Who I Am
I am currently a PhD student in the Human-Computer Interaction Institute at Carnegie Mellon University, working in the ArticuLab, where I am advised by Justine Cassell and Amy Ogan.
In my free time, I am the PI for a Metro21 research project on fire risk prediction 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 Science, and Machine Learning, where I study human behavior in order to design AI 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 natural language processing to understand how interpersonal social factors in learning can inform the design of more social AI-driven educational technologies.
At a higher level, I am also interested in how machine learning and AI systems are integrated into various aspects of civic life, from municipal decision-making to citizen hacking with open civic data.
|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!|
|Feb 2018||Our paper on help-offering and productive support in peer learning was accepted to the ICLS conference as a full paper!|
|Feb 2018||Our paper on reinforcement learning for "socially-aware task reasoning" was accepted to the AAMAS conference as a short paper!|
|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||I spoke at the EDUCAUSE conference about how we use Google Cloud Platform and TensorFlow in our "socially-aware" AI research.|
|Nov 2017||We published an article in the International Journal of Computer-Supported Collaborative Learning (IJCSCL), based on our CSCL best paper.|
|May 2017||Our CSCL paper won the Best Student Paper Award!|
|April 2017||Our paper on detecting interpersonal rapport using machine learning was accepted to the Educational Data Mining (EDM) 2017 conference.|
Socially-Aware Educational Technologies
Madaio, M., Peng, K., Ogan, A., & Cassell, J. (in press). 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).
Zhao, Z., Madaio, M., Pecune, F., Matsuyama, Y., & Cassell, J. (in press). Socially-Conditioned Task Reasoning for a Virtual Tutoring Agent. In Proceedings of the 17th International Conference of Autonomous Agents and Multi-Agent Systems (AAMAS).
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
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
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. “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]