Youngha Jo

I am an end-to-end ML engineer at LG CNS GenAI Business Team — I self-learn whatever a problem needs and carry models from design and training through evaluation, serving, and operation. At LG CNS I've worked across LLM services from PoC planning to production, including a real-time LLM-as-a-judge evaluation batch service deployed in a financial client's production network. Previously, I graduated with a Master's degree from Brain and Machine Intelligence Lab (BML) at KAIST, advised by Sang Wan Lee, where I ran event-cognition research end to end — from hypothesis to a Human-AI experiment platform and deep-learning models. Before KAIST, I completed a recommender system track at Naver Boostcamp AI Tech (3rd cohort). I received my B.S. in Electrical and Electronic Engineering (minor in Psychology) from Yonsei University, where I also worked as an undergraduate research assistant in Computational Clinical Science Lab (SNU, advised by Woo-Young Ahn) and led the cognitive science study club CogSci::IN.

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Skills & Tools

• Programming Languages: Python (Advanced), C++ (Intermediate), JavaScript, C#
• Machine Learning: PyTorch (Advanced), Tensorflow
• LLM & Agent Development: LangChain/LangGraph, Hugging Face, MCP
• Backend & Database: FastAPI, Node.js, PostgreSQL, Neo4j, MongoDB, Firebase
• Tools & Platforms: Streamlit, Git, Unity, Docker

English Proficiency

• OPIc: Intermediate High (2025.01.31)
• TEPS: 2+ (2021.10.02)

LG CNS GenAI Business Team | AI Engineer (2025.07 ~ present)

PoC development to service planning, working on AI systems from a production perspective.

Shinhan Card — AI Agent Evaluation Automation (AutoEval, real-time LLM-as-a-judge batch service): sole design & implementation, jointly planned with Shinhan Card's product planner.

• Built a production batch architecture that collects and evaluates AI agent service logs from the operational DB on a 10-minute window — fault-tolerant via a DB state machine, with resumable/retryable processing on worker failure (FastAPI + SQLAlchemy)
• Designed the LLM-as-a-judge evaluation scheme (turn- and session-level metrics), with intent/tool catalog–based structured evaluation and a delivery-payload assembly API for external integration
• Optimized token and call usage for a closed-network shared-vLLM environment via a single structured judge prompt instead of per-metric calls
• Live in Shinhan Card's production network (~100 QA per 10 min); validated judge reliability with a self-curated golden QA set and Grafana monitoring

Other responsibilities:

• Designed AI OCR/VLM model evaluation framework for insurance documents — built cell-level comparison with normalization logic for systematic comparison across document types and model versions
• Planned and developed RFP-based proposal auto-generation PoC — designed and implemented agent pipeline from requirement analysis to PPT slide generation using LangGraph
• Planned and proposed AI Agent services for multiple financial institutions — analyzed RFP technical requirements and designed service architecture through collaboration with cross-functional engineers

I'm interested in building AI systems that model and analyze complex sequential processes, with a focus on how humans structure predictions and plans into meaningful units. My research combines cognitive science theory with deep learning model design, and I value building complete experimental systems — from platform development to human experiments to computational model validation.

Event Unit Prediction Research (23.07~24.12)

Human Experiment Platform: Built a Unity WebGL-based interactive experiment platform to collect human prediction data. Integrated Google Firebase for real-time data storage and analysis. Designed tasks where participants predict events in a physics-based environment (Angry Birds). Check online experiment link to conduct it by yourself!

Hierarchical Video Prediction Model: A model that predicts future frames in a video by learning hierarchical structures. Designed a temporal abastraction model that incorporates causal inference & event-based representation. Demonstrated AI’s ability to simulate human-like event anticipation.

Stable Predictive Coding Network Research (24.01~)

SPCN: Investigated stability issues in Predictive Coding Networks (PCN) and proposed a novel Stable Predictive Coding Network (SPCN). Conducted experiments to analyze instability factors in PCN, identifying key failure points. Designed a new architecture that stabilizes learning in PCN (SPCN), and showed improved performance in various tasks. Contributed to the project by implementing models, running experiments, and modularizing the codebase for team-wide use.

Jo, Y., Lee, S. W. (2025). Designing a Platform and Algorithm for Event-Cognitive Unit Prediction Experiments. Patent pending.

Ha, M., Kim, H., Sung, Y., Jo, Y., Kang, M., Lee, S. (2026). Stable and Scalable Deep Predictive Coding Networks with Meta Prediction Errors. International Conference on Learning Representation (ICLR). (accepted)

Multimodal Manual RAG — Diagram-Grounded Manual QA (26.04~present)

A multimodal RAG over home-appliance manuals, targeting questions whose answers live only in diagrams, not in body text. The goal is to make diagram information retrievable as embedded data, so that questions text-only RAG cannot answer become solvable. (Follow-up to the Modulabs LLM study, conducted as an individual project.)

• Quantified the core hypothesis: curated an image-required golden set (part location, insertion direction, icon shape, etc.) and measured the text-only baseline's failure (model match 4/8) — evidence that diagram information must be embedded as data to solve these questions
• Agentic RAG + honest evaluation: built a 4-node graph (retrieve→grade→generate) with hybrid retrieval and a Korean reranker (Top-1 95.7%), evaluated with RAGAS and Refusal/Citation accuracy — decomposed by refusal-vs-answered and measurement-infrastructure diagnosis rather than reporting raw averages
• Phase 2 (in progress): confirmed instructional diagrams are entirely vector graphics, and is building cross-modal retrieval via page rasterize → vision embedding to answer diagram-dependent questions

Agentic AI Knowledge Graph Explorer (25.12~present)

A knowledge exploration system that tracks the co-evolution of AI research (papers) and services (frameworks) in the Agentic AI domain, built with LangGraph + Neo4j + ChromaDB.

Main UI

Graph Visualization Settings

Query Processing

Results with Graph

• Designed a knowledge graph schema with 11 core Principles, 31 Methods, 16 Implementations (67 nodes, 79 relationships)
• Built a 4-node LangGraph pipeline: Intent Classifier → Search Planner → Graph Retriever → Synthesizer
• Implemented Neo4j client (520 lines) with domain-specific Cypher queries
• Developed with Claude Code for rapid prototyping

GitHub: https://github.com/hawe66/agentic-kg-explorer

Modulabs LLM Study — From Theory to Agent Development (25.05~26.02)

Participated in a year-long LLM study across three sessions at Modulabs:

Session 1 — LLM Paper Reading (25.05~25.07):
Studied the evolution of LLM architectures (Attention, Transformer, Deepseek) and alignment methods (RLHF, DPO). [Study Link]

Session 2 — SLM Fine-tuning (25.07~25.09):
Conducted practical fine-tuning experiments comparing LoRA, QLoRA, and full fine-tuning using DistilGPT2 and GPT2-Medium on the CNN/DailyMail summarization task. Compared VRAM usage, trainable parameters, training speed, and generation quality. [Study Link] [Colab Notebook]

Session 3 — LangChain/LangGraph Agent Development (25.09~26.02):
Built document search systems using RAG (LangChain + FAISS/Chroma + OpenAI Embeddings). Developed multi-turn conversational RAG chatbots with LangGraph memory (checkpointer). Implemented intent-based routing with conditional edges in LangGraph. Deployed FastAPI services with LangSmith monitoring on Railway. Built demo interfaces with Streamlit. [Study Link]

Github Repository Recommendation Service (22.05~22.06)

Built a personalized recommendation engine for GitHub repositories as the final project of Naver Boostcamp AI Tech (Recommender System track).

• Problem: Users struggle to discover relevant repositories among GitHub's millions of repos.
• Approach: Developed ML ranking algorithms with a hybrid recommendation model to mitigate cold-start issues.
• Role: Designed and implemented the main API server (Node.js + MongoDB), handling model inference and user service APIs.
• Team: 5 members, responsible for backend architecture and recommendation serving.

GitHub: https://github.com/boostcampaitech3/final-project-level3-recsys-04

Source code from jonbarron