Limited openings
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)
SOCAL Lab accepts a small number of M.S., Ph.D., and undergraduate researchers each cycle in federated learning, synthetic data, and AI alignment (LLM).
If you are interested, please visit Prospective students.
Principal 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 works on federated learning (data privacy), synthetic data, and AI alignment (LLM) with statistical rigor. SOCAL Lab is intentionally small; fit and preparation matter. See Prospective students for capacity, funding, and how to inquire.
연합학습·합성 데이터·AI 정렬(LLM)을 통계적 엄밀성과 함께 연구하는 소형랩입니다. 적합성과 준비도를 중시합니다. 정원·재정·문의 방법은 Prospective students 페이지를 참고해 주세요.
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
Orchestrating Federated Tabular Synthesis at Scale with Fault-Tolerant Pipeline Execution
Calibrated mixup for imbalanced regression on tabular data
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)
Active journal submissions in editorial portals.
MIDAS Nowcasts Deserve Better Prediction Intervals
First-Release Nowcasts Need the Right Engine, Not Another Lag Scheme
Density-Stratified Distributionally Robust Optimization with Localized Risk Guarantees
Minimax-Optimal Interpolation in Multi-Source Regression Under Response Heterogeneity
CrossSynth: Scalable Federated Synthetic Data Generation with Single-Round Differentially Private Marginal Aggregation
The Privacy Journey of IoT Data: A Lifecycle Survey of Differential Privacy and Synthetic Data
Not All Large Language Model Deployments Are Created Equal: A Taxonomy-Driven Survey of Security, Defense, and Governance
Improving Temporal Consistency in Text-Based Indices with Rank-Informed Calibration
What Survey Statisticians Can Teach Federated Learning about Non-IID Data
Neighbors Know Best: Joint Manifold Augmentation for Imbalanced Tabular Regression
Under review (conferences)
ProtoIR: Prototype-Guided Statistical Learning for Imbalanced Regression
Not All Synthetic Rows Are Equal: Copula-Calibrated Augmentation for Imbalanced Regression
Join the lab
Prospective students
SOCAL Lab is a boutique lab at KNU: we do not aim to grow a large cohort. We keep membership limited so the PI can mentor each student closely.
KNU 소형랩으로, 멘토링 품질을 위해 연구실 인원을 일정 수준 이하로 유지합니다.
What we offer
We invest in a few students at a time: scoped projects, dedicated compute, and support for strong ML and application venues. Financial support may be available through the Brain Korea (BK) scholarship program and the PI’s personal research funding (tuition and living expenses where applicable, per university rules).
소수 인원에 집중합니다. BK(Brain Korea) 장학금과 교수 개인 연구비로 재정 지원(규정 범위 내 등록금·생활비 등).
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Financial support Brain Korea (BK) scholarship where eligible, plus PI personal research funding for tuition and living on funded projects. BK 장학금 및 교수 개인 연구비.
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Dedicated compute Lab nodes including high-memory NVIDIA RTX 5090 GPUs for model training. RTX 5090 등 전용 GPU.
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Publication-oriented research Active work toward conference and journal outputs in the lab’s core areas. 학회·저널 목표의 연구 수행.