데이터 분석과 정보 연구실에 방문하신 것을 환영합니다. 본 연구실은 적극적이고 열정이 있는 대학원생(석/박사)을 모집하고 있습니다. 데이터 분석에 관심 있는 학생들은 언제든지 문의(jtseong@jnu.ac.kr) 가능합니다. 

Welcome to the Data Analytics and Information Lab. We are looking for active and enthusiastic researchers (including MS or PhD students) to join our lab. Students interested in data analysis are welcome to contact us (jtseong@jnu.ac.kr).

Latest News (updated 1/September/2025)

• (Sep 2025) Congratulations!!!  Our labs Hackathon Team(Gi-Sung Hong, Maurice, and Bubacarr) won the Creativity Award at the K-DS Hackathon competition.

• (Sep 2025)  Jia Yang and Collins presented posters at the K-DS Academic Conference.

• (Sep 2025) Congratulations!!!  Jin-Taek Seong(PI) was appointed as a Tenured professor on September 1st, 2025.

• (Aug 2025) 박주영 학생과 오민재 학생의 석사학위 취득을 축하합니다. Congratulations for graduate of MS !!!

• (Aug 2025) Congratulations!!!  Gi-Sung Hong has been selected for the Research Incentive Grant Program for Master's Students supported by the National Research Foundation of Korea.

• (June 2025) Congratulations!!! Our paper as the corresponding author "Defect Prediction in CWDM Optical Modules Using Multimodal Learning"  was accepted in IEEE Access Journal (IF: 3.6 with top 34.8% JCR ranking).

• (June 2025) Congratulations!!! Our paper as the corresponding author "Temporal Dynamic Networks in Infectious Disease Modeling: A review of Methods and Applications"  was accepted in ICCCI 2025 (Category B ranking in CS conferences).

• (May 2025) Welcome!!!  Yoo-Kyung Park(박유경) has joined to our lab as MS course.

• (March 2025) Welcome!!!  Gi-Sung Hong(홍기성) has joined to our lab as MS course.

• (March 2025) Welcome!!! Botambu Collins has joined to our lab as Ph.D course.

• (Feb 2025) 김혜경 학생의 석사학위 취득을 축하합니다. Congratulations for graduate of MS course!!!

• (September 2024) Welcome!!! TWO MSc students(Bubacarr Jallow , Maurice Kyla Octaviano ) have joined our lab. We now have a total of 9 students in our lab.

• (June 2024) Congratulations!!! Our paper as the corresponding author "A NEW flexible exponent power family of distributions with biomedical data analysis"  was accepted in HELIYON Journal (IF: 3.4 with top 20.5% JCR ranking).

• (June 2024) Congratulations!!! Our paper as the corresponding author "Gait Asymmetrical Evaluation of Lower Limb Amputees using Wearable Inertial Sensors"  was accepted in HELIYON Journal (IF: 3.4 with top 20.5% JCR ranking).

• (June 2024) Congratulations!!! Our paper as the corresponding author "A novel cosine-derived probability distribution: Theory and data modeling with computer knowledge graph"  was accepted in Alexandria Engineering Journal (IF: 6.2 with top 4.7% JCR ranking).

• (June 2024) Congratulations!!! Our paper as the corresponding author "A new sine-arisen probabilistic model and artificial neural network methods for statistical modeling of the music engineering and reliability data"  was accepted in Alexandria Engineering Journal (IF: 6.2 with top 4.7% JCR ranking).

• (June 2024) Congratulations!!! Our paper as the corresponding author "A new weighted probabilistic model for analyzing the injury rate in public transport road accidents"  was accepted in Alexandria Engineering Journal (IF: 6.2 with top 4.7% JCR ranking).

• (May 2024) Congratulations!!! Jooyoung Park, a student in our lab, won the best paper award at the korea smartmedia conference. 

• (May 2024) Congratulations!!! Our paper as the first author "The Impact of Matrix Density and Threshold Variation on Threshold Group Testing "  was accepted in 2024 IEEE International Symposium on Information Theory(Top tier conference)

• (May 2024) Welcome!!! One Ph.D.(양지아) and MSc students(한재현) have joined our lab. We now have a total of 7 students in our lab.

• (May 2024) Congratulations!!! Our paper as the corresponding author "A new cosine-based approach for modelling the time-to-event phenomena in sports and engineering sectors"  was published in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (May 2024) Congratulations!!! Our paper as the corresponding author "A new probabilistic model with mixed-state failure rates: Modeling time-to-event scenarios in reliability and music engineering"  was published in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (Apr. 2024) Congratulations!!! Our paper as the corresponding author "Application of machine learning algorithms in the domain of financial engineering"  was published in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (Apr. 2024) Congratulations!!! Our paper as the corresponding author "The implications of LinkedIn medium and Weibull-based probability model in the financial sector "  was published in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (Mar. 2024) Congratulations!!! Our paper as the corresponding author "Revolutionizing Art Education: Integrating AI and Multimedia for Enhanced Appreciation Teaching "  was published in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (Mar. 2024) Congratulations!!! Our paper as the corresponding author "On the Use of a New Probabilistic Model and Machine Learning Methods with Applications to Reliability and Music Engineering "  was published in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (Mar. ~ Jun. 2024) Prof. Seong offer two courses (Statistics and Information Theory) in the Graduate School of Data Science and one course (C Programming Language) in Department of AI  at CNU.

• (Feb. 2024) Congratulations!!! Our paper as the corresponding author "A new probabilistic model: Theory, simulation and applications to sports and failure times data"  was published in Heliyon (IF: 4.0).

• (Jan. 2024) I hosted two speakers for the DS seminar on 25th and 26th Jan.,
  1) Jusung Kang(Ph.D student in GIST): "강화학습 개론 및 예제 코드 기반의 인공지능 실무 툴 실습"
  2) Haewoong Choi(Ph.D student in GIST): "블록체인과 스마트 컨트랙트 기초 실습" 

• (Dec. 2023) Congratulations!!! Our paper as the corresponding author "A New Cosine-Originated Probability Distribution with Symmetrical and Asymmetrical Behaviors: Repetitive Acceptance Sampling with Reliability Application"  was published in Symmetry (IF: 2.7).

• (Dec. 2023) We have 5 master students and held our first lab seminar on the 27th. All the presenters are expected to do good research. 

• (Dec. 2023) Congratulations!!! Our paper as the corresponding author "On the identifiability and statistical features of a new distributional approach with reliability applications"  was published in AIP Publishing (IF: 1.7).

• (Nov. 2023) I organized a technical seminar with Prof. Manoj from the Banaras Hindu University. Three speakers, including Prof. Manoj, gave presentations on machine learning and artificial intelligence. Our lab looks forward to working closely with Prof. Manoj's group in the future. Find the following link for Prof. Manoj:  https://www.bhu.ac.in/Site/FacultyProfile/1_8?FA000905 

• (Nov. 2023) Congratulations!!! Our paper as the corresponding author "A New Tangent-Generated Probabilistic Approach with Symmetrical and Asymmetrical Natures: Monte Carlo Simulation with Reliability Applications"  was published in Symmetry (IF: 2.7).

• (Oct. 2023) Congratulations!!! Our paper as the corresponding author "Service Delay and Optimization of the Energy Efficiency of a System in Fog-Enabled Smart Cities" has been accepted for publication in Alexandria Engineering Journal (IF: 6.8 with top 8.3% JCR ranking).

• (Oct. 2023) Congratulations!!! Our paper as the corresponding author "A New Sine-Based Distributional Method with Symmetrical and Asymmetrical Natures: Control Chart with Industrial Implication" was published in Symmetry (IF: 2.7).

• (Sep. ~ Dec. 2023) Prof. Seong has two courses (Machine Learning, Data Mining) in the Graduate School of Data Science and one course (Basic Engineering Math) in Department of AI  at CNU.

• ( Aug. 2023) Congratulations!!! Our recently revised and submitted paper as the first author has been finally accepted: "Bounds on Performance for Recovery of Corrupted Labels in Supervised Learning: A Finite Query-Testing Approach" (in Mathematics journal with top 6.8% JCR ranking).

• Projects

• (Co-Investigator, 2023.4. ~ 2029.12.) Regional Demand Orinted Data Science Human Resource Development for Regional Industry Innovation funded by NRF

• (Principal Investigator, 2023.4. ~ 2024.2.) Nonadaptive Algorithm for Recovery of Coded Labels in Binary Classification funded by CNU

• (Principal Investigator, 2020.6. ~ 2024.5.) Development of Deep Learning based Group Testing Systems using Coding Theory funded by NRF

• (Principal Investigator, 2018.8. ~ 2019.7.) Development of a compressed sensing algorithm using self-growth reinforcement learing funded by MNU

• (Principal Investigator, 2017.9. ~ 2020.8.) Development of Framework for Recovery of Sparse Signals based on Deep Learning Techniques funded by NRF

• Research Areas

Anomaly Detection

In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behaviour. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data. 

Detection of Electricity Theft based Learning Technieques

Electricity theft refers to the unauthorized and illegal consumption of electricity without proper billing or payment. This illicit activity poses significant challenges for power utilities, leading to revenue losses and increased operational costs. It involves various techniques, such as tampering with meters, illegal connections, and meter bypassing, which not only undermines the financial stability of utility companies but also disrupts the overall power distribution system. Combating electricity theft requires implementing robust monitoring systems, raising public awareness, and enforcing stringent penalties to deter such practices 

Label Noise  in Supervised Learning
Label noise in supervised learning refers to the presence of incorrect or inaccurate labels in the training data. In this paradigm, the algorithm learns from data where each input is associated with a corresponding target label. However, real-world datasets can be prone to errors, mislabeling, or ambiguity, which introduces label noise. Addressing label noise is crucial as it can severely impact the model's performance and generalization ability, necessitating the use of various data cleaning, regularization, and ensemble techniques to mitigate its adverse effects and improve the reliability of the learned model. 

Group Testing

Group testing is a technique where multiple samples are combined into pools and tested together to identify positive cases efficiently. By testing multiple samples at once, group testing reduces the number of tests required and saves resources. Group testing is used in various fields such as healthcare, public health, and agriculture for screening, surveillance, and control of diseases. Group testing can detect positive cases with high sensitivity and specificity while reducing the burden on the healthcare system. Group testing requires a certain level of prevalence, and additional confirmatory tests may be needed for positive pools. COVID-19 testing of asymptomatic individuals in a community can be done using group testing to identify positive cases efficiently.

Latent Factor Analysis 

Latent factor analysis is a statistical technique used to identify the underlying structure of a set of variables. It assumes that there are underlying factors that influence the observed variables and seeks to identify those factors. It is commonly used in fields such as psychology, education, marketing, and finance. The technique involves several steps, including data collection, data preprocessing, factor extraction, and factor rotation. The results of the analysis can provide insights into the relationships between the observed variables and the underlying factors. It can also be used for predictive modeling and dimensionality reduction.

Compressive Sensing

Compressive sensing is a signal processing technique used to efficiently acquire and process high-dimensional data with fewer measurements than traditional methods. It assumes that the signal to be acquired is sparse, meaning it has few non-zero coefficients in a known or unknown basis. It allows for data to be acquired at a lower rate and then reconstructed with high accuracy, reducing data acquisition and storage costs. The technique involves several steps, including signal acquisition, signal processing, and sparse signal recovery. Compressive sensing has applications in a wide range of fields, including image and video processing, wireless communication, medical imaging, and more. The technique is still an active area of research, with ongoing efforts to improve its efficiency and accuracy.