Mon, 9 May, 13:00 - 13:45 UTC
Recently we have seen a rapid rise in the amount of education data available through the digitization of education. This huge amount of education data usually exhibits in a mixture form of images, videos, speech, texts, etc. It is crucial to consider data from different modalities to build successful applications in AI in education (AIED). This talk targets AI researchers and practitioners who are interested in applying state-of-the-art multimodal machine learning techniques to tackle some of the hard-core AIED tasks. These include tasks such as automatic short answer grading, student assessment, class quality assurance, knowledge tracing, etc.
In this talk, I will share some recent developments of successfully applying multimodal learning approaches in AIED, with a focus on those classroom multimodal data. Beyond introducing the recent advances of computer vision, speech, natural language processing in education respectively, I will discuss how to combine data from different modalities and build AI driven educational applications on top of these data. Participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit real-world AIED applications.
Biography
Zitao Liu is the Head of Engineering, Xueersi 1 on 1 at TAL Education Group (NYSE:TAL), one of the largest leading education and technology enterprises in China. His research is in the area of machine learning, and includes contributions in the areas of artificial intelligence in education, multimodal knowledge representation and user modeling. He has published his research in highly ranked conference proceedings, such as NeurIPS, AAAI, WWW, AIED, etc. and serves as the executive committee of the International AI in Education Society and top tier AI conference/workshop organizers/program committees. He won the 1st place at NeurIPS 2020 education challenge (Task 3), 1st place at Ubicomp 2020 time series classification challenge, 1st place at CCL 2020 humor computation competition and 2nd place at EMNLP 2020 ClariQ challenge. He is a recipient of ACM/CCF Distinguished Speaker and Beijing Nova Program 2020. Before joining TAL, Zitao was a senior research scientist at Pinterest and received his Ph.D degree in Computer Science from University of Pittsburgh.