Actively recruiting
Where statistics meets AI
SOCAL Lab — Statistical Optimization & Collaborative AI Learning at Kyungpook National University (KNU).
Prof. Nathaniel Kang (강네이트)
Assistant Professor · Department of Computer Science and Engineering (CSE) · Kyungpook National University (KNU)
Open recruitment — Ph.D., M.S., and undergraduate researchers are sought in federated learning (data privacy), synthetic data, and AI alignment (LLM).
석·박사 및 학부연구생 모집Principal investigator
About the PI
From Irvine and Seoul to Daegu — bridging rigorous statistics with secure, real-world AI.
I am an Assistant Professor in the Department of Computer Science and Engineering (CSE), Kyungpook National University, starting March 2026. Before KNU, I was a Postdoctoral Fellow in Statistics at Yonsei University and a Researcher at UCLA.
My research group focuses on federated learning (data privacy), synthetic data, and AI alignment (LLM) — with statistical rigor throughout. I'm looking for curious students who want to go deep on hard ML problems under close faculty mentorship, with funded projects. Prospective students are welcome to contact me.
PhD
Yonsei University
Statistics and Data Science
Statistics and Data Science
MS
University of California, Los Angeles
Master of Science in Engineering
Master of Science in Engineering
BS
UCLA & NTU
Computer Science / Statistics
Computer Science / Statistics
Research program
What we work on
Three threads — one goal: models and systems you can trust when data are messy, private, or rare.
Federated learning & privacy
Robust aggregation and learning when local data are non-IID and never leave the device.
See detailsImbalanced data
Calibration, synthesis, and regression when the tail of the distribution matters most.
See detailsAI alignment (LLM)
Auditing and privacy frameworks as LLMs and public-sector AI scale in the wild.
See detailsScholarship
Publications
Selected peer-reviewed work. Full bibliography on Google Scholar.
2026
2025
Optimizing federated learning: Addressing key challenges in real-world applications
Generalized additive models for mixed-data regression using informal data
Re-sampling calibrated SNN loss: A robust approach to non-IID data in federated learning
2024
Online news-based economic sentiment index
Working papers
Manuscripts under review or in active revision for resubmission — titles may change with revision.
Journals: venues name the current submission target and may change after revision or resubmission. Conferences: while a paper is under anonymous (double-blind) review, we do not list the conference or track name on this page.
Under review (journals)
Calibrating Non-IID Federated Models with Conditional Cryptographic Privacy
Not All Large Language Model Deployments Are Created Equal: A Taxonomy-Driven Survey of Security, Defense, and Governance
Measuring the Implementation Gap: A Framework for Auditing Policy Erosion in Public Sector Technology Procurement
Orchestrating Federated Tabular Synthesis at Scale with Fault-Tolerant Pipeline Execution
CrossSynth: Federated Differentially Private Tabular Data Synthesis with Cross-Party Correlation Preservation
One Privacy Budget to Rule Them All: Synthesizing Multi-Table Databases Under Differential Privacy
Making Gradient Boosting Care About Rare Targets
LLMSynth: LLM-Augmented Semantic Constraint Discovery for High-Fidelity Neural Tabular Data Augmentation
In revision (journals)
Improving Temporal Consistency in Text-Based Indices with Rank-Informed Calibration
Under review (conferences)
FedSynth: Federated Differentially Private Tabular Data Synthesis
ProtoIR: Prototype-Guided Statistical Learning for Imbalanced Regression
CopulaCalib: Correlation-Preserving Calibration for Imbalanced Regression on Tabular Data
Conditional Diffusion Models for Imbalanced Tabular Regression
Join the lab
Prospective students
SOCAL Lab is actively recruiting Ph.D., M.S., and undergraduate researchers. If you are motivated, collegial, and serious about statistical machine learning and its applications in modern AI systems, please get in touch — I read every message.
What we offer
The lab emphasizes close collaboration between students and faculty: well-scoped projects, dedicated compute, and support for strong venues in both machine learning and application-focused outlets where the ideas fit best.
연구실에서는 교수와 학생이 가깝게 협력하는 분위기를 가장 중요하게 생각합니다. 연구 과제는 범위를 분명히 정해 진행하고, 실험에는 전용 연산 자원을 쓰며, 논문은 연구 내용에 맞는 학회나 저널을 함께 고르고 다듬어 나가고자 합니다. 관심 있으시면 부담 없이 메일 주시면, 보내주신 내용을 꼼꼼히 읽고 답드리겠습니다.
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Full financial support BK scholarship eligibility and lab support for tuition and living expenses where applicable. BK(Brain Korea) 장학금 지원 및 연구실 연구비를 통한 재정 지원.
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Dedicated compute Lab nodes for large experiments — including access to high-memory NVIDIA RTX 5090 GPUs for model training. 대규모 실험을 위한 전용 GPU 워크스테이션 (RTX 5090 등).
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Real projects, real impact Government-backed AI projects — experience that transfers to top industry labs and research institutes. 정부 지원 AI 프로젝트 참여로 실무 역량 강화.