In reverse chronology. For citation and full list please visit google scholar.

Dissertation

  • Explainable and Network-based Approaches for Decision-making in Emergency Management. Anika Tabassum. PhD Dissertation, Virginia Tech 2021. [pdf]
  • Dynamic Group Trip Planning Queries in Spatial Database. Anika Tabassum. BSc. Dissertation, Bangladesh University of Engineering and Technology 2016.

Pre-print

  • Explaining Neural Spike Activity for Simulated Bio-plausible Network through Deep Sequence Learning. Anika Tabassum, Shruti Kulkarni, Seung Hwan Lim, Bradley James, Felix Wang, Brad Theilman. 2023.
  • Attention for Causal Relationship Discovery from Biological Neural Dynamics. Ziyu Lu, Anika Tabassum, Shruti Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim. NeuRIPS CRL Workshop 2023. [pdf]
  • Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning. Anika Tabassum, Nikhil Muralidhar, Ramakrishnan Kannan, and Srikanth Allu. NeuRIPS AI for Science 2022. [pdf]

Peer-reviwed Journals and Conference

  • Bharat Srikishan, Anika Tabassum, Ramakrishnan Kannan, Srikanth Allu, Nikhil Muralidhar. Reinforcement Learning Prediction Cascades: A Case Study for Image Segmentation. AAAI 2024 (to appear). [pdf] [code] [slides] [video]
  • Sangkeun Lee, Supriya Chinthavali, Narayan Bhushal, Nils Stenvig, Anika Tabassum, Teja Kuruganti. Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping. IEEE Access Vol. 12, 2024. [pdf]
  • Anika Tabassum, Nikhil Muralidhar, Ramakrishna Kannan, Srikanth Allu. MatPhase: Material Phase Prediction for Li-ion Battery Reconstruction using Curriculum Learning. IEEE BigData 2022. [pdf] [code]
  • Anika Tabassum, Supriya Chinthavali, Sangkeun Lee, Bill Kay, Nils Stenvig, and B. Aditya Prakash. Efficient Contingency Analysis in Power Systems via Network Trigger Nodes. IEEE BigData 2021. [pdf] [code]
  • Anika Tabassum, Supriya Chinthavali, Varisara Tansakul, and B. Aditya Prakash. Actionable Insights in Urban Multivariate Time-series. ACM CIKM 2021. [pdf] [code]
  • Alexander Rodriguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikary, and B. Aditya Prakash. DeepCOVID: An Operational DL-driven Framework for Explainable Real-time COVID-19 Forecasting. Annual Conference on Innovative Applications of Artificial Intelligence (IAAI) 2021. [pdf] [code]
  • Alexander Rodriguez, Nikhil Muralidhar, Bijaya Adhikary, Anika Tabassum, Naren Ramakrishnan, B. Aditya Prakash. CALINET: Steering a Historical Disease Forecasting Model Under a Pandemic. AAAI 2021. [pdf] [code]
  • Nikhil Muralidhar, Anika Tabassum, Liangzhe Chen, Supriya Chinthavali, Naren Ramakrishnan, and B. Aditya Prakash. Cut-n-Reveal: Timeseries segmentations with explanations. ACM Transactions on Intelligent Systems and Technology (TIST) May 2020. [pdf] [code]
  • Sorour E. Amiri, Anika Tabassum, E. Thomas Ewing, and B. Aditya Prakash. Tracking and analyzing dynamics of news-cycles during global pandemics: a historical perspective. ACM SIGKDD Explorations Vol. 21 Issue 2 December 2019. [pdf]
  • Anika Tabassum, Sukarna Barua, Tanzima Hashem and Tasmin Chowdhury. Dynamic Group Trip Planning Queries in Spatial Databases. SSDMB 2017. [pdf]

Peer-reviwed Workshops

  • Ziyu Lu, Anika Tabassum , Shruti Kulkarni, Nathan Kutz, and Eric Shea Brown, Seung-Hwan Lim. Attention for Causal Relationship Discovery from Biological Neural Dynamics. Causal Representation Learning (CRL) Wokshop, NeuRIPS 2023. [pdf]
  • Anika Tabassum, Nikhil Muralidhar, Ramakrishnan Kannan, and Srikanth Allu. Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning. AI for Science Workshop, NeuRIPS 2022. [pdf]
  • Bill Kay, Hao Lu, Pravallika Devineni, Anika Tabassum, Supriya Chintavali, and Sangkeun Lee. Identification of Critical Infrastructure via PageRank. IEEE BigData (BTSD). 2021. [pdf]
  • Alexander Rodriguez, Nikhil Muralidhar, Bijaya Adhikary, \textbf{Anika Tabassum}, Naren Ramakrishnan, and B. Aditya Prakash. Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19. NeuRIPS Workshop on Machine Learning in Public Health (MLPH), 2020. [pdf]
  • Pravallika Devineni, Bill Kay, Hao Lu, Anika Tabassum, Supriya Chintavali, and Sangkeun Lee. Towards Quantifying Vulnerabilities in Critical Infrastructure Systems. IEEE BigData Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), 2020. [pdf]
  • Supriya Chinthavali, Varisara Tansakul, Sangkeun Lee, Anika Tabassum, JeffMunk, Jan Jakowski, Michael Starke, Teja Kuruganti, Heather Buckberry, JimLeverette. Quantification of Energy Cost Savings through Optimization and Control of Appliances within Smart Neighborhood Homes. ACM International Workshop on Urban Building Energy Sensing, (UrbSys), 2019. [pdf]
  • Anika Tabassum, Supriya Chinthavali, Sangkeun Lee, Liangzhe Chen, B. Aditya Prakash. Urban-Net: A System to Understand and Analyze Critical Infrastructure Networks for Emergency Management. ACM SIGKDD 2019. [pdf]

Invited Article

  • Supriya Chinthavali, Varisara Tansakul, Sangkeun Lee, Matthew Whitehead, Anika Tabassum, and others. COVID-19 Pandemic Ramifications on Residential Smart Homes Energy Use Load Profiles. In Proc. of Energy and Buildings, Volume 259, pp 111847, (Elsevier) 2022. [pdf]
  • Cramer, Estee Y., et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. In Proc. of the National Academy of Sciences of U.S.A. (PNAS) 2022. [pdf]
  • Anika Tabassum, Supriya Chinthavali, Liangzhe Chen, and B. Aditya Prakash. Data Mining Critical Infrastructure Systems: Models and Tools. IEEE Intelligent Informatics Bulletin, 2018. [pdf]