# CAREER: Trustworthy, Robust, and Efficient Multimodal Framework for Video Analytics.

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of Arkansas (AR) · $499,556

## Abstract

Artificial intelligence (AI) technologies are revolutionizing fields such as healthcare, robotics, agriculture, public safety, and many more. However, the increasing reliance on AI also brings concerns about trust, reliability, and real-world conditions that pose significant risks to the long-term value of AI. This project tackles these challenges by developing a human-inspired AI framework that processes data in ways similar to human perception - drawing information from multiple sensory inputs, providing transparent and explainable decisions, performing reliably even with noisy or missing inputs, and operating efficiently with reduced energy use. These innovations will help ensure that AI technologies are safe, and accountable, aligning with national priorities for secure, ethical, and responsible AI development. It will offer hands-on experiences to K–12 students, mentoring to undergraduate and graduate students, and developing new college courses on trustworthy machine learning, thereby cultivating a next generation of scientific leaders.

Despite significant advances in AI, current systems remain limited by key challenges: they are often task-specific, brittle to real-world disruptions, computationally intensive, constrained to single modalities, and lack interpretability. To address these limitations, this project develops a unified multimodal analytics framework centered on three core research goals: interpretability, robustness, and efficiency. First, it introduces 

## Key facts

- **NSF award ID:** 2443877
- **Awardee organization:** University of Arkansas (AR)
- **SAM.gov UEI:** MECEHTM8DB17
- **PI:** Ngan Le
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CAREER-Faculty Erly Career Dev, EXP PROG TO STIM COMP RES
- **Estimated total:** $499,556
- **Funds obligated:** $499,556
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2443877

## Citation

> US National Science Foundation, Award 2443877, CAREER: Trustworthy, Robust, and Efficient Multimodal Framework for Video Analytics.. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2443877. Licensed CC0.

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