양성민

• 관심 분야 Object Detection

• 응용통계학 석사

• E-MAIL iamsheep1209@gmail.com

이용학

• 관심 분야 딥러닝을 활용한 이미지 처리, 이미지 및 텍스트 정보 기반 상품 추천 시스템

• 응용통계학 석사

• E-MAIL feint225@gmail.com

정수진

• 관심 분야 Computer Vision, AI Robotics

• 응용통계학 학사

• E-MAIL sujini7773@gmail.com

이선재

• 관심 분야 Object Detection & Recommender System

• 컴퓨터소프트웨어공학 학사

• E-MAIL dltjswo0323@gmail.com

최광일

• 관심 분야 Multi Modal, Recommender System

• 컴퓨터공학 학사

• E-MAIL cgi2148@gmail.com

문우형

• 관심 분야 Data Analysis

• 문화재보존학, 항공컴퓨터학 학사

• E-MAIL dngud1013@gmail.com

이채환

• 관심 분야 Data Analysis

• 수학 학사

• E-MAIL ch204804@naver.com

이인진

• 관심 분야 Big Data Analysis

• 사회학, 경영학 학사

• E-MAIL in_jjing_@naver.com

김재민

• 관심 분야 Data Science

• 관광경영학, 응용통계학 학사

• E-MAIL kjm6076@gmail.com

권희재

• 관심 분야 딥러닝을 이용한 데이터 분석

• 컴퓨터공학 학사

• E-MAIL hjkwon93@kakao.com

임자연

• 학위 기간 중 논문 실적

  [1] Han, S. W., Park, S., Zhong, H., Ryu, E. S., Wang, P., Jung, S., ... & Kim, S. (2021). Estimation of joint directed acyclic graphs with lasso family for gene networks. Communications in Statistics-Simulation and Computation, 50(9), 2793-2807.

  [2] Lim, J., Bang, S., Kim, J., Park, C., Cho, J., & Kim, S. (2019). Integrative deep learning for identifying differentially expressed (DE) biomarkers. Computational and mathematical methods in medicine, 2019.

  [3] Lim, J., Cho, J., Kim, J., Kim, J., & Kim, S. (2019). Deeper Integrative Neural Network Analysis for Multi-level Omics Data. Quantitative Bio-Science, 38(2), 73-79.

• 현직장 Hutom, Data Scientist

한지성

• 학위 기간 중 논문 실적

  [1] Han, J.*, Jo, K., Lim, W., Lee, Y., Ko, K., Sim, E., ... & Kim, S. (2021). Reinforcement learning guided by double replay memory. Journal of Sensors, 2021, 1-8.

  [2] Han, J.*, Kim, G., Lee, C., Han, Y., Hwang, U., & Kim, S. (2019, February). Predictive models of fire via deep learning exploiting colorific variation. In 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 579-581). IEEE.

  [3] Kim, S., Jung, S., Yang, S., Han, J.*, Lee, B., Lee, J., & Han, S. W. (2019). Vision-based deep Q-learning network models to predict particulate matter concentration levels using temporal digital image data. Journal of Sensors, 2019.

• 현직장 Medipixel, AI Developer

정세희

• 학위 기간 중 논문 실적

  [1] Han, S. W., Park, S., Zhong, H., Ryu, E. S., Wang, P., Jung, S., ... & Kim, S. (2021). Estimation of joint directed acyclic graphs with lasso family for gene networks. Communications in Statistics-Simulation and Computation, 50(9), 2793-2807.

  [2] Jung, S., Yang, S., Lee, E., Lee, Y., Ko, J., Lee, S., ... & Kim, S. (2020). Estimation of Particulate Levels Using Deep Dehazing Network and Temporal Prior. Journal of Sensors, 2020, 1-9.

  [3] Kim, S., Heo, S. M., Yang, S., Kim, Y., Han, J., & Jung, S. (2020). Instance Segmentation Guided by Weight Map with Application to Tooth Boundary Detection. Quantitative Bio-Science, 39(2), 159-167.

  [4] Min, J. H., Jung, S. J., Jung, S. H., Yang, S., Cho, J. S., & Kim, S. H. (2020). Grammatical error correction models for Korean language via pre-trained denoising. Quantitative Bio-Science, 39(1), 17-24.

  [5] Kim, S., Jung, S., Yang, S., Han, J., Lee, B., Lee, J., & Han, S. W. (2019). Vision-based deep Q-learning network models to predict particulate matter concentration levels using temporal digital image data. Journal of Sensors, 2019.

• 현직장 Ph.D. Student, Industrial and Systems Engineering, NC State University

고경민

• 학위 기간 중 논문 실적

  [1] Ko, K., Kim, S., & Kwon, H. (2023). Multi-targeted audio adversarial example for use against speech recognition systems. Computers & Security, 128, 103168.

  [2] Kwon, H., Ko, K., & Kim, S. (2021). Optimized adversarial example with classification score pattern vulnerability removed. IEEE Access, 10, 35804-35813.

  [3] Ko, K., Gwak, H., Lee, E., Kim, G., Moon, D., Cho, Y., & Kim, S. (2021). Action Recognition Model to Monitor Illegal Dumping using Zoom-In Image Sequence Data. Quantitative Bio-Science, 40(2), 95-102.

  [4] Ko, K., Gwak, H., Thoummala, N., Kwon, H., & Kim, S. (2021). SqueezeFace: Integrative face recognition methods with LiDAR sensors. Journal of Sensors, 2021, 1-8.

  [5] Suh, S., Park, Y., Ko, K., Yang, S., Ahn, J., Shin, J. K., & Kim, S. (2021). Weighted mask R-CNN for improving adjacent boundary segmentation. Journal of Sensors, 2021, 1-8.

• 현직장 EY Consulting, Data Scientist

김성근

• 학위 기간 중 논문 실적

  [1] Kim, S., Moon, H., Oh, J., Lee, Y., Kwon, H., & Kim, S. (2022). Automatic Measurements of Garment Sizes Using Computer Vision Deep Learning Models and Point Cloud Data. Applied Sciences, 12(10), 5286.

• 현직장 LG CNS, Data Scientist

김용래

• 학위 기간 중 논문 실적

  [1] Kim, Y., Gwak, H., Oh, J., Kang, M., Kim, J., Kwon, H., & Kim, S. (2023). CloudNet: A LiDAR-based face anti-spoofing model that is robust against light variation. IEEE Access, 11, 16984-16993.

• 현직장 KEPCO Nuclear Fuel, Researcher

양성민

• 학위 기간 중 논문 실적

  [1] Oh, J., Lingnan, P., Lee, Y., Yang, S., & Kim, S. (2022). An Investment Model Based on a Head-And-Shoulder Pattern with Multiple Moving Average Technical Indicators for Future Markets. Quantitative Bio-Science, 41(2), 107-115.

  [2] Suh, S., Park, Y., Ko, K., Yang, S., Ahn, J., Shin, J. K., & Kim, S. (2021). Weighted mask R-CNN for improving adjacent boundary segmentation. Journal of Sensors, 2021, 1-8.

  [3] Kim, S., Heo, S. M., Yang, S., Kim, Y., Han, J., & Jung, S. (2020). Instance Segmentation Guided by Weight Map with Application to Tooth Boundary Detection. Quantitative Bio-Science, 39(2), 159-167.

  [4] Min, J. H., Jung, S. J., Jung, S. H., Yang, S., Cho, J. S., & Kim, S. H. (2020). Grammatical error correction models for Korean language via pre-trained denoising. Quantitative Bio-Science, 39(1), 17-24.

  [5] Kim, S., Jung, S., Yang, S., Han, J., Lee, B., Lee, J., & Han, S. W. (2019). Vision-based deep Q-learning network models to predict particulate matter concentration levels using temporal digital image data. Journal of Sensors, 2019.

• 현직장 Ph.D. Student, Applied Statistics, Konkuk University

이용학

• 학위 기간 중 논문 실적

  [1] Lee, Y., Oh, J., Yang, S., & Kim, S. (2022). Integrated Analytic Methodology Using Visual Image and Meta-Data for Product Recommendation. Quantitative Bio-Science, 41(1), 27-35.

  [2] Kim, S., Moon, H., Oh, J., Lee, Y., Kwon, H., & Kim, S. (2022). Automatic Measurements of Garment Sizes Using Computer Vision Deep Learning Models and Point Cloud Data. Applied Sciences, 12(10), 5286.

  [3] Oh, J., Lingnan, P., Lee, Y., Yang, S., & Kim, S. (2022). An Investment Model Based on a Head-And-Shoulder Pattern with Multiple Moving Average Technical Indicators for Future Markets. Quantitative Bio-Science, 41(2), 107-115.

  [4] Jung, S., Yang, S., Lee, E., Lee, Y., Ko, J., Lee, S., ... & Kim, S. (2020). Estimation of Particulate Levels Using Deep Dehazing Network and Temporal Prior. Journal of Sensors, 2020, 1-9.

• 현직장 Ph.D. Student, Applied Statistics, Konkuk University

오재훈

• 학위 기간 중 논문 실적

  [1] Kim Y., Kwak H., Oh J., Kang M., Kim J., Kwon H. and Kim S. (2023). CloudNet: A LiDAR-based face anti-spoofing model that is robust against light variation. IEEE Access, 11, 16984-16993.

  [2] Lee, Y., Oh, J., Yang, S., & Kim, S. (2022). Integrated Analytic Methodology Using Visual Image and Meta-Data for Product Recommendation. Quantitative Bio-Science, 41(1), 27-35.

  [3] Kim, S., Moon, H., Oh, J., Lee, Y., Kwon, H., & Kim, S. (2022). Automatic Measurements of Garment Sizes Using Computer Vision Deep Learning Models and Point Cloud Data. Applied Sciences, 12(10), 5286.

  [4] Oh, J., Lingnan, P., Lee, Y., Yang, S., & Kim, S. (2022). An Investment Model Based on a Head-And-Shoulder Pattern with Multiple Moving Average Technical Indicators for Future Markets. Quantitative Bio-Science, 41(2), 107-115.