Profile

Aspiring data scientist with entrepreneurial grit in the manufacturing and software services industry. An experienced co-founder who has confronted and solved numerous challenging problems with insane persistence and concentration. Well-versed in producing data-driven actionable solutions with quantitative analysis, statistical learning and deep learning modeling, and data mining.

Education

Sep 2023 – Jan 2025 / Boston University / MS in Applied Data Analytics / Dept. of Computer Science

Sep 2016 – May 2020 / University of San Francisco / BS in Data Science / Data Institute and Dept. of Mathematics & Statistics

Experience

Feb 2025 – / Limbic Technologies / Data Scientist & Founder

Dec 2020 – Mar 2023 / Hanwha R&D Center / Data Scientist & Research Engineer

Jun 2011 – May 2016 / ITA Co., Ltd. / Data Analyst & Founder

Ki B. Kim

Data Scientist

/ * individual work / ** team work /

**Statistics 1 - [Significance Test, Method of Steepest Descent, Saddle Point Approximation]

**Statistics 1 - [Significance Test, Method of Steepest Descent, Saddle Point Approximation]
*Statistics 2 - [Statistical Inference, Statistical Hypothesis Test, Regression Analysis]

*Statistics 2 - [Statistical Inference, Statistical Hypothesis Test, Regression Analysis]
*Statistics 3 - [Statistical Hypothesis Test, Regression Analysis]

*Statistics 3 - [Statistical Hypothesis Test, Regression Analysis]
*Statistics 4 - [Regression Analysis]

*Statistics 4 - [Regression Analysis]
*ML 1 - [Statistical Learning Modeling, Image Processing, Cluster Analysis, Recommendation System]

*ML 1 - [Statistical Learning Modeling, Image Processing, Cluster Analysis, Recommendation System]
*Machine Learning 2 - [Feature Engineering, Statistical Inference, Statistical Classification]

*Machine Learning 2 - [Feature Engineering, Statistical Inference, Statistical Classification]
*Machine Learning 3 - [Time Complexity, Deep Learning Modeling]

*Machine Learning 3 - [Time Complexity, Deep Learning Modeling]
*Machine Learning 4 - [Feature Engineering, Statistical Learning Modeling, Deep Learning Modeling]

*Machine Learning 4 - [Feature Engineering, Statistical Learning Modeling, Deep Learning Modeling]
*Machine Learning 5 - [Cluster Analysis, Audio Signal Processing, Deep Learning Modeling]

*Machine Learning 5 - [Cluster Analysis, Audio Signal Processing, Deep Learning Modeling]
*Machine Learning 6 - [Evolutionary Algorithm, Deep Learning Modeling, Bayesian Network]

*Machine Learning 6 - [Evolutionary Algorithm, Deep Learning Modeling, Bayesian Network]
*ML 7 - [Time Series Anomaly Detection, Feature Engineering, Statistical/Deep Learning Modeling]

*ML 7 - [Time Series Anomaly Detection, Feature Engineering, Statistical/Deep Learning Modeling]
**Big Data 1 - [Big Data Analytics, ETL Pipeline, Cloud Computing]

**Big Data 1 - [Big Data Analytics, ETL Pipeline, Cloud Computing]
**Big Data 2 - [Big Data Analytics, Text Analysis, Sentiment Analysis, Cloud Computing]

**Big Data 2 - [Big Data Analytics, Text Analysis, Sentiment Analysis, Cloud Computing]
*Database 1 - [Database Normalization, Distributed Database, Dimensional Data Warehouse]

*Database 1 - [Database Normalization, Distributed Database, Dimensional Data Warehouse]
*Database 2 - [Database Design, Database Schema, DML, DDL, Data Pipeline]

*Database 2 - [Database Design, Database Schema, DML, DDL, Data Pipeline]
*Database 3 - [Query Plan, Database Performance Tuning, Query Optimization, OLAP]

*Database 3 - [Query Plan, Database Performance Tuning, Query Optimization, OLAP]
*Database 4 - [ETL Pipeline, Cloud Computing, Dimensional Data Warehouse]

*Database 4 - [ETL Pipeline, Cloud Computing, Dimensional Data Warehouse]