Vivek Sivaraman Narayanaswamy

2025 1 publication

• Thopalli, K., Narayanaswamy, V., & Thiagarajan, J. J. (2025): “Group Conformal Prediction.” In Uncertainty Quantification for Computer Vision Workshop (UnCV 2025) at CVPR 2025. (Accepted workshop paper proposing a conformal prediction approach for group-conditioned uncertainty calibration in computer vision.)

2024 4 publications

• Narayanaswamy, V., Thopalli, K., Anirudh, R., Mubarka, Y., Sakla, S., & Thiagarajan, J. J. (2024): “On the Use of Anchoring for Training Vision Models.” In Advances in Neural Information Processing Systems (NeurIPS 2024). (Spotlight presentation; proposes an anchoring technique to improve vision model training stability and robustness.)
• Narayanaswamy, V., Thopalli, K., Subramanyam, R., & Thiagarajan, J. J. (2024): “DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation.” In European Conference on Computer Vision (ECCV 2024). (Introduces a method using foundation model priors to better detect and explain failures in vision models.)
• Thiagarajan, J. J., Narayanaswamy, V., Trivedi, P., & Anirudh, R. (2024): “PAGER: A Framework for Failure Analysis of Deep Regression Models.” In International Conference on Machine Learning (ICML 2024). (Develops a framework for analyzing and understanding failures in deep regression models.)
• Narayanaswamy, V., Anirudh, R., & Thiagarajan, J. J. (2024): “The Double-Edged Sword of AI Safety: Balancing Anomaly Detection and OOD Generalization via Model Anchoring.” In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024). (Examines how an anchoring-based approach can improve anomaly detection without sacrificing generalization to out-of-distribution data.)

2023 2 publications

• Narayanaswamy, V., Ayyanar, R., Tepedelenlioglu, C., Srinivasan, D., & Spanias, A. (2023): “Optimizing Solar Power Using Array Topology Reconfiguration With Regularized Deep Neural Networks.” IEEE Access, 11, 7461–7470. (Journal article presenting a deep learning approach for reconfiguring solar array topology to maximize power output under varying conditions.)
• Narayanaswamy, V., Mubarka, Y., Anirudh, R., Rajan, D., Spanias, A., & Thiagarajan, J. J. (2023): “Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors.” In Medical Imaging with Deep Learning (MIDL 2023). (Presents a data augmentation strategy to construct inliers/outliers that significantly improves out-of-distribution detection in medical image classifiers.)

2022 1 publication

• Thiagarajan, J. J., Anirudh, R., Narayanaswamy, V., & Bremer, P. T. (2022): “Single Model Uncertainty Estimation via Stochastic Data Centering.” In Advances in Neural Information Processing Systems (NeurIPS 2022). (Introduces a technique for estimating prediction uncertainty from a single trained model by using stochastic alterations of input data.)

2021 4 publications

• Rao, S., Narayanaswamy, V., Esposito, M., Thiagarajan, J. J., & Spanias, A. (2021): “COVID-19 detection using cough sound analysis and deep learning algorithms.” Intelligent Decision Technologies, 15(4), 655–665. (Demonstrates a machine learning framework for detecting COVID-19 from cough audio, including hyperparameter tuning for robust performance.)
• Sattigeri, P., Thiagarajan, J. J., Ramamurthy, K., Spanias, A., Banavar, M., Dixit, A., Fan, J., Rao, S., Shanthamallu, U. S., Narayanaswamy, V., Katoch, S. (2021): “Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors.” International Journal of Virtual and Personal Learning Environments (IJVPLE), 12(1), Article 12 (July 2021), 15 pages. (Describes educational tools integrating DSP, machine learning, and sensor data for remote learning.)
• Thiagarajan, J. J., Narayanaswamy, V., Rajan, D., Liang, J., Chaudhari, A., & Spanias, A. (2021):“Designing counterfactual generators using deep model inversion.” In Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pp. 16873–16884. (Develops a technique to generate counterfactual examples for model interpretability by inverting deep models.)
• Thiagarajan, J. J., Anirudh, R., Narayanaswamy, V., & Bremer, P. T. (2021): “Accurate and robust feature importance estimation under distribution shifts.” In Proceedings of the AAAI Conference on Artificial Intelligence 35(9), pp. 7891–7898 (AAAI 2021). (Proposes a robust method for estimating feature importance that remains reliable even when data distribution shifts from training to deployment.)

2017 1 publication

• Rajkumar, S., Sivaraman, N. V., Murali, S., & Selvan, K. T. (2017): “Heptaband swastik arm antenna for MIMO applications.” IET Microwaves, Antennas & Propagation, 11(9), 1255–1261. (Early work on antenna design leveraging novel multi-band configurations.)