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 an Assistant Director of the CMU Students for Urban Data Systems, as well as the project lead for a Metro21 project on fire risk analysis for the Pittsburgh Bureau of Fire.

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 collaboration in order to design AI systems that can collaborate effectively with humans.

In my main research, I focus on educational applications, using methods from machine learning and natural language processing to understand how social factors in learning can be implemented into AI-driven educational technology.

At a higher level, I am also interested in how machine learning and AI systems are integrated into various levels of civic life, from municipal decision-making to citizen hacking with open civic data.

Recent News

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.
Feb 2017 Our paper studying peer tutors' use of feedback was accepted to the Computer-Supported Collaborative Learning (CSCL) conference.
Jan 2017 I published an article in the Spark Creative Teaching and Learning Journal on student data privacy.


Nov 2017 EDUCAUSE - Philadelphia, PA
July 2017 EDM - Wuhan, China
June 2017 CSCL - Philadelphia, PA
Sept 2016 Design of eLearning - The New School, NY
Aug 2016 KDD - San Francisco, CA
June 2016 ITS - Zagreb, Croatia
June 2016 ICTD - Ann Arbor, MI

Selected Publications

Socially-Aware Educational Technologies

Madaio, M., 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]

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]

Yu, H., Gui, L., Madaio, M., Ogan, A., & Cassell, J. (2017). Temporally Selective Attention Model for Social and Affective State Recognition in Multimedia Content. In Association for Computing Machinery Conference on Multimedia, 2017. [pdf]

Educational Technologies for International Development

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]