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Interpretable Engagement Models for MOOCs Using Hinge-Loss Markov Random Fields., , , , and . IEEE Trans. Learn. Technol., 13 (1): 107-122 (2020)Understanding the Issues Surrounding COVID-19 Vaccine Roll Out via User Tweets., , , and . CSoNet, volume 13116 of Lecture Notes in Computer Science, page 197-205. Springer, (2021)Deep Latent Generative Models for Energy Disaggregation., , and . AAAI, page 850-857. AAAI Press, (2019)Learning Latent Engagement Patterns of Students in Online Courses., , , , and . AAAI, page 1272-1278. AAAI Press, (2014)Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums., , , and . ACL (1), page 74-83. The Association for Computer Linguistics, (2015)Fine-Grained Analysis of Cyberbullying Using Weakly-Supervised Topic Models., and . DSAA, page 504-513. IEEE, (2018)SWIFT: A non-emergency response prediction system using sparse Gaussian Conditional Random Fields., , , and . Pervasive Mob. Comput., (2021)Understanding the Societal Disruption due to COVID-19 via User Tweets., , and . SMARTCOMP, page 137-144. IEEE, (2021)Mobility-aware COVID-19 Case Prediction using Cellular Network Logs., , , , and . LCN, page 479-486. IEEE, (2021)Poster: Understanding Human Mobility during COVID-19 using Cellular Network Traffic., , , , , , and . Networking, page 1-3. IEEE, (2021)