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