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Welcome to the Machine Intelligence and Data Science (MIDaS) Laboratory @ Yonsei University
Introduction
The MIDaS lab has been established in the School of Mathematics and Computing (Computational Science and Engineering) at Yonsei since March 2019 and is led by Professor Won-Yong Shin. Our lab is currently funded by the BK21 Four program, the Basic Research Laboratory (BRL) program, and the Mid-career Researcher program (Type 2). Our research focuses basically on data mining and machine learning in broad fields of social media, healthcare, bioinformatics, and wireless networking.
Recruiting!
Ph.D., M.S., and Undergraduate Research positions available (scholarship and competitive subsidies offered). If any of you are interested in our lab, please feel free to contact Prof. Shin at wy.shin_at_yonsei.ac.kr.
For international applicants: Please customize your research statement along with the research directions above in the MIDaS Lab. Dr. Shin will not respond to email broadcasting.
For undergraduate applicants: Please refer to https://cseurpyonsei.weebly.com/admission.html.
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대학원 석/박사과정 및 학부 연구 학생 모집 (장학금 및 연구비 지급)합니다. 우리 연구실에서 수행하는 연구에 관심이 있는 학생들은 e-mail 을 보내주시기 바랍니다. 혹시 학부 인턴을 하지 않았는데 대학원 진학을 고려하는 학생이라면 아래 영상을 필히 시청해보기 바랍니다.
Latest News
The paper, entitled "Turbo-CF: Matrix decomposition-free graph filtering for fast recommendation", has been accepted for presentation at the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024) (short).
The paper, entitled "Collaborative filtering based on diffusion models: Unveiling the potential of high-order connectivity", has been accepted for presentation at the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024).
The paper, entitled "PAGE: Prototype-based model-level explanations for graph neural networks", has been accepted for publication in the IEEE Transactions on Pattern Analysis and Machine Intelligence
The paper, entitled "Energy-efficient edge learning via joint data deepening-and-prefetching", has been accepted for publication in the IEEE Transactions on Wireless Communications.
The paper, entitled "IM-META: Influence maximization using node metadata in networks with unknown topology", has been accepted for publication in the IEEE Transactions on Network Science and Engineering.
The paper, entitled "Machine learning investigation of high-k metal gate processes for dynamic random access memory peripheral transistor", has been accepted for publication in the APL Materials.
The paper, entitled "The experiences of layoff survivors: Navigating organizational justice in times of crisis", has been accepted for publication in the Sustainability.
The paper, entitled "Propagate & Distill: Towards effective graph learners using propagation-embracing MLPs", has been accepted for presentation at the LoG 2023.
Jin-Duk has won the Best Paper Award of the 2023 KICS Summer Conference for the paper, entitled "Oversmoothing alleviation in graph neural network-based criteria recommender systems".
The paper, entitled "MONET: Modality-embracing graph convolutional network and target-aware attention for multimedia recommendation", has been accepted for presentation in the ACM WSDM 2024.
The paper, entitled "On the power of gradual network alignment using dual-perception similarities", has been accepted for publication in the IEEE Transactions on Pattern Analysis and Machine Intelligence.
The paper, entitled "Edgeless-GNN: Unsupervised representation learning for edgeless nodes", has been accepted for publication in the IEEE Transactions on Emerging Topics in Computing.
