Title:
Understanding Human Functioning & Enhancing Human Potential through Computational Methods
Understanding Human Functioning & Enhancing Human Potential through Computational Methods
| dc.contributor.author | D'Mello, Sidney K. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Data Engineering and Science | en_US |
| dc.contributor.corporatename | University of Colorado, Boulder. Dept. of Computer Science | en_US |
| dc.contributor.corporatename | University of Colorado, Boulder. Institute of Cognitive Science | en_US |
| dc.date.accessioned | 2020-10-27T16:56:53Z | |
| dc.date.available | 2020-10-27T16:56:53Z | |
| dc.date.issued | 2020-10-08 | |
| dc.description | Presented online on October 8, 2020 at 2:00 p.m. | en_US |
| dc.description | Sidney D’Mello is an Associate Professor in the Institute of Cognitive Science and Department of Computer Science at the University of Colorado Boulder. His work lies at the intersection of the computing, cognitive, affective, social, and learning sciences. D’Mello is interested in the dynamic interplay between cognition and emotion while individuals and groups engage in complex real-world activities. | en_US |
| dc.description | Runtime: 55:09 minutes | en_US |
| dc.description.abstract | 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. | en_US |
| dc.format.extent | 55:09 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/63801 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | IDEaS-AI Seminar Series | en_US |
| dc.subject | Behavior | en_US |
| dc.subject | Computational methods | en_US |
| dc.subject | Data | en_US |
| dc.subject | Human computer interaction | en_US |
| dc.title | Understanding Human Functioning & Enhancing Human Potential through Computational Methods | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Institute for Data Engineering and Science | |
| local.relation.ispartofseries | IDEaS Seminar Series | |
| relation.isOrgUnitOfPublication | 2c237926-6861-4bfb-95dd-03ba605f1f3b | |
| relation.isSeriesOfPublication | 315185f2-d0ec-4ea2-8fdc-822ed04da3a8 |
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