# CAREER: Foundations of Machine Learning-Enhanced Branch-and-Bound Algorithms for Scalable Global Optimization

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · Virginia Polytechnic Institute and State University (VA) · $639,433

## Abstract

This Faculty Early Career Development Program (CAREER) grant will advance national energy security and economic welfare by developing improved tools for optimizing complex energy and industrial systems. Critical infrastructures, such as electric power grids and chemical refineries, depend on solving large optimization problems to determine safe and efficient operating conditions. Current optimization tools are inadequate for large-scale planning and operational needs, limiting the ability to operate these energy systems efficiently, modernize them, and maintain resilient operations. This project will create a new generation of optimization algorithms that use machine learning to leverage shared structure in real-world applications, significantly accelerating solution times while preserving mathematical guarantees. It will develop new machine learning techniques to guide key algorithmic decisions in optimization algorithms while ensuring scalability, generalizability, and data efficiency. These advances have the potential to transform how energy systems are designed and operated, enabling more efficient operations, improved reliability, and lower operational costs and environmental impact. The educational plan will introduce optimization and machine learning concepts into high-school classrooms through an interactive web-based tool, teacher workshops, and partnerships with regional schools. Undergraduate research, new graduate modules, and interdisciplinary workshops will prepare the next-generation workforce at the interface of artificial intelligence, optimization, and engineering.

This research will build a unified, theory-driven framework that leverages machine learning to enhance branch-and-bound algorithms for the guaranteed global optimization of mixed-integer nonlinear programs. It will (1) formulate new expert branching policies and develop supervised graph-based machine learning methods to imitate them; (2) create semi-supervised learning methods to gene

## Key facts

- **NSF award ID:** 2543398
- **Awardee organization:** Virginia Polytechnic Institute and State University (VA)
- **SAM.gov UEI:** QDE5UHE5XD16
- **PI:** Rohit Kannan
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** OPTIMIZATION & DECISION MAKING, CAREER-Faculty Erly Career Dev, OPERATIONS RESEARCH
- **Estimated total:** $639,433
- **Funds obligated:** $639,433
- **Transaction type:** Standard Grant
- **Period:** 05/15/2026 → 04/30/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2543398

## Citation

> US National Science Foundation, Award 2543398, CAREER: Foundations of Machine Learning-Enhanced Branch-and-Bound Algorithms for Scalable Global Optimization. Retrieved via AI Analytics 2026-06-26 from https://api.ai-analytics.org/grant/nsf/2543398. Licensed CC0.

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