Join Our Team!

AI-Powered Software Engineering

Our lab specializes in cutting-edge research and development of Retrieval-Augmented Generation Small Large Language Models (RAG-sLLM) and Retrieval-Augmented Generation Large Multimodal Models (RAG-LMMs), establishing our expertise in these transformative fields. We aim to push the boundaries of RAG-sLLM and RAG-LMMs in diverse applications, enhancing their effectiveness and adaptability across software engineering tasks. In addition, we explore complementary areas including:

  • (Technical) Large Language Models (LLMs): We focus on advancing large language models, driving innovations in natural language processing for automation and improved model performance.
  • (Technical) Small Language Models (SLM): We investigate efficient, resource-conscious models tailored to specific tasks, optimizing performance without compromising quality.
  • (Technical) Large Multimodal Models (LMMs): We combine multiple data modalities to enhance our models' understanding and generation across text, images, and contextual information.
  • (Technical) Data Mining: We leverage LLMs and LMMs to enhance data management and analysis, extracting valuable insights from vast datasets.
  • (Technical) DevSecOps/KubeFlow: We integrate LLMs and LMMs into DevSecOps and KubeFlow pipelines, automating and optimizing deployment, security, and operational workflows.
  • (Technical) Blockchain: We explore the potential of LLMs and LMMs to enhance blockchain interoperability and automate smart contract generation and analysis.
  • (Technical) Game AI: We develop advanced multi-agent strategies for games like StarCraft 2, using reinforcement learning, LLMs, and LMMs to create dynamic in-game content.
  • (Technical) Financial Techniques: We apply LLMs and LMMs for financial fraud detection in telecommunications, analyzing patterns within large transactional datasets.
  • (Technical) Industrial Techniques: We build reinforcement learning-based AI systems for industrial applications, automating control algorithms and maintenance schedules.
  • (Academic) Software Evolution: We create intelligent systems powered by LLMs and LMMs to automate bug repair and code refactoring, ensuring code quality and maintainability.

Graduate Student Support

  • State-of-the-Art Resources: Provided access to a powerful Kubernetes Cluster optimized for scalable and efficient model training and deployment, featuring high-performance specifications: an Intel i9 CPU with 192 cores and 256 threads, 1TB of RAM, and an NVIDIA RTX 4090 GPU with 192GB of memory.
  • Personalized Research Space: Each student receives an optimized personal research space, including a high-performance workstation and triple 27-inch LG monitors, supporting a productive and immersive work environment.
  • Financial Support: Comprehensive funding for tuition, living expenses, and annual productivity incentives is provided.
  • Conference and Travel Opportunities: Fully funded participation in leading domestic and international conferences and technical seminars is offered.
  • Knowledge and Career Development: Unrestricted access to IT literature, textbooks, coding tests, portfolio-building resources, and career guidance is provided, focusing on securing positions at top-tier tech companies.
  • Industry Interaction: Opportunities to regularly engage with industry professionals for knowledge sharing and networking are available.

Get in Touch

Lab. website Adviser website

Who Should Apply

  • We are looking for passionate, driven individuals who are eager to collaborate in a cutting-edge research environment and aspire to become leaders in the field of software engineering and development.

Application Details

  • Interested applicants should contact Prof. Geunseok Yang at gsyang@hknu.ac.kr (located in Engineering Building 1, Room 311).

No. of Recruitments

  • We are currently accepting applications for 5 positions: 3 Postdoctoral Fellow and 2 Graduate students. (November 2024)