Jiwoong (Gio) Choi

This paper examines the "Board and Ownership and Control" Key Issue within the MSCI ESG Ratings Corporate Governance Theme, with a focus on board independence and adherence to the one-share-one-vote (OSOV) principle. Our analysis reveals significant governance challenges among Korean companies. Notably, less than half of the directors on Korean company boards are independent, a figure significantly lower than the global average of 66%. Additionally, 82% of Korean companies were flagged for Related Party Transactions (RPT), with smaller firms exhibiting lower board independence. Furthermore, Korean companies that deviated from the OSOV principle demonstrated weaker financial performance, with both return-on-equity (ROE) and price-to-book ratio (PBR) falling below the market average. These findings underscore the need for enhanced governance practices within Korean corporations to align more closely with global standards.

- Conducted SDG Gender Outlook research as part of the Women Count Flagship Programme on Gender Statistics, in charge of SDG 4th, 9th, 16th goal.
- Analyzed the household survey data, such as DHS and MICS data, and visualized the result in R and Python.
- Utilized GIS data to find the association between illiteracy and the time required to reach dense areas in Cambodia, Philippines, and Myanmar.

- Analyzed stock market regimes of the United States using 10 macroeconomic indicators, specifically focusing on the Financial Stress Index(FSI).
- Developed a classification model to identify bear and bull markets based on FSI changes.
- Achieved 85.96% prediction accuracy in the test set data, utilizing a RandomForest and Deep Neural Network(DNN) and implementing techniques to prevent overfitting.

This paper proposes a robust visual SLAM and a path planning algorithm for autonomous vehicles in the outdoor environment. The consideration of the outdoor characteristics was essential in both SLAM and path planning processes. This study can be used when it is necessary to know the exact appearance of the environment due to the impossibility of observing the environment through a satellite map, e.g., inside a forest. The visual SLAM system was developed using GPS data in consideration of the deterioration of camera recognition performance outdoors. The GPS data was inserted into every multi-thread of visual SLAM, which are Camera Tracking, Local Mapping, and Loop Closing. It enhanced the accuracy of the map and saved computational power by preventing useless calculations. In the path planning part, our method divided the path based on the stability of the roads. When determining the optimal path, the stability of the road and the driving time were considered, and the weight was assigned based on the GPS data.

- Collected text data from YouTube videos, YouTube comments, News, and Bank Reports by STT, OCR, and Crawling methods.
- Fine-tuned Google’s ELECTRA model which is a GAN-based transformer model by PyTorch.
- Conducted a Sentimental Analysis and made a prototype service.
- Gave a presentation on behalf of our team and finally achieved 4th place out of 1,000.

- Aimed to develop a Korean NLP model and Translated the English dataset into Korean by API.
- Utilized LSTM and GRU networks by PyTorch first.
- Conducted Transfer Learning of BERT multilingual base model for Korean and KoBERT.