# CAREER: Large-Scale Multi-Objective Learning: Novel Algorithms and Fundamental Theory

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · SUNY at Buffalo (NY) · $549,999

## Abstract

Many real-world AI and big data applications, including 5G networks, autonomous systems, healthcare, finance, recommendation engines, and large foundation models, frequently involve multiple, often competing objectives arising from complex environments, conflicting goals, and vast datasets encompassing different domains and modalities. Multi-objective optimization (MOO) provides a robust theoretical framework for navigating these challenges by identifying sets of solutions that represent the best trade-offs among objectives. Despite notable efforts toward conflict-avoidant MOO approaches, algorithmic and theoretical progress in large-scale, data-driven settings remains limited. This project aims to significantly advance the theoretical and algorithmic foundations of MOO, offering provably convergent and efficient stochastic, bilevel, and fairness-aware MOO algorithms. Its outcomes hold the promise of propelling MOO research to new heights, with broad impacts on both theory and practice across wireless communication networks, multi-agent transportation and robotics systems, recommendation systems, and foundation models. The research outcomes are integrated into education and outreach activities for K-12 educators, graduate, and undergraduate students through (i) summer camp for K-12 students, (ii) student supervision, (iii) Experiential Learning and Research (ELR) undergraduate activity, (iv) CSE Colloquium and Upbeat events, and (v) course development.

The research efforts

## Key facts

- **NSF award ID:** 2442418
- **Awardee organization:** SUNY at Buffalo (NY)
- **SAM.gov UEI:** LMCJKRFW5R81
- **PI:** Kaiyi Ji
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Wireless comm & sig processing, CAREER-Faculty Erly Career Dev
- **Estimated total:** $549,999
- **Funds obligated:** $549,999
- **Transaction type:** Continuing Grant
- **Period:** 09/01/2025 → 08/31/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2442418

## Citation

> US National Science Foundation, Award 2442418, CAREER: Large-Scale Multi-Objective Learning: Novel Algorithms and Fundamental Theory. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2442418. Licensed CC0.

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