Data Mining and Information Systems Lab
(고려대학교 컴퓨터학과 강재우교수 연구실)
Data science has advanced to the point where it is changing our world. It is now the center of exploring and uncovering knowledge in different domains and acts as a bridge to connect them. With ever growing amount of data and opportunity to explore, DMIS lab aims to drive the data science revolution.
DMIS (Data Mining and Information Systems) Lab seeks to develop explainable AI in the following areas: Drug Discovery, Bioinformatics Analysis, Biomedical Image Processing, Recommender Systems, Question and Answering, Search, Financial Data Analysis, and much more. We focus on finding models, algorithms, and systems for any kinds of data analysis with applications on prediction, knowledge discovery, representation learning and anomaly detection. DMIS Lab is also participating various data science competitions such as DREAM Challenges to solve difficult real-world problems and facilitate knowledge sharing with other research teams around the world.
- Aug. 2019: Donghyeon Park's paper, KitcheNette: Predicting and Ranking Food Ingredient Pairings based on Siamese Neural Network, got accepted to IJCAI 2019, one of the top-tier conferences for general AI.
- July. 2019: Congratulations! Our DMIS team (Sungjoon Park, Minji Jeon, Sunkyu Kim, Junhyun Lee, Seongjun Yun, Bumsoo Kim, Buru Chang) has been selected as the top performers in the IDG-DREAM Drug-Kinase Binding Prediction Challenge. As one of the best performers, we will present our model at the RSG with DREAM Conference, NY in November. (Link)
- AI기반 버추얼약물스크리닝모델로 일리노이대-칭화대 컨소시움, 노스캐롤라이나대 팀과 함께 약물활성예측 드림챌린지 공동 최우수팀 선정! 연구팀은 11월 뉴욕에서 개최될 RSG with Dream Conference에서 해당 모델을 발표할 예정이다.
- May. 2019: Real-Time Open-Domain Question Answering on Wikipedia with Dense-Sparse Phrase Index, co-first authored by Jinhyuk Lee, is accepted to ACL 2019, the top conference in computational linguistics and natural language processing.
- May. 2019: ReSimNet: Drug Response Similarity Prediction using Siamese Neural Networks, co-first authored by Minji Jeon and Donghyeon Park, has been accepted to Bioinformatics, the best journal for computational biology.
- ReSimNet measures the transcriptional response similarity of the two chemical compounds, and the team achieved first place in the Multi-targeting Drug DREAM Challenge with this model (outperforming Janssen Pharmaceutica).
- Apr. 2019: Self-Attention Graph Pooling, co-first authored by Junhyun Lee and Inyeop Lee, has been accepted to ICML 2019, the top conference in machine learning.
- Apr. 2019: SAIN: Self-Attentive Integration Network for Recommendation, co-first authored by Seoungjun Yun and Raehyun Kim, got accepted by SIGIR 2019, the best conference in Information Retrieval.
- Apr. 2019: Congratulations to Dr. Minji Jeon for her first Nature series publication! (accepted to Nature Communications)
- Previously, Dr. Minji Jeon's team won 2nd place in the AstraZeneca Sanger Drug Synergy Prediction DREAM challenge (outperforming Stanford(6th), MIT(11th)).
- Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen is an overview paper for the DREAM challenge and is coauthored by top performing teams and organizers from AstraZeneca-Sanger. (bioarxiv)
- Jan. 2019: BioBERT: a pre-trained biomedical language representation model for biomedical text mining, co-first authored by Jinhyuk Lee and Wonjin Yoon, has been submitted in arxiv. BioBERT is the first biomedical domain representation model to be pre-trained on large-scale biomedical corpus. Code and dataset is available (github).
- Dec. 2018: Our DMIS team (Minji Jeon, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, Miyoung Ko, Sunkyu Kim, Yonghwa Choi) won 1st place in the Multi-targeting Drug DREAM Challenge. The team outperformed multinational pharmaceutical firms such as Janssen Pharmaceutica. (Link1, Link2)
- DMIS 연구팀, 다국적 제약사를 (얀센, 바이엘 등) 제치고 대회에서 우승! 연구팀은 신약 후보 물질을 발굴하는 모델을 개발했고 AI로 선택한 물질의 가능성이 입증되어 대회 우승팀으로 선정되었다.
- Dec. 2018: Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network, co-first authored by Raehyun Kim and Hyunjae Kim, has been accepted as full paper by SDM19, one of the top-tier conferences in data-mining.
- Nov. 2018: Buru Chang received the NAVER Ph.D Fellowship Award as he showed stellar performance with his papers.
- Aug. 2018: Jinhyuk Lee's paper, Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering, was accepted to EMNLP2018, one of the most renowned conferences in NLP field.
- Aug. 2018: Learning User Preferences and Understanding Calendar Contexts for Event Scheduling (co-first authored by Donghyeon Kim and Jinhyuk Lee) got accepted by CIKM2018, which is one of the top-tier international conferences in Database/Data Mining/Information Retrieval field with 10% acceptance rate.
- Jul. 2018: Buru Chang's paper, Content-Aware Point-of-Interest Embedding Model for Successive POI Recommendation, was accepted to IJCAI 2018, one of the top-tier conferences for general AI.
- Nov. 2017: Our DMIS team (Sunkyu Kim, Heewon Lee, Keonwoo Kim, Hwisang Jeon, Minji Jeon, Yonghwa Choi, Daehan Kim) was awarded as the BEST performers of the NCI-CPTAC DREAM Proteogenomics Challenge, sponsored by the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC). This was the very first time that Korea team won the Challenge. (Link)
- UCLA: 3rd place
- Stanford University: 13th place
- Nov. 2017: 고려대학교 강재우 교수 연구팀 - 암 환자의 단백질 활성도를 예측하는 NCI-CPTAC DREAM Proteogenomics Challenge에 참가하여 대회 역사상 한국팀 최초 우승! 해당 Challenge는 미국 국립 암 연구원의 유전단백체 연구센터(NCI-CPTAC)가 주최하였다. (뉴스 링크)
- Aug. 2017: Jinhyuk Lee's paper, Name Nationality Classification with Recurrent Neural Network, got accepted for IJCAI 2017, one of the top-tier conferences for general AI.
- Apr. 2017: Constructing and Evaluating a Novel Crowdsourcing-based Paraphrased Opinion Spam Dataset, co-first authored by Seongsoon Kim and Seongwoon Lee, has been accepted to WWW 2017, one of the top conferences for web.
- Oct. 2016: Among 42 teams from different parts of the world, our DMIS team ranked 2nd place at the Disease Module Identification DREAM Challenge: Discover disease pathways in genomic networks. The goal is to systematically assess module identification methods on a panel of state-of-the-art genomic networks and to discover novel network pathways.
- Oct. 2016: 생물학적 네트워크에서 질병에 연관된 모듈을 발굴하는 Disease Module Identification DREAM Challenge: Discover disease pathways in genomic networks에 참여하여 전체 42팀 중 종합성적 공동2위 달성!
- Mar. 2016: Our DMIS team won 2nd place at the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is designed to predict synergistic drug combinations and to identify associated biomarkers. As the challenge was hosted by AstraZeneca, one of the top 10 pharmaceutical companies in the world, the DMIS team showed stellar performance in this grand competition, ranking 2nd place. (Link)
- Stanford University: 6th place
- MIT: 11th place
- Mar. 2016: 항암제 병합 치료 효능을 예측하는 The AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge에 참여하여 전 세계 62팀 중 2위 입상! 해당 Challenge는 세계 10대 제약회사 "AstraZeneca"가 주최하였으며 강재우 교수 연구팀은 Stanford University(6위), MIT(11위)를 압도적으로 제치고 2위를 기록했다. (뉴스 링크)