# Collaborative Research: CIF: Small: New Theory, Algorithms and Applications for Large-Scale Bilevel Optimization

> **NSF 01002324DB NSF RESEARCH & RELATED ACTIVIT** · Johns Hopkins University (MD) · $299,549

## Abstract

In recent years, the world has witnessed significant progress in optimization for emerging fields, including meta-learning, fine-tuning, automated hyperparameter selection, continual learning, fair batch selection, adversarial learning, and artificial intelligence (AI)-aware communication networks. Problems arising from these fields often exhibit a common nested optimization structure, which has motivated the study of bilevel optimization. However, there are many theoretical and computational challenges in large-scale bilevel optimization problems, e.g., those arising from machine learning on massive amounts of data in high-dimensional feature domains that have manifold constraints. This project will provide a comprehensive study of bilevel optimization theory, algorithms, and applications for large-scale problems. The outcomes of this project will benefit researchers in academia, government labs, and industry aiming to solve large-scale nested optimization problems in science and engineering. New applications in information science, signal processing, communications, statistics, and machine learning will be studied. 

This project consists of three intertwined thrusts. The first thrust focuses on developing fast and scalable Hessian-free bilevel algorithms with convergence rate guarantees. Specifically, several Hessian-free approaches will be designed and analyzed using methods of fully single-loop momentum, finite-difference matrix-vector estimation, and residual response

## Key facts

- **NSF award ID:** 2626366
- **Awardee organization:** Johns Hopkins University (MD)
- **SAM.gov UEI:** FTMTDMBR29C7
- **PI:** Shiqian Ma
- **Primary program:** 01002324DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, COMM & INFORMATION FOUNDATIONS, SMALL PROJECT
- **Estimated total:** $299,549
- **Funds obligated:** $188,168
- **Transaction type:** Standard Grant
- **Period:** 07/01/2026 → 07/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2626366

## Citation

> US National Science Foundation, Award 2626366, Collaborative Research: CIF: Small: New Theory, Algorithms and Applications for Large-Scale Bilevel Optimization. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2626366. Licensed CC0.

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