Curriculum vitae · Updated October 2026
Jaewook (Jake) Lee
Natural Language Processing · ML Systems
Technical Skills
- Core: LLM Post-Training, Reinforcement Learning, Supervised Fine-tuning, Preference Optimization, LLM Evaluation, Agentic RAG, Personalization, Dialogue Systems, User Simulation, Vision-Language Model
- Systems: Multi-Turn Conversational AI, Retrieval Systems, Tool Use, Multi-Agent Systems, Large Language Models, Vision-Language Models, vLLM, RAG Accelerators, AI Accelerators
- Evaluation & Alignment: Human Evaluation, Benchmark Design, Evaluator Design, Reward Modeling
- Engineering: Python, PyTorch, Hugging Face, iOS, React, Go
Experience
Apple
- Contributed to multimodal search systems by fine-tuning VLM for Apple's Answers, Knowledge, and Information team; additional technical details limited by confidentiality.
Amazon Web Services
- Designed the evaluation methodology for a production multi-agent AppSec framework, defining task rubrics and assessments for design reviews.
Eedi
- Prototyped AnSearch, an LLM-based diagnostic-generation system that combines model outputs with domain expertise to create targeted assessments for identifying student misconceptions.
Selected Research
Agentic RAG, Tool Use & Retrieval Systems
- Developing reinforcement learning and on-policy self-distillation methods to improve spatiotemporal reasoning in VLMs for long-video question answering. (Ongoing project)
- Built an agentic RAG policy that trains LLMs with reinforcement learning to choose retrieval granularity and coordinate keyword/semantic search tools (EMNLP 2026).
- Evaluated latency–accuracy trade-offs in software–hardware co-designed RAG, analyzing how approximate vs. exact nearest-neighbor retrieval affects time-to-first-token and downstream answer quality (Submitted to HPCA 2027).
Post-Training, Dialogue Systems & Model Behavior
- Developing tool-using LLM agents that simulate students by editing executable (SymPy) solution programs to reproduce each student’s reasoning errors, to predict their future mistakes (Ongoing project).
- Built an evaluation framework for LLM-simulated users in tutoring dialogues, measuring linguistic, behavioral, and cognitive realism and showing that surface-level plausibility can mask failures in learning dynamics (ACL 2026).
- Developed a preference-optimization method for deriving tutor-persona steering vectors from human tutor-student dialogues, enabling interpretable control of LLM dialogue behavior without explicit persona prompting (ACL 2026 BEA Workshop).
- Developed an Expectation Maximization-based personalization framework that infers community-level and user-specific latent preferences, enabling LLM generation under cold-start conditions (EMNLP 2025).
Applied NLP, Personalization & Human-Centered Evaluation
- Built an iOS prototype for adaptive LLM generation that personalizes learning content to individual user preferences through interactive feedback (CHI EA 2026).
- Led research on phonological similarity algorithms for cross-lingual keyword discovery grounded in user language priors, supporting personalized mnemonic generation (EMNLP 2025).
- Designed evaluation pipelines combining psycholinguistic metrics with human assessment to measure memorability and creative quality in LLM-generated mnemonics (EMNLP 2024).
Education
University of Massachusetts Amherst
- Advisor: Dr. Andrew Lan
- Research: LLM Post-Training and Evaluation, Personalization, Human-in-the-Loop AI
- Mentored student-led ML/NLP research projects, including collaborations with Spotify and Adobe Research
- Passed qualifying exam with distinction (May 2025); M.S. earned Feb. 2026
Korea University
- M.E. (Jan. 2019 – Feb. 2022), Advisor: Dr. Seon Wook Kim
- Research: Compiler Optimization, Processing-in-Memory, AI Frameworks
- Developed ONNX Runtime integration and profiling/scheduling algorithms for PIM-based DNN inference with SK Hynix
- Built NPU compiler tooling for memory-trace extraction and scratchpad-aware LLVM code generation with Samsung
- B.E. (Mar. 2013 – Aug. 2019), Graduated with honors; Mandatory Military Service (21 months)
Awards
- NAEP Math Automated Scoring Challenge Grand Prize 🏆 (2023)
- Organized by National Center for Education Statistics (NCES)
- Challenge to develop an accurate, LLM-based scoring system for open-ended math responses
- NeurIPS 2022 Causal Edu Competition (Task 3), 3rd Place (2022)
- Organized by Eedi
- Challenge to identify causal relationships in real-world educational time-series data
- iOS Application Hackathon Grand Prize 🏆 (2018)
- Organized by Software Technology and Enterprise, Korea University
- Developed a vocabulary builder app designed to help users efficiently memorize new words