It is generally accepted that computational methods can complement traditional approaches to understanding human functioning, including thoughts, feelings, behaviors, and social interactions. I suggest that their utility extends beyond a mere complementary role. They serve a necessary role when data is too large for manual analysis, an opportunistic role by addressing questions that are beyond the purview of traditional methods, and a promissory role in facilitating change when fully-automated computational models are embedded in closed-loop intelligent systems. Multimodal computational approaches provide further benefits by affording analysis of disparate constructs emerging across multiple types of interactions in diverse contexts. To illustrate, I will discuss a research program that use linguistic, paralinguistic, behavioral, and physiological signals for the analysis of individual, small group, multi-party, and human-computer interactions in the lab and in the wild with the goals of understanding cognitive, noncognitive, and socio-affective-cognitive processes while improving human efficiency, engagement, and effectiveness. I will also discuss how these ideas align with our new NSF National AI Institute on Student-AI Teaming and how you can get involved in the research.