About Me

I am an Assistant Professor in the Department of Computer Science at the Oklahoma State University

I completed my Ph.D. in June 2019 from the Department of Computer Science at the University of North Carolina at Charlotte

I received my undergraduate degree from the Sri Krishna College of Engineering and Technology in April 2014

Research Interests

I work on algorithms to study dynamics of conversations in online social media like Twitter, Gab, and Reddit. Particularly, my work involve applying deep learning models to engineer features from multiple user interactions in the social media. Currently, my group work with the combination of NLP and network models to forecast the dynamics of users conversations - how it is going to evolve, how it is going to affect the community, and how user role and influence plays a vital role in such dynamics. We also study information diffusion between multiple social media platforms.

I am also broadly interested in recommender's and decision support systems. In my Ph.D. thesis, I created scalable frameworks to extract actionable recommendations from massive data sources. To validate my frameworks, I used applications like increasing the profit of business and reducing hospital readmission. Currently, I work on methods to generate recommendations from user interactions and their polarity towards such interactions.

Following research domains collaborate in my works:

• Data mining
• Network science
• Social network analysis
• Machine learning
• Computational social science
• Cloud computing
• Natural language processing

Latest Publications

2020

• Ridenhour, M., bagavathi, A., Raisi, E., and Krishnan, S. "Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models" (to appear) in International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), 2020


• Safarnejad, L., Xu, Q., Ge, Y., Bagavathi, A., Krishnan, S., Chen, S. "Identifying Influential Factors on Discussion Dynamics of Emerging Health Issues on Social Media: A Computational Study", (to appear) in JMIR Public Health and Surveillance, 2020 [Pre-print]


• Tzacheva, A. A., Ranganathan, J., and Bagavathi, A. "Action Rules for Sentiment Analysis", in International Journal of Social Network Mining, Vol. 3, No. 1, pp. 35-51, 2020


• Tzacheva, A.A., Shankar, R.A., Ramachandran, S., Bagavathi, A., "Action Rules of Lowest Cost and Action Set Correlations", in Fundamenta Informaticae journal, Volume 172, pp. 1-14, 2020



2019

• Bagavathi, A., Krishnan, S., Subrahmanyan, S., Narasimhan, S. L., "ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert", in IEEE 18th International Conference on Machine Learning and Applications, Boca Raton, FL, December 16-19, 2019, pp. 1517-1522 [Paper]


• Wooley, S., Edmonds, A., Bagavathi, A., Krishnan, S., "Extracting Cryptocurrency Price Movements from the Reddit Community Network", (to appear) in IEEE 18th International Conference on Machine Learning and Applications, Boca Raton, Florida, December 16-19, 2019, pp. 500-505 (Acceptance Rate: 18.6%)


• Bagavathi, A., Bashiri, P., Reid, S., Phillips, M., Krishnan, S., "Examining Untempered Social Media: Analyzing Cascades of Polarized Conversations", (to appear) in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Vancouver, BC, Canada, August 27-30, 2019, pp. 625-632 (Acceptance Rate: <20%) [Paper]


• Bagavathi, A., Tzacheva, A. A., "Scalable Action Mining for Recommendations to Reduce Hospital Readmission", in IEEE 20th International Conference on Information Reuse and Integration for Data Science, Los Angeles, CA, USA, July 30-August 1, 2019, pp. 159-166 (Acceptance Rate: 23.1%) [Paper]



2018

• Bagavathi, A., Krishnan ,S., "Multi-Net: A Scalable Multiplex Network Embedding Framework", in 7th International Conference on Complex Networks and Their Applications, Cambridge, UK, Dec. 11-13, 2018, pp. 119-131 (Acceptance rate: < 20%) [Paper]


• Bagavathi, A., Tripathi A., Tzacheva A.A., Ras, Z.W., "Actionable Pattern Mining - a Scalable Data Distribution Method based on information granules", in IEEE 17th International Conference on Machine Learning and Applications, Orlando, Florida, Dec. 17-20, 2018, pp. 32-39 (Acceptance rate: 31%) [Paper]


• Bagavathi, A., Rao V., Tzacheva A.A., "Data Distribution Method for Scalable Actionable Pattern Mining", in International Conference on Data Science, E-learning and Information Systems, Madrid, Spain, Oct. 1-3, 2018, Article 3 [Paper]


• Tzacheva A.A., Bagavathi A., Suryanarayanaprasad C.B., "In Search of Actionable Patterns of Lowest Cost - a Scalable Action Graph Method", in IEEE 1st International Conference on Artificial Intelligence and Knowledge Engineering(AIKE), Santa Ana, CA, Sept. 26-28, 2018, pp. 119-124 [Paper]


• Chen S., Xu Q., Buchenberger J., Bagavathi A., Fair G., Shaikh, S. Krishnan S., "Investigating the Dynamics of Health Agency Response and Public Engagement during Public Health Emergency: A Case Study of CDC Tweeting Pattern during 2016 Zika Epidemic in the U.S.", in JMIR Public Health and Surveillance, Vol. 4, No. 4, e10827, 2018 [Paper]


• Bagavathi, A., Krishnan, S., "Social Sensors: Early Detection of Contagious Outbreaks in Social Media", in the 9th International Conference on Applied Human Factors and Ergonomics(AHFE), Orlando, FL, July 22-26, Springer, Cham, 2018, pp. 400-407 []


• Ranganathan, J., Irudayaraj, A.S., Bagavathi, A., Tzacheva, A.A., "Actionable Pattern Discovery for Sentiment Analysis on Twitter Data in clustered environment", Journal of Intelligent & Fuzzy Systems, Vol. 34, No. 5, 2018, pp. 2849-2863


• Tzacheva, A.A., Bagavathi, A., Datta, A.K., "In Search of Actionable Patterns of Lowest Cost – A Scalable Graph Method", in International Journal of Database Management Systems(IJDMS), 2018, Vol. 10, No. 3, pp. 1-19 [Paper]



Previous Years

All my publications are available on my Google Scholar page