◾ KAIST 산업 및 시스템공학과 박사, 2006.03 - 2012.04
◾ KAIST 산업공학과 학사, 2001.03 - 2005.07
◾ Lunit Inc. Research Lead, 2017.03 - 2018.02
◾ Lunit Inc. Senior Researcher, 2015.01 - 2017.02
◾ 삼성전자 종합기술원 전문연구원, 2012.05 - 2014.12
◾ Deep learning and its applications
◾ Artificial intelligence
◾ Medical image analysis
◾ Machine learning
◾ (학부) 산업정보시스템전공: 파이썬프로그래밍, 딥러닝
◾ (학부) ITM전공: Artificial Intelligence
◾ (대학원) 데이터사이언스: 데이터분석을 위한 수학, 인공신경망과 딥러닝
◾ M. Veta, [et al. including S. Hwang] (2019), "Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge," Medical Image Analysis, 290(1), 218-228.
◾ J. G. Nam, S. Park, [et al. including S. Hwang] (2019), "Development and validation of deep Learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs," Radiology, 290(1), 218-228.
◾ S. Hwang, and D. Kim (2018), "A scalable feature based clustering algorithm for sequences with many distinct items," International Journal of Fuzzy Logic and Intelligent Systems, 18(4), 316-325.
◾ S. Hwang, and M. K. Jeong (2018), "Robust relevance vector machine for classication with variational inference," Annals of Operations Research, 263(1-2), 21-43.
◾ S. Hwang, J. Yoo, C. Lee, and S. H. Lee (2016), "Collaborative crystal structure prediction," Expert Systems with Applications, 63, 222-230.
◾ Y.-S. Jeong, S. Hwang, and Y.-D. Ko (2015), "Quantitative analysis for plasma etch modeling using optical emission spectroscopy: prediction of plasma etch responses," Industrial Engineering and Management Systems, 14(4), 392-400.
◾ D. Kim, C. Lee, S. Hwang, and M. K. Jeong (2015), "A robust support vector regression with a linear-log concave loss function," Journal of Operational Research Society, 67(5), 735-742.
◾ S. Hwang, D. Kim, M. K. Jeong, and B.-J. Yum (2015), "Robust kernel based regression with bounded infuence for outliers," Journal of Operational Research Society, 66(8), 1385-1398.
◾ D. Mishra, [et al. including S. Hwang] (2015), "Effect of piezoelectricity on critical thickness for misfit dislocation formation at InGaN/GaN interface," Computational Materials Science, 97, 254-262.
◾ S. Hwang, M. K. Jeong, and B.-J. Yum (2014), "Robust relevance vector machine with variational inference for improving virtual metrology accuracy," IEEE Transactions on Semiconductor Manufacturing, 27(1), 83-94.
◾ Y.-H. Cho, [et al. including S. Hwang] (2013), "Quantum efficiency affected by localized carrier distribution near the V-defect in GaN based quantum well," Applied Physics Letters, 103, 261101.
◾ S.-H. Park, [et al. including S. Hwang] (2013), "Partial strain relaxation effects on polarization anisotropy of semipolar (1122) InGaN/GaN quantum well structures," Applied Physics Letters, 103, 221108.
◾ Additive Ensemble Neural Networks, IEEE ACCESS, vol.8 pp.113192~113199, 2020황상흠
◾ A New Splitting Criterion for Better Interpretable Trees, IEEE ACCESS, vol.8 pp.62762~62774, 2020황상흠
◾ 한국어 기술문서 분석을 위한 BERT 기반의 분류모델, 한국전자거래학회지, vol.25 No.1 pp.203~214, 2020황상흠
◾ S. Hwang, and S. Park, "Accurate lung segmentation via network-wise training of convolutional networks," The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.
◾ K. Paeng, S. Hwang, S. Park, and M. Kim, "A unied framework for tumor proliferation score prediction in breast histopathology," The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.
◾ S. Hwang, and H.-E. Kim, "Self-transfer learning for weakly supervised lesion localization," The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 239-246, Oct. 2016.
◾ S. Hwang, H.-E. Kim, J. Jeong, and H.-J. Kim, "A novel approach for tuberculosis screening based on deep convolutional neural networks," in Proceedings of SPIE Medical Imaging, 9785, Mar. 2016.
◾ S. Kim, [et al. including S. Hwang], \Deep convolutional neural network-based mitosis detection in invasive carcinoma of breast by smartphone-based histologic image acquisition," in Modern Pathology (USCAP Annual Meeting), 29, Mar. 2016.
◾ 이건규, 황상흠, 영역 분할 모델의 성능 향상을 위한 대조적 손실함수의 활용, 대한산업공학회 추계학술대회, 온라인, 2020황상흠
◾ Jiin Koo, Seungmoo Yang, Sangheum Hwang, A Comparison of the Performance of Deep Learning Models for Electric Load Forecasting, International Conference on Electric-Vehicle, Smart Grid and Information Technology, Online, 2020황상흠
◾ Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang, Confidence-Aware Learning for Deep Neural Networks, Proceedings of the International Conference on Machine Learning, Online, 2020황상흠
◾ 황재문, 황상흠, 해부학적 구조를 반영한 흉부 X-ray 영상에서의 폐 영역 분할 모델, 대한산업공학회 추계학술대회 논문집, 서울대학교, 2019황상흠
◾ 문주영, 김지효, 황상흠, 심층 신경망의 과한 확신을 방지하는 새로운 정규화 방법, 대한산업공학회 추계학술대회 논문집, 서울대학교, 2019황상흠
◾ 김수민, 황상흠, 윤동희, 김도현, Unsupervised Feature Selection for Autoencoder, 대한산업공학회 춘계공동학술대회 논문집, 광주 김대중컨벤션센터, 2019황상흠
◾ Brain CT에 대한 뇌출혈검출 알고리즘 개발, 산학협력단, 2019.03.~2020.02.황상흠
◾ 확률 보정 기법 기반의 능동적 학습 방법의 개발, (주)엘지씨엔에스, 2019.03.~2019.12.황상흠
◾ 도메인 일반화를 위한 제약 최적화 기반의 딥러닝 알고리즘 개발, 한국연구재단, 2018.11.~2020.10.황상흠
◾ LGCNS Deep Learning 기반 비전검사 알고리즘 고도화 자문, (주)엘지씨엔에스, 2018.06.~2018.10.황상흠
◾ 인공지능 기술을 적용한 영상정보 식별에 관한 연구, 합동참모본부, 2018.06.~2018.11.황상흠
◾ 깊은 신경망 모형의 불균형 데이터 학습 양상에 대한 고찰, 산학협력단, 2018.04.~2019.03.황상흠
◾ Inner Product based Deep Neural Networks, 2018 INFORMS International Conference Poster Competition, The Institute for Operations Research and the Management Sciences (INFORMS), 2018황상흠