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- π§ AI Researcher & MLOps Engineer at Lingora AI Innovation Team
- π Industrial Management Engineering graduate from Myongji University (Summa Cum Laude)
- π¬ Former Undergraduate Researcher at Computational Data Science Lab, Myongji University
- π Founder and President of Data Science Club 'FoM'
- πΌ Ex-AI Researcher and Prompt Engineer at Market Designers
- π Born on January 19, 1999
- π§ Email: heonyus@gmail.com
- πͺ Military Service: 2020.04 - 2021.10 (Auxiliary Policeman, Seoul Metropolitan Police Agency, 3rd Mobile Unit)
- 2024.08: MJU, Graduated Summa Cum Laude in Industrial Management Engineering
- 2022.03: MJU, Transferred to Industrial Management Engineering
- 2019.02: DIMA, Withdrew from Acting Major
- 2018.03: DIMA, Entered Acting Major
- 2017.02: Gwan-ak High School, Graduated
- 2014.03: Gwan-ak High School, Entered
| π₯ Summa Cum Laude in Industrial Management Engineering (2024.08) | π National Assembly Public Data Competition - Commendation Award (2024.07) |
| ποΈ Myongji University On-site Training Review Competition Grand Prize (2024.01) | π Myongji University Capstone Design Graduation Project Grand Prize (2023.06) |
| π Myongji University Data Analytics Competition Excellence Award (2022.12) | π‘ Myongji University Learning Community Scholarship (2022.06) |
π§ Personal Knowledge Model for Learner Persona Understanding
- π Technologies: GPT-3.5, Personal Knowledge Management, Python, MLOps
- π Key Achievements:
- Implemented knowledge extraction module with GPT-3.5 API
- Optimized personal knowledge management using multi-domain approach
- Enhanced AI tutoring personalization through effective knowledge integration
π¬ Open-end Chat Modifier Model
- π Technologies: GPT-3.5, AWS SageMaker, Python, Docker
- π Key Achievements:
- Achieved efficient context handling in long AI conversations
- Deployed model with 1.7 seconds response time using AWS SageMaker
- Implemented CI/CD pipeline for continuous model updates
π National Assembly Bill Proposal System
- π Technologies: NLP, Python, API Development
- π Key Achievements:
- Automated bill proposal process using NLP techniques
- Enhanced legislative efficiency by integrating existing laws
- Successfully deployed and tested within the National Assembly
π Entity Recognition Search Model & LLMOps System
- π Technologies: SBERT, GPT-4 API, LLMOps, Docker, AWS
- π Key Achievements:
- Developed robust entity recognition model with SBERT
- Improved word recommendation accuracy by over 75%
- Integrated LLMOps for automated deployment and scaling
"Exploring human essence and creating Γbermensch through AI."