Nikhil Muralidhar

Research output

Research output per year 2016 2022 2024 2024

• 17 Conference contribution
• 8 Article
• 1 Conference article

Research output per year

Research output per year

Search results

• 2024

  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

  • Long Short-Term Memory 100%
  • Deep Learning Method 100%
  • Mode Dynamic 100%
  • Neural Network 50%
  • Heat Exchanger 50%

• 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

  Open Access

  • Wireless System 100%
  • Artificial Intelligence 100%
  • Large Language Model 75%
  • Natural Language Processing 50%
  • Mathematical Reasoning 50%
  2 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

  • Language Modeling 100%
  • De Facto Standard 100%
  • Pre-Trained Language Models 100%
  • Priming 100%
  • Numeracy 100%

• NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication

  Faysal, A., Rostami, M., Roshan, R. G., Wang, H. & Muralidhar, N., 2024, 2024 33rd Wireless and Optical Communications Conference, WOCC 2024. p. 103-108 6 p. (2024 33rd Wireless and Optical Communications Conference, WOCC 2024).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Wireless Communication 100%
  • Base Classifier 100%
  • Noise-to-Signal Ratio 75%
  • Class Distribution 50%
  • Computational Complexity 25%

• Physics informed deep learning for flow and force predictions in dense ellipsoidal particle suspensions

  Ashwin, N. R., Tafti, D., Muralidhar, N. & Cao, Z., 15 Apr 2024, In: Powder Technology. 439, 119684.

  Research output: Contribution to journal › Article › peer-review

  • Drag Force 100%
  • Particle Suspension 100%
  • Deep Learning Method 100%
  • Velocity Field 66%
  • Pressure Distribution 66%
  2 Scopus citations

• Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation

  Srikishan, B., Tabassum, A., Allu, S., Kannan, R. & Muralidhar, N., 25 Mar 2024, In: Proceedings of the AAAI Conference on Artificial Intelligence. 38, 13, p. 15066-15074 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  • Reinforcement Learning 100%
  • Large Data Set 100%
  • Image Segmentation 100%
  • Case Study 100%
  • Computational Cost 100%
  1 Scopus citations

• Title evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

  Mathis, S. M., Webber, A. E., León, T. M., Murray, E. L., Sun, M., White, L. A., Brooks, L. C., Green, A., Hu, A. J., Rosenfeld, R., Shemetov, D., Tibshirani, R. J., McDonald, D. J., Kandula, S., Pei, S., Yaari, R., Yamana, T. K., Shaman, J., Agarwal, P. & Balusu, S. & 90 others, Gururajan, G., Kamarthi, H., Prakash, B. A., Raman, R., Zhao, Z., Rodríguez, A., Meiyappan, A., Omar, S., Baccam, P., Gurung, H. L., Suchoski, B. T., Stage, S. A., Ajelli, M., Kummer, A. G., Litvinova, M., Ventura, P. C., Wadsworth, S., Niemi, J., Carcelen, E., Hill, A. L., Loo, S. L., McKee, C. D., Sato, K., Smith, C., Truelove, S., Jung, S. M., Lemaitre, J. C., Lessler, J., McAndrew, T., Ye, W., Bosse, N., Hlavacek, W. S., Lin, Y. T., Mallela, A., Gibson, G. C., Chen, Y., Lamm, S. M., Lee, J., Posner, R. G., Perofsky, A. C., Viboud, C., Clemente, L., Lu, F., Meyer, A. G., Santillana, M., Chinazzi, M., Davis, J. T., Mu, K., Pastore y Piontti, A., Vespignani, A., Xiong, X., Ben-Nun, M., Riley, P., Turtle, J., Hulme-Lowe, C., Jessa, S., Nagraj, V. P., Turner, S. D., Williams, D., Basu, A., Drake, J. M., Fox, S. J., Suez, E., Cojocaru, M. G., Thommes, E. W., Cramer, E. Y., Gerding, A., Stark, A., Ray, E. L., Reich, N. G., Shandross, L., Wattanachit, N., Wang, Y., Zorn, M. W., Aawar, M. A., Srivastava, A., Meyers, L. A., Adiga, A., Hurt, B., Kaur, G., Lewis, B. L., Marathe, M., Venkatramanan, S., Butler, P., Farabow, A., Ramakrishnan, N., Muralidhar, N., Reed, C., Biggerstaff, M. & Borchering, R. K., Dec 2024, In: Nature Communications. 15, 1, 6289.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Accurate Forecast 100%
  • Baseline Model 100%
  • Component Model 100%
  • Seasonal Influenza 100%
  • Nutrition Policy 100%
  7 Scopus citations
• 2023

  Comparison of reduced order models based on dynamic mode decomposition and deep learning for predicting chaotic flow in a random arrangement of cylinders

  Raj, N. A., Tafti, D. & Muralidhar, N., 1 Jul 2023, In: Physics of Fluids. 35, 7, 073330.

  Research output: Contribution to journal › Article › peer-review

  • Reduced Order Model 100%
  • Deep Learning 100%
  • Ground Truth 60%
  • Drag Force 60%
  • Neural Network 25%
  6 Scopus citations

• Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency

  Sharma, M., Muralidhar, N. & Ramakrishnan, N., 2023, Long Papers. p. 6178-6191 14 p. (Proceedings of the Annual Meeting of the Association for Computational Linguistics; vol. 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • linguistic skills 100%
  • linguistic proficiency 100%
  • Reasoning 100%
  • Linguistics 100%
  • Occupational Qualification 40%
  1 Scopus citations

• ML-Assisted Optimization of Securities Lending

  Prasad, A., Arunachalam, P., Motamedi, A., Bhattacharya, R., Liu, B., McCormick, H., Xu, S., Muralidhar, N. & Ramakrishnan, N., 27 Nov 2023, ICAIF 2023 - 4th ACM International Conference on AI in Finance. p. 628-636 9 p. (ICAIF 2023 - 4th ACM International Conference on AI in Finance).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Machine Learning 100%
  • Seq2Seq 100%
  • Modeling Framework 50%
• 2022

  Deep learning methods for predicting fluid forces in dense particle suspensions

  Ashwin, N. R., Cao, Z., Muralidhar, N., Tafti, D. & Karpatne, A., Mar 2022, In: Powder Technology. 401, 117303.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Networks 100%
  • Deep Learning 100%
  • Convolutional Neural Network 100%
  • Multilayer Perceptron 100%
  • Neural Network 100%
  18 Scopus citations

• Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

  Rathinavel, G., Muralidhar, N., O'Shea, T. & Ramakrishnan, N., 2022, Proceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022. Zhu, X., Ranka, S., Thai, M. T., Washio, T. & Wu, X. (eds.). p. 1161-1166 6 p. (Proceedings - IEEE International Conference on Data Mining, ICDM; vol. 2022-November).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Network Activity 100%
  • Anomaly Detection 100%
  • Contrastive Learning 50%
  • Wireless Networks 25%
  • Roles 25%
  1 Scopus citations

• Efficient Generative Wireless Anomaly Detection for Next Generation Networks

  Rathinavel, G., Muralidhar, N., Ramakrishnan, N. & Oshea, T., 2022, MILCOM 2022 - 2022 IEEE Military Communications Conference. p. 594-599 6 p. (Proceedings - IEEE Military Communications Conference MILCOM; vol. 2022-November).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Anomaly Detection 100%
  • Generation Network 100%
  • Quality of Service 33%
  • Access Schemes 33%
  • Deep Learning Technique 33%
  2 Scopus citations

• MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

  Tabassum, A., Muralidhar, N., Kannan, R. & Allu, S., 2022, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Tsumoto, S., Ohsawa, Y., Chen, L., Van den Poel, D., Hu, X., Motomura, Y., Takagi, T., Wu, L., Xie, Y., Abe, A. & Raghavan, V. (eds.). p. 1936-1941 6 p. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Prediction 100%
  • Battery (Electrochemical Energy Engineering) 100%
  • Material 100%
  • Lithium Ion Battery 100%
  • Prediction Phase 100%
  2 Scopus citations
• 2021

  Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores

  Muralidhar, N., Zubair, A., Weidler, N., Gerdes, R. & Ramakrishnan, N., 2021, Proceedings of the 2021 IEEE International Symposium on Hardware Oriented Security and Trust, HOST 2021. p. 181-191 11 p. (Proceedings of the 2021 IEEE International Symposium on Hardware Oriented Security and Trust, HOST 2021).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Detection 100%
  • Hardware 100%
  • Graph Convolutional Network 100%
  • Design 40%
  • Models 40%
  26 Scopus citations

• PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields

  Muralidhar, N., Bu, J., Cao, Z., Raj, N., Ramakrishnan, N., Tafti, D. & Karpatne, A., 2021, Proceedings - 21st IEEE International Conference on Data Mining, ICDM 2021. Bailey, J., Miettinen, P., Koh, Y. S., Tao, D. & Wu, X. (eds.). p. 1246-1251 6 p. (Proceedings - IEEE International Conference on Data Mining, ICDM; vol. 2021-December).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Flow Distribution 100%
  • Deep Learning 100%
  • Physics 100%
  • Fluid Flow 100%
  • Computational Fluid Dynamics 75%
  5 Scopus citations

• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

  Rodríguez, A., Muralidhar, N., Adhikari, B., Tabassum, A., Ramakrishnan, N. & Prakash, B. A., 2021, 35th AAAI Conference on Artificial Intelligence, AAAI 2021. p. 4855-4863 9 p. (35th AAAI Conference on Artificial Intelligence, AAAI 2021; vol. 6A).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Forecasting Model 100%
  • Models 100%
  • Adaptation 66%
  • Research 33%
  • Healthcare 33%
  16 Scopus citations
• 2020

  Cut-n-Reveal: Time Series Segmentations with Explanations

  Muralidhar, N., Tabassum, A., Chen, L., Chinthavali, S., Ramakrishnan, N. & Prakash, B. A., Sep 2020, In: ACM Transactions on Intelligent Systems and Technology. 11, 5, 53.

  Research output: Contribution to journal › Article › peer-review

  • Infrastructure System 100%
  • Critical Infrastructure 100%
  • Series Segmentation 100%
  • Optimization Formulation 50%
  • Temporal Correlation 50%
  3 Scopus citations

• Phynet: Physics guided neural networks for particle drag force prediction in assembly

  Muralidhar, N., Bu, J., Cao, Z., He, L., Ramakrishnan, N., Tafti, D. & Karpatne, A., 2020, Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020. Demeniconi, C. & Chawla, N. (eds.). p. 559-567 9 p. (Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • Models 100%
  • Simulation 80%
  • Machine Learning 40%
  • Learning Process 40%
  • Computational Cost 20%
  38 Scopus citations

• Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems

  Muralidhar, N., Bu, J., Cao, Z., He, L., Ramakrishnan, N., Tafti, D. & Karpatne, A., 1 Oct 2020, In: Big Data. 8, 5, p. 431-449 19 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  • Models 100%
  • Deep Learning 100%
  • Simulation 80%
  • Machine Learning 40%
  • Learning Process 40%
  36 Scopus citations
• 2019

  Detection of false data injection attacks in cyber-physical systems using dynamic invariants

  Nakayama, K., Muralidhar, N., Jin, C. & Sharma, R., Dec 2019, Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. Wani, M. A., Khoshgoftaar, T. M., Wang, D., Wang, H. & Seliya, N. (eds.). p. 1023-1030 8 p. 8999069. (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Detection 100%
  • Cyber Physical Systems 100%
  • False Data Injection Attacks 100%
  • Attack 60%
  • Kalman Filtering 60%
  2 Scopus citations

• Dyat nets: Dynamic attention networks for state forecasting in cyber-physical systems

  Muralidhar, N., Muthiah, S. & Ramakrishnan, N., 2019, Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. Kraus, S. (ed.). p. 3180-3186 7 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2019-August).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  Open Access

  • State 100%
  • Attention 100%
  • Forecasting 100%
  • Novels 22%
  • Performance 22%
  17 Scopus citations

• Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach

  Muralidhar, N., Muthiah, S., Nakayama, K., Sharma, R. & Ramakrishnan, N., Dec 2019, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Baru, C., Huan, J., Khan, L., Hu, X. T., Ak, R., Tian, Y., Barga, R., Zaniolo, C., Lee, K. & Ye, Y. F. (eds.). p. 543-552 10 p. 9005511. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Cyber Physical Systems 100%
  • Sparsity 14%
  • Attention (Machine Learning) 14%
  • Analytic Model 14%
  • Training Model 14%
  8 Scopus citations
• 2018

  Illiad: InteLLigent invariant and anomaly detection in cyber-physical systems

  Muralidhar, N., Wang, C., Self, N., Momtazpour, M., Nakayama, K., Sharma, R. & Ramakrishnan, N., Feb 2018, In: ACM Transactions on Intelligent Systems and Technology. 9, 3, 35.

  Research output: Contribution to journal › Article › peer-review

  • Cyber Physical Systems 100%
  • Anomaly Detection 100%
  • Application 60%
  • Relationships 40%
  • Models 40%
  15 Scopus citations

• Incorporating Prior Domain Knowledge into Deep Neural Networks

  Muralidhar, N., Islam, M. R., Marwah, M., Karpatne, A. & Ramakrishnan, N., 2 Jul 2018, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Abe, N., Liu, H., Pu, C., Hu, X., Ahmed, N., Qiao, M., Song, Y., Kossmann, D., Liu, B., Lee, K., Tang, J., He, J. & Saltz, J. (eds.). p. 36-45 10 p. 8621955. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Domain Knowledge 100%
  • Deep Neural Network 100%
  • Domains 75%
  • Performance Improvement 50%
  • Models 25%
  125 Scopus citations
• 2016

  Recommending temporally relevant news content from implicit feedback data

  Muralidhar, N., Rangwala, H. & Han, E. H. S., 4 Jan 2016, Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015. p. 689-696 8 p. 7372200. (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; vol. 2016-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • User Engagement 100%
  • Explicit Information 50%
  • Recommendation Algorithm 50%
  • Highly Engaged User 50%
  • Digital Format 50%
  8 Scopus citations