인공지능 연구실
Artificial Intelligence Laboratory
연구실 소개 및 연구분야
본 연구실은 인간 수준의 지능 및 이를 뛰어넘는 초지능 (Superintelligence)의 구현을 목표로 딥러닝, 머신러닝, 빅데이터 분석, 병렬처리, AI기반 바이오, 지능형 정보시스템 등 관련 기술에 대한 이론 및 다양한 실제 응용에 대한 연구를 진행하고 있다.
최근 관심분야 및 주요 연구과제
* 딥러닝 및 기계학습: supervised/unsupervised 학습, 강화학습 (reinforcement learning), transfer learning 등 이론 및 산업 응용 (industrial AI)
* 빅데이터: 대규모 분석 알고리즘, 분산병렬 시스템 등 scalable big data analytics 기술
* AI 기반 바이오메디컬 기술: 유전체/의료영상/약물/EMR/text 분석, multi-modal/multi-task 융합, privacy-preserving 의료정보 분석기술
* 지능형 시스템: neuromorphic computing, AI 기반 시스템 설계, AI 기반 system monitoring, near-data processing 및 processor-in-memory 기술
* Secure/private AI: 정보보호를 위한 AI 기술, collaborative AI 기술, explainable AI (XAI)
* 빅데이터: 대규모 분석 알고리즘, 분산병렬 시스템 등 scalable big data analytics 기술
* AI 기반 바이오메디컬 기술: 유전체/의료영상/약물/EMR/text 분석, multi-modal/multi-task 융합, privacy-preserving 의료정보 분석기술
* 지능형 시스템: neuromorphic computing, AI 기반 시스템 설계, AI 기반 system monitoring, near-data processing 및 processor-in-memory 기술
* Secure/private AI: 정보보호를 위한 AI 기술, collaborative AI 기술, explainable AI (XAI)
주요 논문/특허
[1] Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, and Sungroh Yoon, “Deep Recurrent Neural Network-Based Identification of Precursor microRNAs,” in Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, USA, December 2017.
[2] Jaekoo Lee, Hyunjae Kim, Jongsun Lee, and Sungroh Yoon, “Transfer Learning for Deep Learning on Graph-Structured Data,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), San Francisco, USA, February 2017.
[3] Seonwoo Min, Byunghan Lee, and Sungroh Yoon, “Deep Learning in Bioinformatics,” Briefings in Bioinformatics, 2016.
[4] Taesup Moon, Seonwoo Min, Byunghan Lee, and Sungroh Yoon, “Neural Universal Discrete Denoiser,” in Proceedings of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
[5] Taehoon Lee and Sungroh Yoon, “Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions,” in Proceedings of International Conference on Machine Learning (ICML), Lille, France, July 2015.
[2] Jaekoo Lee, Hyunjae Kim, Jongsun Lee, and Sungroh Yoon, “Transfer Learning for Deep Learning on Graph-Structured Data,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), San Francisco, USA, February 2017.
[3] Seonwoo Min, Byunghan Lee, and Sungroh Yoon, “Deep Learning in Bioinformatics,” Briefings in Bioinformatics, 2016.
[4] Taesup Moon, Seonwoo Min, Byunghan Lee, and Sungroh Yoon, “Neural Universal Discrete Denoiser,” in Proceedings of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
[5] Taehoon Lee and Sungroh Yoon, “Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions,” in Proceedings of International Conference on Machine Learning (ICML), Lille, France, July 2015.
