In Jae Seo

Jae won Chung

Ju Hyung Park

Hee Jeong Park

Won Gyung Nam

Byung Soo Kim

Ji Hyung Yoon

Soon Chan Kwon

Jae Heon Kim

Dong Hee Lee

Hui Do Jung

Sue Jeong Kang

Sang Hak Park

Se Yeon Park

Sung A Hwang

Chul Young Cho

Da Eun Ji

Jong Hyeon Choi

Since March 2021, ANDlab has been dedicated to uncovering meaningful insights from diverse real-world data. Our mission is to advance the frontiers of artificial intelligence by developing innovative methodologies in big data analytics, natural language processing (NLP), and information extraction. Our research spans a wide spectrum of domains, combining cutting-edge AI techniques with interdisciplinary applications. By leveraging state-of-the-art models and scalable data-driven approaches, we aim to address complex challenges in fields such as healthcare, economics, sociology, and industry. Our team consists of researchers with strong expertise in modern AI paradigms, including large language models (LLMs), knowledge-augmented systems, and privacy-preserving learning. We actively explore emerging directions such as agentic AI, multimodal intelligence, and physical AI, while also contributing to foundational areas like information retrieval, knowledge graphs, and retrieval-augmented generation (RAG). At ANDlab, we believe in bridging theory and real-world impact. Our research focuses not only on advancing algorithms but also on enabling AI transformation (AIx) across industries, from healthcare and finance to technology and large-scale systems. We are always looking for motivated and curious researchers who are passionate about NLP, data analytics, and the future of AI. If you are eager to tackle challenging problems and create meaningful impact through data and intelligence, we warmly welcome you to join ANDlab.

ANDlab은 신재우 교수님의 지도 아래 연세대학교에서 연구를 수행하는 그룹입니다. 저희 연구는 실제 문제 해결을 위한 혁신적인 머신러닝 모델 및 시스템 개발에 중점을 둡니다. 특히 대규모 머신러닝, 딥러닝 최적화, 그래프 신경망, 프라이버시 보존 AI 등의 분야에 관심을 가지고 있습니다. 저희 목표는 AI 연구의 한계를 뛰어넘고 지능형 시스템 발전에 기여하는 것입니다.

Q-Learning Algorithms: A Comprehensive Classification and Applications

Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

Word2vec convolutional neural networks for classification of news articles and tweets

Indoor Positioning Technologies Without Offline Fingerprinting Map: A Survey

Health Big Data Analytics: A Technology Survey

Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey

IEEE 802.11 Saturation Throughput Analysis in the Presence of Hidden Terminals

An asynchronous scheduled MAC protocol for wireless sensor networks

A Survey Of differential privacy-based techniques and their applicability to location-Based services

Application of Local Differential Privacy to Collection of Indoor Positioning Data

A deep attention model to forecast the Length Of Stay and the in-hospital mortality right on admission from ICD codes and demographic data

Survey of Landmark-based Indoor Positioning Technologies

Deep Learning for Electronic Health Records Analytics

Collecting Health Lifelog Data From Smartwatch Users in a Privacy-Preserving Manner

AS-MAC: An asynchronous scheduled MAC protocol for wireless sensor networks

Privacy-preserving aggregation of personal health data streams

Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection

Privacy Provision in Collaborative Ehealth With Attribute-Based Encryption: Survey, Challenges and Future Directions

Deep learning-based privacy-preserving framework for synthetic trajectory generation

Collaborative Ehealth Privacy and Security: An Access Control With Attribute Revocation Based on OBDD Access Structure

PROGRAM: Programmatic Retrieval Optimization with Generative Reasoning and Augmented Multi-queries

CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning

“As Eastern Powers, I will veto.” : An Investigation of Nation-level Bias of Large Language Models in International Relations

Privacy-Preserving Trajectory Data Publication: A Distributed Approach without Trusted Servers

UniRAG: A Unified RAG Framework for Knowledge-Intensive Queries with Decomposition, Break-Down Reasoning, and Iterative Rewriting

A pivot-enhanced question answering framework: using iterative sub-question decomposition and answer-to-question verification

Enhancing Medical Cross-Modal Hashing Retrieval using Dropout-Voting Mixture-of-Experts Fusion

Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review

Visual Question Answering: A Survey of Methods, Datasets, Evaluation

Forecasting Epidemic Spread with Recurrent Graph Gate Fusion Transformers

Privacy-Preserving Generation and Publication of Synthetic Trajectory Microdata: A Comprehensive Survey

A survey on multimodal bidirectional machine learning translation of image and natural language processing

Long-term influenza outbreak forecast using time-precedence correlation of web data

Survey of landmark-based indoor positioning technologies

Scalable Graph Neural Networks with Adaptive Neighbor Sampling

Federated Learning for Resource-Constrained Devices

Privacy-Preserving Deep Learning via Differential Privacy and Homomorphic Encryption

Beakcheol Jang

Full Professor, Graduate School of Information, Adjunct Professor, Department of Artificial Intelligence

Artificial Intelligence Natural Language Processing & Bigdata Analytics Laboratory

교수님 소개

장백철

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

PROGRAM: Programmatic Retrieval Optimization with Generative Reasoning and Augmented Multi-queries

CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning

“As Eastern Powers, I will veto.” : An Investigation of Nation-level Bias of Large Language Models in International Relations

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

장백철

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

PROGRAM: Programmatic Retrieval Optimization with Generative Reasoning and Augmented Multi-queries

CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning

“As Eastern Powers, I will veto.” : An Investigation of Nation-level Bias of Large Language Models in International Relations

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집