table-synthesizers
Jan 15, 2026
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1 min read
Unified 16 synthesizer architectures (CTGAN, TVAE, TabDDPM, AutoDiff, TabSyn, AIM, PATECTGAN, DPCART, Bayesian Network, ARF, NFlow, GREAT, and more) behind a single factory interface with per-model production configs. Built the end-to-end training → generation → evaluation loop, integrating with alfred-analytica for fidelity and machine-learning-efficacy scoring against real data. Deployed distributed training on NCSA DeltaAI (SLURM, H100 GPU nodes) with reproducible experiment tracking and versioned experiment outputs.
Table-Synthesizers is the generation and measurement substrate of my Master’s thesis, Toward an Auto-Synthesizer: Stress-Aware Selection and Recommendation of Deep Tabular Generative Models.
Authors
Hochan Son
(he/him)
Database Architect · SRE & DevOps Engineer · ML/GenAI Practitioner
Hochan Son is a distributed systems architect and site reliability engineer
who builds data infrastructure, ML pipelines, and high-scale distributed
systems, working the gap between GenAI/LLM research and the systems that run it
in production. His graduate research at the UCLA Trustworthy AI Lab spans
synthetic tabular data generation and agentic, intent-conditioned model
selection. His background spans Adtech, entertainment, and enterprise — from
MySpace and Hallmark Labs to Branch.io and ADP.