In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we've been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We've been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.
Being highly enthusiastic about research in deep learning I was always searching for unexplored areas in the field (Though it is tough to find one). I had previously worked on Maths word problem…
This study examined the question, ‘What is the impact of a digital math intervention on secondary ELL
students’ mathematical capabilities and perceptions of their future possibilities?’ The hypothesis was
that through its direct effect on increasing students’ math ability and its indirect effect on increasing
students’ perceived math self-efficacy, the digital intervention affects students’ perceptions of their
functionings and future possibilities. A path analysis, with qualitative data nested into the design, was
used to analyze the conceptualized relationships. The study was conducted with 50 ninth-and-10thgrade
Hispanic students in a Colorado high school, over 6 months. The primary finding was that
through its direct effect on increasing students’ math ability and its indirect effect on increasing students’
perceived math self-efficacy, the digital intervention improved students’ perceptions of their functionings
and future possibilities. What this study specifically underscores is the importance of taking
a coherent and purposeful approach toward the design of digital student-directed educational technology,
especially for ELL students who may have specific learning needs.
Abridged transcription of his talk about the different affordances of various media, especially books and screens. For the former, written text is foregrounded, whereas images are foregrounded for the latter.
D. Khattar, J. Goud, M. Gupta, and V. Varma. The World Wide Web Conference, page 2915–2921. New York, NY, USA, Association for Computing Machinery, (2019)
R. Heinrich, C. Zimmerer, M. Fischbach, and M. Latoschik. Proceedings of the 27th International Conference on Multimodal Interaction (ICMI '25), Association for Computing Machinery, (2025)
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
G. Cheema, S. Hakimov, A. Sittar, E. Müller-Budack, C. Otto, and R. Ewerth. Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, WA, United States, July 10-15, 2022, page 962--979. Association for Computational Linguistics, (2022)