Minha Hwang

About

Recursive Self-Improving System, Causal Inference, Synthetic and Simulated Data for Enterprise AI Assistant and Agents, Privacy-preserving algorithms, AI Search, Enterprise Search and Agent Evaluation and Post-Training.

As a Principal Applied Scientist at Microsoft, I leverage my expertise in synthetic data, AI evaluation, AI engineering, natural language processing (NLP), causal inference, A/B testing, online/offline metric design, and machine learning (ML) to drive innovation and impact. With over 15 years of experience in deep learning, machine learning, and data science, I have a proven track record of delivering cutting-edge solutions for search and retrieval, AI/LLM evaluation, inverse RL, preference measurement and alignment, AI/ML interpretability, A/B testing, pricing science, assortment optimization, and user behavior modeling problems.

My passion lies in applying LLM, preference measurement and alignment, causal inference from observational data, experimentation, pricing, promotion, assortment, purchase structure, personalization, digital marketing campaign design and evaluation, customer relationship management, and more.