Jaewook Lee | CS Ph.D. Candidate @ UMass Amherst

Education

University of Massachusetts Amherst
- Ph.D. in Computer Science (2022.9 - present)
- Advisor: Prof. Andrew Lan
- Research Area: NLP Application, Human-in-the-loop AI
- Passed Qualification Exam with distinction (2025.05)
Korea University, Seoul, Republic of Korea
- M.E. in Electrical and Computer Engineering (2019.9 - 2022.2)
- Advisor: Prof. Seon Wook Kim
- Research Area: Compiler, Processing-in-Memory, AI framework
Korea University, Seoul, Republic of Korea
- B.E. in Electrical Engineering (2013.3 - 2019.8)
- Graduated with honors

Work Experience

Apple, Santa Clara, CA, United States
- Machine Learning Engineer Intern
- Contributing to multimodal search systems for Apple’s Answers, Knowledge, and Information team; additional technical details limited by confidentiality.
Amazon Web Service, Seattle, WA, United States 
- Applied Scientist Intern
- Designed the evaluation methodology for a production multi-agent AppSec framework, defining task rubrics and assessments
  for design reviews, pull-request code reviews, and context-aware penetration testing.
Eedi, London, United Kingdom (Remote)
- Machine Learning Research Intern
- Built the prototype for AnSearch, an AI-driven math diagnostic question generator that won the Tools Competition, combining LLM speed with educator expertise to create assessments targeting common misconceptions.

Research Experience

Agentic RAG, Tool Use & Retrieval Systems
- Built an agentic RAG policy that trains LLMs with reinforcement learning to choose retrieval granularity and coordinate keyword/semantic search tools (Submitted to 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 (Manuscript in preparation).
Post-Training, Dialogue Systems & Model Behavior
- Developing SAE-based interpretability methods to identify latent dialogue traits and steer LLM behavior toward interpretable user characteristics (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).
AI Systems and Platform Optimization (Industry–Academia collaboration)
- PIM platforms: Developed ONNX Runtime integration for PIM on both x86 and ARM environments; designed profiling and scheduling algorithms to optimize DNN inference on heterogeneous PIM architectures (SK Hynix)
- Compiler-based frameworks for NPUs: Built tools to extract memory traces from DNN accelerators and modified LLVM to generate code that maximizes scratchpad memory efficiency (Samsung)

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 (2022)
- Organized by EEDI
- Challenge to identify causal relationship 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

Key Skills & Strengths

Date Published

August 9, 2025

Author

Jaewook Lee

My journey so far

“Used to map matmuls to silicon. Now I wrap LLMs in uncertainty—and snap when the app shows a gap.”

It’s a playful rhyme for my journey: from studying electrical engineering in my bachelor’s to exploring large language models in education for my Ph.D.

Back in 2018—pre-ChatGPT era—I participated in an iOS Hackathon and built a vocabulary-builder prototype that won the grand prize. My vision for future work was to automatically generate keyword mnemonics and pair them with images combining those keywords. At the time, automatically generating keyword mnemonics was impossible, so I instead pursued a master’s in computer engineering to build strong foundations in both hardware and software.

During my master’s, I worked extensively with FPGA platforms. A notable project was developing a software interface for Processing-in-Memory (PIM) emulator, which became the basis for my first journal publication. My role involved creating an interface between the PIM hardware and ONNX Runtime, then designing a scheduling algorithm to optimize execution on a heterogeneous platform. Because the device had no internet access, the only way to transfer the framework was via an SD card—a process I repeated so many times that the card eventually wore out and had to be replaced.

From this experience, I discovered two things:

I love writing papers—it feels like wrapping a hard-earned gift.
I finally grasped the value of a top-down approach in research, after struggling early in my master’s by treating research like coursework.

I chose a Ph.D. in my current field because I wanted to make education more engaging. The vocabulary-builder project remains one of my happiest moments—coding all night felt like pure fun. When I started my Ph.D., the sudden boom of LLMs made possible what I had once only imagined: automatic mnemonic generation. This became the topic of my first publication.

Now, I work on applying machine learning, reinforcement learning, and evaluation methods for LLM-generated content, as well as developing web apps for conducting human evaluations.

Hi, I am Jaewook