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.
|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|
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]