Who I Am
I am currently a PhD student in the Human-Computer Interaction Institute at Carnegie Mellon University, working in the ArticuLab and OH Lab, where I am advised by Justine Cassell and Amy Ogan.
In my free time, I am the PI for a Metro21 Smart Cities Institute civic machine learning research project with the Pittsburgh Bureau of Fire, as well as an Assistant Director of the CMU Students for Urban Data Systems.
Before coming to CMU, I completed a M.S. in Digital Media at Georgia Tech, advised by Ian Bogost. I graduated from the University of Maryland with a Masters of Education and a Bachelors in English Language and Literature, and I taught at a public high school in Maryland for several years.
What I Do
I work at the intersection of Human-Computer Interaction, Learning Sciences, and ICT for Development, where I study human behavior in order to design systems that can better support people and society.
In my primary research area, I focus on educational applications, using methods from machine learning, HCI, and computational linguistics to understand how interpersonal social factors in learning can inform the design of more social educational technologies.
At a higher level, I am interested in how machine learning and AI systems are integrated into various aspects of civic life, from municipal decision-making to citizen data science with open civic data.
|July 2018||I'll be speaking at the Google Cloud NEXT event this week, about our work using machine learning to design "socially-aware" educational AI.|
|June 2018||Our paper won the Best Paper Award at the International Conference of the Learning Sciences (and was nominated for Best Student Paper)!|
|May 2018||Our teams recently had papers accepted to IWSDS [pdf], ACM COMPASS [pdf], and KDD [pdf]. I'm very thankful to work with such wonderful and talented collaborators in the ArticuLab, the OH Lab, and the Metro21 Smart Cities Institute!|
|April 2018||I presented a short paper at the HCI Across Borders Symposium at CHI 2018, about our work developing a mobile literacy support tool to scaffold parent-child literacy learning in Côte d'Ivoire.|
|March 2018||Pittsburgh's Mayor Peduto announced the launch of our Metro21 team's fire risk prediction tool at a press conference with the Bureau of Fire! The city has been a fantastic partner, and we're excited to deploy our model to improve public safety in Pittsburgh.|
|March 2018||I'll be spending the summer as a visiting research fellow at the United Nations Institute for Computing and Society. I'm excited to join the research group there!|
|Feb 2018||I was selected to be a mentor for the Uptake.org Data Fellows 2018 cohort. I'm looking forward to giving back to the data science community!|
|Jan 2018||Our Metro21 fire risk analysis project was awarded the "Innovation of the Month" by MetroLab Network, a national network of city-university partnerships!|
|Nov 2017||We published an article in the International Journal of Computer-Supported Collaborative Learning (IJCSCL), based on our CSCL Best Student Paper!|
Socially-Aware Educational Technologies
Madaio, M., Peng, K., Ogan, A., & Cassell, J. (2018). A climate of support: a process-oriented analysis of the impact of rapport on peer tutoring. In Proceedings of the 12th International Conference of the Learning Sciences (ICLS) , 2017. [*Best Paper Award Winner*] [*Best Student Paper Nominee*] [pdf]
Zhao, Z., Madaio, M., Pecune, F., Matsuyama, Y., & Cassell, J. (2018). Socially-Conditioned Task Reasoning for a Virtual Tutoring Agent. In Proceedings of the 17th International Conference of Autonomous Agents and Multi-Agent Systems (AAMAS). [pdf]
Goel, P., Matsuyama, Y., Madaio, M., & Cassell, J. (2018). “I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation. In Proceedings of the International Workshop of Spoken Dialogue Systems (IWSDS). [pdf]
Madaio, M., Cassell, J., & Ogan, A. (2017). “I think you just got mixed up”: confident peer tutors hedge to support partners’ face needs. In International Journal of Computer-Supported Collaborative Learning, 1-21. [pdf]
Madaio, M., Cassell, J., & Ogan, A. (2017, June). The Impact of Peer Tutors’ Use of Indirect Feedback and Instructions. In Proceedings of the Twelfth International Conference of Computer-Supported Collaborative Learning, 2017. [*Best Student Paper*] [pdf]
Madaio, M., Ogan, A., Cassell, J. (2017). Using Temporal Association Rule Mining to Predict Dyadic Rapport in Peer Tutoring. In Proceedings of the 10th International Conference on Educational Data Mining, 2017. [pdf]
Yu, H., Gui, L., Madaio, M., Ogan, A., Cassell, J., & Morency, L.P. (2017). Temporally Selective Attention Model for Social and Affective State Recognition in Multimedia Content. In Association for Computing Machinery Conference on Multimedia, 2017. [pdf]
Madaio, M., Ogan, A., & Cassell, J. (2016, June). The Effect of Friendship and Tutoring Roles on Reciprocal Peer Tutoring Strategies. In International Conference on Intelligent Tutoring Systems (pp. 423-429). Springer International Publishing. [pdf]
Educational Technologies for International Development
Uchidiuno, J., Yarzebinski, E., Madaio, M., Maheshwari., N., Koedinger, K., & Ogan, A. (2018). Designing Appropriate Learning Technologies for School vs Home Settings in Tanzanian Rural Villages. In the Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (ACM COMPASS). [pdf]
Madaio, M. & Ogan, A. (2018, April). Supporting Parent-Child Literacy Interactions with Feature Phones in Cote d’Ivoire. Presented at the HCI Across Borders Symposium at the 2018 CHI Conference. [pdf]
Madaio, M., Grinter, R. E., & Zegura, E. W. (2016, June). Experiences with MOOCs in a West-African Technology Hub. In Proceedings of the Eighth International Conference on Information and Communication Technologies and Development (p. 49). ACM. [pdf]
Zegura, E. W., Madaio, M., & Grinter, R. E. (2015, May). Beyond bootstrapping: the liberian ilab as a maturing community of practice. In Proceedings of the Seventh International Conference on Information and Communication Technologies and Development. (p. 70). ACM. [pdf]
Fire Risk Analysis
Singh Walia, B., Hu, Q., Chen, J., Chen, F., Lee, J., Kuo, N., Narang, P., Batts, J., Arnold, G., and Madaio, M. (2018). A real-time pipeline for spatio-temporal fire risk prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD) (in press). [pdf]
Metro21: Smart Cities Initiative (2018). Predictive Modeling of Building Fire Risk: Designing and evaluating predictive models of fire risk to prioritize property fire inspections. A Metro21 Research Publication. [pdf]
Madaio, M., Shang-Tse Chen, Oliver L Haimson,Wenwen Zhang, Xiang Cheng, Hinds-Aldrich, M., Chau, D.H., and Dilkina, B. (2016). Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. 2016, pp. 185–194. [*Best Student Paper Runner-Up*] [pdf]
Madaio, M., Haimson, O. L., Zhang, W., Cheng, X., Hinds-Aldrich, M., Dilkina, B., & Chau, D. H. P. (2015). Identifying and Prioritizing Fire Inspections: A Case Study of Predicting Fire Risk in Atlanta. In Bloomberg Data for Good Exchange. [pdf]