency antenna for an advanced research instrument. • Learned large database management, including optimization of data sourcing and efficient connection to supercomputer by using solid analysis pipeline. • Practiced a thorough way to evaluate the project results by using statistical techniques and the back-of-the-envelope calculation.

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Skills

  • Python

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  • tensorflow

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  • Deep Learning

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  • Statistical Data Analysis

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  • Google Cloud Platform

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  • Monte Carlo Simulation, Interferometry, Convolutional Neural Networks and 46 skills

Accomplishments / Portfolio

言語

  • English - Professional
  • Japanese - Professional
  • Korean - Native
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