Scholar
Sangyeop Kim
Google Scholar ID: hxVvuSYAAAAJ
Seoul National University
Citations & Impact
Citations
6
H-index
1
i10-index
0
Publications
12
Co-authors
5
list available
Publications
Resume (English only)
Academic Achievements
- The provided document was cut off before listing specific academic achievements under 'PUBLICATIONS'. However, it can be inferred that Sangyeop Kim has publications in the fields of Information Retrieval, Conversational AI, and AI Safety based on the context.
Research Experience
- - AI Lead & Researcher at Coxwave, Sep 2023 – Present
- - Developed a natural language retrieval model for Align AI, an enterprise conversation analytics solution
- - Implemented advanced conversation analysis features for user intent detection and topic modeling, leveraging LLMs to enhance customer insights
- - Built a multi-agent AI system that automates conversation analytics and generates actionable business reports
- - Conducted collaborative research with KAIST Graduate School of AI on LLM safety and reliability
- - AI Lead & Researcher at RTM, Apr 2020 – Aug 2023
- - Led the development and successful deployment of Apollo at Intel’s Portland semiconductor fabrication plant - an end-to-end manufacturing process anomaly detection and data management solution
- - Built Predictive Health Management (PHM) system for equipment maintenance and downtime reduction
- - Joined as the first technical hire, scaling company from 3 to 55 employees while establishing core AI foundation
Education
- - Ph.D. in Industrial Engineering, Seoul National University, Mar. 2021 – Aug. 2025 (Advisor: Prof. Sungzoon Cho at Data Mining Lab)
- - M.S. in Industrial Engineering, Seoul National University, Mar. 2019 – Feb. 2021 (Advisor: Prof. Sungzoon Cho at Data Mining Lab)
- - B.S. in Industrial Engineering, Seoul National University, Mar. 2013 – Feb. 2019
Background
- - Research Interests: Information Retrieval, Conversational AI, AI Safety
- - Field of Study: Industrial Engineering
- - Summary: Focuses on integrating Large Language Models with Information Retrieval technologies, aiming to develop user-centric semantic search systems and conversational data analysis solutions.
Miscellany
- No additional information such as personal interests or hobbies is provided.
