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.
I am also the PI for a Metro21 Smart Cities Institute civic machine learning research project, as well as an Assistant Director of the CMU data science for social good organization, SUDS.
Before coming to CMU, I completed an 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, Machine Learning, Technology for Development (ICTD), and the Learning Sciences, where I study human cognition and behavior in order to understand and design for human-AI collaborative systems, focused primarily on educational and civic applications.
In one strand of this work, my research focuses on the use of AI systems in education, from multimodal machine learning used to contribute to fundamental learning science research, to machine learning driven adaptive educational technologies, to natural language dialogue systems.
Another strand of my research focuses on the implications of how ML and AI systems are deployed in cities and communities to inform high-stakes civic decision-making. I am interested in how these systems are designed for and with multiple stakeholders in the community, and how to ensure they are equitable, fair, and accountable to the public.
|December 2018||Our team presented our short paper on a longitudinal evaluation of our deployed fire risk model at the AI for Social Good workshop at the NeurIPS conference in Montreal!|
|November 2018||Our research team's paper on co-designing an early literacy system with family and community stakeholders was accepted to the 2019 ACM CHI Conference on Human Factors in Computing Systems!|
|July 2018||I spoke on a panel at the Google Cloud NEXT event, about our work using machine learning to design educational AI, informed by theories of learning.|
|June 2018||Our paper won the Best Paper Award at the International Conference of the Learning Sciences (and was nominated for Best Student Paper)!|
|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!|
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., Lasko, R., 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]
Information-Communication Technologies for Development
Madaio, M., Tanoh, F., Blahoua Seri, A., Jasinska, K. & Ogan, A. (2019, to appear). "Everyone Brings Their Grain of Salt": Designing for Low-Literate Parental Engagement with a Mobile Literacy Technology in Côte d'Ivoire. Accepted to the 2019 ACM CHI Conference on Human Factors in Computing Systems.[pre-print draft available on request]
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]
Civic Machine Learning
Lee, J., Lin, Y., and Madaio, M. (2018). A Longitudinal Evaluation of a Deployed Fire Risk Model. In the AI for Social Good Workshop at the Neural Information Processing System Conference. (NeurIPS 2018). [pdf]
Madaio, M., Martin, S.E. (2018). Who owns the Smart City? Towards an Ethical Framework for Civic AI. In Ethics in Design and Technology. Bloomsbury Academic (in press, draft available on request).
Singh Walia, B., Hu, Q., Chen, J., Chen, F., Lee, J., Kuo, N., Narang, P., Batts, J., Arnold, G., and Madaio, M. (2018). A dynamic pipeline for spatio-temporal fire risk prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD). [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.
Singh Walia, B., Hu, Q., Chen, J., Chen, F., Lee, J., Kuo, N., Narang, P., Batts, J., Arnold, G., and Madaio, M. (2018). A dynamic pipeline for spatio-temporal fire risk prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD). [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]