# Risk-Sensitive Learning-Augmented Adaptive Control Algorithms and Applications in Stochastic Networks

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · William Marsh Rice University (TX) · $279,654

## Abstract

This grant supports research that will contribute to the advancement of national prosperity and social and economic welfare by developing adaptive control and learning algorithms to solve complex, practical problems arising from networked systems with uncertainties. Large-scale service operations, manufacturing and production systems, inventory and logistics, healthcare patient flows, telecommunications, and cloud computing all have complex network structures and often face various challenging operational risks such as sudden changes in demand or disruptions in service. Unlike traditional methods that assume full knowledge of system behavior, this research will create new algorithms that can learn from data and adapt in real time, while also accounting for risk and variability in outcomes - weighing in on the potentially high fluctuations around the average values of certain performance metrics. Beyond the technical contributions, the project will enhance STEM education by integrating cutting-edge research into both undergraduate and graduate curricula. It will prepare students with advanced mathematical and engineering skills needed to lead in fields like artificial intelligence, operations research, industrial and systems engineering - strengthening the U.S. science and engineering workforce.
 
This research will advance the computational and learning methods of risk-sensitive control of Markov chains and diffusions and their applications in stochastic networked systems. 

## Key facts

- **NSF award ID:** 2452829
- **Awardee organization:** William Marsh Rice University (TX)
- **SAM.gov UEI:** K51LECU1G8N3
- **PI:** Guodong Pang
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** NETWORKS & QUEUING SYSTEMS, OPTIMIZATION & DECISION MAKING, OPERATIONS RESEARCH
- **Estimated total:** $279,654
- **Funds obligated:** $279,654
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2452829

## Citation

> US National Science Foundation, Award 2452829, Risk-Sensitive Learning-Augmented Adaptive Control Algorithms and Applications in Stochastic Networks. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2452829. Licensed CC0.

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