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Apr. 2021 –  Smart and Advanced environmental public heaLth sUrveillance System (환경부 연구과제)

It aims to develop a GIS-based environmental public health surveillance system using big data and machine learning. To be specific, we develop an environmental index, which can reflect environmental conditions and risks, by using environmental factors.

Jan. 2021 – Development of Intelligent Bio Multi-omics Analysis (게놈 특구 프로젝트)

Intelligent multi-omics analysis means an integrated analysis of biomedical data such as the genome, transcriptome, and epigenome with novel machine learning (ML) methodologies. Based on multi-omics data, it seeks to uncover disease mechanisms and develop more accurate ML models for disease prediction. It is anticipated to realize genome-based personalized and precision medicine and the expansion of the bio-health industry through the prediction, diagnosis, and treatment of hereditary diseases.

Jan. 2021 – Development of Big Data Processing Techniques and Diagnostics for Commercial Air Conditioners (LG전자 산학과제)

Development of anomaly detection and failure cause diagnosis algorithm using air conditioner sensor data.

Jan. 2021 – Smart Insole Advancement Project Based on Gait Data Analysis (중기부 연구과제)

It aims to improve smart insoles’ performance by developing an artificial intelligence model based on smart insoles’ gait data. Starting with the development of a weight prediction model through sensor data of smart insoles, a model applied to various fields such as ‘Disease Prediction’ and ‘Fitness Training Monitoring’ will be developed.

Jul. 2020 – Jan. 2021 Manufacturing Data Analysis and AI Model Development (KPX 케미컬 과제)

The purpose of this project is to predict defects and determine the cause of faults using the chemical mechanical polishing pad (CMP pad) manufacturing process data for semiconductors produced by KPX Chemical.

Mar. 2019 – Feb. 2023 Development of Deep Learning based Privacy-Preserving Federated Learning Platform for Artificial Intelligence (신진연구과제)

Developing deep learning based privacy-preserving federated learning system for artificial intelligence. As privacy has become an important issue, federated learning (FL) is being noticeable as a remarkable solution of machine learning (ML) in recent years. FL is an emerging configuration of ML techniques, where collaborative learning of a global model between individuals or institutions is possible with no migration of data under the administration of a central server.

Mar. 2018 – Sensor-Based Gas Detection Algorithm Development Project (산학융합원 과제)

It aims to develop a machine learning model for identifying the type of gas in mixed gas. It is possible to grasp the type of gas without human intervention through this model, which can learn from the data obtained through multiple sensors.

Apr. 2020 – Dec. 2022 Retail Recommendation System Development Project (중기부 연구과제)

This study developed a sequential recommendation system for retail. The sequential recommendation extracts user preference from a series of users’ actions. However, the purchasing sequence is hard to collect in the offline market. Therefore, the sequential data is determined by a shopping list and shopping routes and then is used for item recommendation.

Feb. 2020 – Mar. 2021 Financial Household Project

It aims to develop an overall financial health score based on the data-driven approach. By suggesting the household finance score, we can expect that the individuals can monitor their positions and manage to prevent or overcome the financial risk.

Jul. 2019 –  Leukemia Drug Prediction Project

It aims to develop a machine learning model for recommending optimal drugs to leukemia patients. The project is joint work with the Seoul St. Mary’s Hospital.

Jul. 2019 –  Prediction of Diabetes Causes Based on Genetic and Epidemiological Survey Data

This study developed a machine learning model to predict type 2 diabetes caused by multiple causes such as genetic and environmental factors. We constructed a model that can predict type 2 diabetes incidence with high performance by reflecting information on family history, lifestyle, genetic factors, and metabolites that can cause diabetes. In particular, to reflect genetic factors, a new indicator called genome-wide polygenic risk score (gPRS) was developed, thereby significantly improving type 2 diabetes predictability.

May. 2019 – Development of a Prediction Model for Crude Oil Import Volume (울산항만공사 연구과제)

Developing an accurate prediction model for crude oil import volume for Ulsan Port Authority (UPA). The model will assist UPA in terms of facility expansion plans of crude oil processing units and decision support.

Apr. 2019 – SK Lubricants Process Optimization (SK 루브리컨츠 산학과제)

Developing optimized prediction models for lubricant refinery process for SK Energy. In the process of refining crude oils to make Lubricants, many sub-processes are manually controlled. We try to improve such unstable processes using machine learning models and prediction model tools.

May. 2019 – Dec. 2019 Development of New Biological-Age Model (바이오에이지 산학과제)

A health index is a useful tool for people as it can work as a blueprint for managing their own health. To estimate one of the beneficial health indices, the Biological Age (BA), we developed a new BA algorithm based on representation learning to obtain better accuracy than existing methods.

Feb. 2019 – Voice Activity Detection Model Development Project

Creating a Voice Activity Detection Algorithm aims to implement an algorithm that can analyze participation patterns in discussions and the interactions between participants by classifying whether or not they are speaking when several speakers are discussing. In particular, this research field plays the most fundamental building block of quantitative analysis when studying interactions between people in organizational behavior theory in business administration.

Sep. 2018 – Aug. 2020 Development of Privacy-preserving and Secure Machine Learning-based Federated Prediction Models for Distributed Data (생애첫 연구과제)

Developing a novel strategy for handling sensitive data in a distributed setting by combining privacy methods and security techniques to achieve the best of both worlds.

Dec. 2018 – Aug. 2019 Development of Prediction Model for Thick-Plate Roughing Mill in POSCO (포스코 산학과제)

Improving current prediction model of roughing mill in POSCO. Steel plates are manufactured by hot rolling, which requires an adequate amount of torque applied on the slab. We applied machine learning models to improve the previous prediction model based on physical equations of roll-torque.


Student Awards

**UG-Under Graduate, G- Graduate

[2020.12.21] Statistical Data Analysis and Utilization Contest – Encouragement Award – Statistics Korea 

Kyeongbin Kim(G), Dongcheol Lim(G)

[2020.10.29] Molecular Structure Image SMILES Transformation AI Contest – 3rd Prize – LG AI & DACON 

Jaeho Kim(G), Yejin Kim(G), Dongcheol Lim(G)

[2020.02.18] L.POINT Big Data Competition – Encouragement Award – Lotte Members

Yeongjae Gil(G), Wonho Sohn(G), Seok-Ju Hahn(G) 

[2019.12.20] Paper Competition for Development of Ulsan- 3rd place – Ulsan Development Institute 

Dongcheol Lim(UG), etc.

[2019.11.22] 2019 Financial Big Data Utilization Idea Competition- Excellence Prize – BC Card

Kyeongbin Kim(G), etc.

[2019.11.08] Port Logistic International Conference Paper Competition- 2nd place – The Korea Port Economic Association 

Dongcheol Lim(UG), etc.

[2019.08.13] FIELD Camp Competition – 2nd place – Korean Institute of Industrial Engineers (KIIE)

Jae Ho Kim(UG), etc.

[2019.08.02] The 8th Big Data Analysis Competition – 3rd place – UNIST 

Jae Ho Kim(UG), etc.

[2018.09.11] Culture and Tourism Big Data Analysis Competition – 3rd place – Korea Culture and Tourism Institute 

Suhyeon Kim(G), etc.

[2018.06.30] Naver Data Science Competition 2018 – 2nd place – NAVER 

Yeongjae Gil(UG), etc.

[2017.08.08] The 6th Big Data Analysis Competition – 1st place – UNIST 

Suhyeon Kim(G), etc.