Hello, I'm
ML Engineer & Data Scientist
I build ML systems judged by what they move — a loss ratio, an approval rate, a completion curve. First-author publication in bioinformatics, recommendation systems used by millions, and now decision intelligence at Ailys.
↳ Based in Seoul — happiest shipping from anywhere with a good view.
The part of ML that's easiest to skip is the last mile: making a model actually change a decision. That's the only version I find worth doing. Right now that's decision intelligence at Ailys — building ML for insurance and loan judgments measured by loss ratio and approval rate, not accuracy on a held-out set.
I've worked across applied ML — recommendation and knowledge tracing used by millions, computer vision on real construction sites, and peptide-identification research I published as first author. The throughline isn't one technique; it's carrying a problem from "no one's framed this yet" to something that ships and holds up in the real world.
The domain keeps changing — bioinformatics, edtech, fintech — but the question doesn't: how does something that works in a notebook move something real in the world?
Ailys — Data Scientist
Apr 2025 – Present
Building ML for insurance risk and loan approval decisions on the DEIN platform — judged by loss ratio and net interest margin, not accuracy alone.
TmaxEduAI — AI Engineer
Jan 2023 – Nov 2024
Built knowledge tracing and recommendation systems serving 2M+ learners — lifting course enrollment 5% and content completion 25% — shipped via Airflow pipelines.
Bioinformatics and Intelligent Systems Lab, Hanyang University — Graduate Research Assistant
Aug 2020 – Dec 2022
Designed ML/DL-based post-processing tools for reranking and rescoring to improve peptide identification accuracy in de novo sequencing.
The Construction Systems Laboratory, Inha University — Undergraduate Researcher
Jul 2019 – Feb 2020
Developed image classification and object detection models to detect hazardous objects in indoor construction environments.
NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning
Jangho Seo, Seunghyuk Choi, Eunok Paek — J. Proteome Res. 2025, 24, 2, 903–910
Read paper →Decision Intelligence · Insurance & Loan
On Ailys's DEIN platform I build ML judged by business KPIs — an insurance disease-risk model (recall >93%, ↓ loss ratio) and loan reject inference that expanded approvals +4.6% with no change in delinquency (+$2.21M NIM).
Recommendation Systems · Knowledge Tracing
Course recommendations for a 2M+ user platform were generic and didn't reflect learner progress. I built a deep learning recommendation model on sequential interaction data and shipped it through an Airflow pipeline, lifting course enrollment (CVR) by 5% and content completion by 25%.
Bioinformatics · Deep Learning · Clustering
Conventional de novo peptide sequencing tools score spectra in isolation and frequently misidentify peptides. I built a two-step clustering and deep learning re-ranking pipeline that cross-checks candidates against similar spectra, improving precision and recall by an average of 4.6% and 4.5% across three state-of-the-art tools.
Computer Vision · Image Augmentation
Recognizing small hazard objects on construction sites is data-hungry, but labeled images are scarce. I studied which image-augmentation strategies actually help, lifting a CNN's accuracy from 85.7% to 87.6% — and found rotation hurts more than it helps for these objects.
Python, SQL, Java
PyTorch, TensorFlow, scikit-learn, Polars, Airflow, AWS SageMaker
Decision Intelligence (insurance & loan), Recommendation Systems, Knowledge Tracing, Computer Vision, NLP, Bioinformatics