Muralidhar, N., Zubair, A., Weidler, N., Gerdes, R., & Ramakrishnan, N. Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores. In Proceedings of the 14th IEEE International Symposium on Hardware-Oriented Security and Trust (IEEE HOST) 2021.
Muralidhar, N., Bu, J., Cao, Z., Raj, N., Ramakrishnan, N., Tafti, D., & Karpatne, A., “Phyflow: Physics-guided deep learning for generating interpretable 3D flow fields,” In Proceedings of the 21st IEEE International Conference on Data Mining (ICDM), IEEE, 2021.
Rodriguez, A.*,Muralidhar, N.,*, Adhikari, B., Tabassum, A., Ramakrishnan, N., & Prakash B.A. Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19. AAAI 2021
Rodriguez, A.*,Muralidhar, N.,*, Adhikari, B., Tabassum, A., Ramakrishnan, N., & Prakash B.A. Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19. NeurIPS Machine Learning in Public Health (MLPH) Workshop, Virtual Event. December 2020
Muralidhar, N., Mayer, B., Self, N., Kondoju, P., Tonshal, B., Schmotzer, J., & Ramakrishnan,N. RAD: Rapid Automobile Data Analytics Framework for Structured Data. In Proceedings of the 9th SIGKDD International Workshop on Urban Computing (UrbComp 2020).
Muralidhar, N., Bu, J., Cao, Z., He, L., Ramakrishnan, N., Tafti, D., & Karpatne, A. (2020).Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems. BigData, 8(5), 431-449.(Impact Factor: 3.644)
Muralidhar, N., Tabassum, A., Chen, L., Chinthavali, S., Ramakrishnan, N., & Prakash, B. A. (2020). Cut-n-Reveal: Time Series Segmentations with Explanations. ACM Transactions on Intelligent Systems and Technology (TIST), 11(5), 1-26. (Impact Factor: 3.971)
Muralidhar, N., Bu, J., Cao, Z., He, L., Ramakrishnan, N., Tafti, D., & Karpatne, A. "Physics Guided Learning for Particle Drag Force Estimation in Multi Phase Fluid Flow." Proceedings of the 3rd Workshop on Physics Informed Machine Learning 2020.
Muralidhar, N., Bu, J., Cao, Z., He, L., Ramakrishnan, N., Tafti, D., & Karpatne, A. (2020). PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly. In Proceedings of the 2020 SIAM International Conference on Data Mining (pp. 559-567). Society for Industrial and Applied Mathematics. [CODE]
Muralidhar, N., Muthiah, S., Nakayama, K., Sharma, R., & Ramakrishnan, N. (2019, December). Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to SequenceApproach. In 2019 IEEE International Conference on Big Data (IEEE Big Data) (pp. 543-552).
Muralidhar, N., Muthiah, S., & Ramakrishnan, N. (2019). DyAt Nets:Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 3180–3186. https://doi.org/10.24963/ijcai.2019/441 [CODE]
Muralidhar, N., Islam, M.R., Marwah, M., Karpatne, A., & Ramakrishnan, N. (2018) Incorporating Prior Domain Knowledge into Deep Neural Networks. In 2018 IEEE International Conference on Big Data (Big Data), pp. 36-45. IEEE, 2018.
Muralidhar, N., Wang, C., Self, N., Momtazpour M., Nakayama, K., Sharma, R., & Ramakrishnan, N. (2018) illiad: Intelligent invariant and anomaly detection in cyber-physical systems. ACM Transactions on Intelligent Systems and Technology (TIST) 9, no. 3 (2018): 35. (Impact Factor: 3.971)
Muralidhar, N., Rangwala H., & Han E.H. Recommending Temporally Relevant News Content from Implicit Feedback Data. In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 689-696. IEEE, 2015.