Research output per year
Research output per year
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Dive into the research topics where Nikhil Muralidhar is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
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DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
Faysal, A., Boushine, T., Rostami, M., Roshan, R. G., Wang, H., Muralidhar, N., Sahoo, A. & Yao, Y. D., 2025, In: IEEE Communications Letters. 29, 7, p. 1659-1663 5 p.Research output: Contribution to journal › Article › peer-review
1 Scopus citations -
Comparative Study of Future State Predictions of Unsteady Multiphase Flows Using DMD and Deep Learning
Raj, N. A., Tafti, D., Muralidhar, N. & Karpatne, A., 2024, Fluid Mechanics and Fluid Power, Volume 4 - Select Proceedings of FMFP 2022. Singh, K. M., Dutta, S., Subudhi, S. & Singh, N. K. (eds.). p. 923-935 13 p. (Lecture Notes in Mechanical Engineering).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway
Tabassum, A., Allu, S., Kannan, R. & Muralidhar, N., 2024, Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024. Ding, W., Lu, C.-T., Wang, F., Di, L., Wu, K., Huan, J., Nambiar, R., Li, J., Ilievski, F., Baeza-Yates, R. & Hu, X. (eds.). p. 2224-2233 10 p. (Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Xu, S., Kurisummoottil Thomas, C., Hashash, O., Muralidhar, N., Saad, W. & Ramakrishnan, N., 2024, In: IEEE Network. 38, 5, p. 10-20 11 p.Research output: Contribution to journal › Article › peer-review
18 Scopus citations -
Laying Anchors: Semantically Priming Numerals in Language Modeling
Sharma, M., Taware, R. M., Koirala, P., Muralidhar, N. & Ramakrishnan, N., 2024, Findings of the Association for Computational Linguistics: NAACL 2024 - Findings. Duh, K., Gomez, H. & Bethard, S. (eds.). p. 2653-2660 8 p. (Findings of the Association for Computational Linguistics: NAACL 2024 - Findings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review