Boran Han

Senior Applied Scientist / Amazon AGI Foundations / AWS

Boran Han.

Building foundation models and learning systems that reason across agents, structured data, multimodal signals, and the physical world.

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Santa Clara, California / 37.35 N

Research focus / 01

Learning systems that move from representation to reasoning.

ReSkill framework for creating and testing reusable skills during agentic reinforcement learning

Agentic RL / 2026

Skills that evolve with the policy

Reconciling reusable skill creation with reward-driven policy optimization.

SenTSR-Bench knowledge injection framework for time-series reasoning

Reasoning / 2026

Thinking with injected knowledge

Bridging specialist time-series models and general reasoning models.

Chronos-2 architecture for universal time-series forecasting

Foundation models / 2025

Universal zero-shot forecasting

One model for univariate, multivariate, and covariate-informed time series.

HetRL scheduling and execution framework for reinforcement learning across heterogeneous GPU environments

Systems / MLSys 2026

RL across heterogeneous hardware

Efficient LLM post-training across mixed GPUs, regions, and interconnects.

By the numbers / 02

Research from foundational ideas to deployed models.

2K+ citations across machine learning, imaging, and natural science
20+ selected publications in leading conferences and journals
6M+ monthly downloads for Chronos-2 reported in 2026

Recent work / 03

Selected ideas, systems, and scientific applications.

SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning

AISTATS

ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL

Preprint

HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments

MLSys

MLZero: A Multi-Agent System for End-to-end Machine Learning Automation

NeurIPS

Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models

NeurIPS
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