CAREER: Large-Scale Multi-Objective Learning: Novel Algorithms and Fundamental Theory — NSF Award to SUNY at Buffalo (NY, $549,999
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 domain
| Award title | CAREER: Large-Scale Multi-Objective Learning: Novel Algorithms and Fundamental Theory |
|---|---|
| Award ID | 2442418 |
| Awardee | SUNY at Buffalo |
| City | AMHERST |
| State | NY |
| Amount obligated | $549,999 |
| Principal investigator | Kaiyi Ji |
| Program | CCSS-Comms Circuits & Sens Sys |
| Start date | 09/01/2025 |
| 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 bes |
| Source | NSF Awards |
$799/mo
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