Who I Am
I am currently a PhD candidate in the Human-Computer Interaction Institute at Carnegie Mellon University, where I am advised by Amy Ogan and Justine Cassell.
I am a Siebel Scholar and a fellow in the PIER Program in Interdisciplinary Education Research. I have previously worked with the FATE (Fairness, Accountability, Transparency, and Ethics in AI) group at Microsoft Research, and I was a research intern with the United Nations University, Institute on Computing and Society.
Before coming to CMU, I completed an M.S. in the Digital Media program at Georgia Tech, advised by Ian Bogost, and prior to that I was a public high school teacher in Maryland.
What I Do
I work at the intersection of human-computer interaction, machine learning, and public interest technology, where I use human-centered methods to understand how we might equitably co-design data-driven technologies in the public interest with multiple groups of stakeholders.
One strand of my work focuses on the implications of how AI/ML systems are deployed in cities and communities to inform high-stakes civic decision-making, while ensuring they are equitable, fair, and accountable to the public.
In another strand of my work, I focus on the use of data-driven systems to supplement gaps in public sector service delivery, focusing primarily on education, focusing primarily on educational applications in low-resource communities.
|Fall 2019||I'm on the academic job market! For now, I'm primarily looking for tenure-track positions in interdisciplinary departments, but I'm open to different opportunities! If you see a good fit, feel free to reach out!|
|August 2019||I was named a Siebel Scholars fellow! Very honored and proud to join this prestigious group of scholars!|
|July 2019||We won the best paper award for our paper on an early deployment of Allo Alphabet, our voice-based early literacy system, at the ACM Compass conference!|
|June 2019||Our paper outlining a research agenda for ethics and equity in NLP systems in education was presented at the ACL Workshop on Innovative Use of NLP for Building Educational Applications.|
|May 2019||I'm presenting our paper on our qualitative design work co-designing an early literacy system with families in Côte d'Ivoire at the CHI conference in Glasgow!|
|March 2019||I'm excited to be interning this summer at Microsoft Research, in their Fairness, Accountability, Transparency, and Ethics in AI (FATE) research group, working with Hanna Wallach and Jenn Wortman Vaughan!|
|January 2019||I successfully proposed my thesis work on co-designing an early literacy technology with families in low-resource contexts!|
|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!|
|March 2018||Pittsburgh's Mayor Peduto announced the launch of our Metro21 team's fire risk prediction tool at a press conference we held 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.|
Madaio, M., Kamath, V., Yarzebinski, E., Zasacky, S., Tanoh, F., Hannon-Cropp, J., Cassell, J., Jasinska, K. & Ogan, A. (2019). "You Give a Little of Yourself": Family Support for Children’s Use of an IVR Literacy System. In the Proceedings of the 2019 ACM SIGCAS Conference on Computing and Sustainable Societies (ACM COMPASS). [pdf]
Best Paper Award
Madaio, M., Tanoh, F., Blahoua Seri, A., Jasinska, K. & Ogan, A. (2019). "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 (CHI). [pdf]
Baker, R., Ogan, A., Madaio, M., Walker, E. (2019). Culture in Computer-Based Learning Systems: Challenges and Opportunities. In In Computer-Based Learning in Context, 1(1), 1-13. 2019. [pdf]
Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Roman, E., Black, E. (2019). Equity Beyond Bias in Language Technologies for Education. In 14th Workshop on Innovative Use of NLP for Building Educational Applications, at ACL 2019. [pdf]
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]
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]
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., 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. [pdf] Best Paper Award; Best Student Paper Nominee
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]
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. [pdf] Best Student Paper
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., 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. [pdf] Best Student Paper, Runner-Up
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]