Karthikeyan Natesan Ramamurthy

Karthikeyan Natesan Ramamurthy

Publications

Journal Articles

  1. M. Yu, K. N. Ramamurthy, A. Thompson, and A. C. Lozano, “Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models,” Frontiers in Big Data, vol. 2, 2019.
  2. M. Arnold et al., “FactSheets: Increasing trust in AI services through supplier’s declarations of conformity,” IBM Journal of Research and Development, vol. 63, no. 4/5, pp. 6:1-6:13, 1 July-Sept. 2019.
  3. R. K. E. Bellamy et al., “AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias,” IBM Journal of Research and Development, vol. 63, no. 4/5, pp. 4:1-4:15, 1 July-Sept. 2019.
  4. R. K. E. Bellamy et al., “Think Your Artificial Intelligence Software Is Fair? Think Again,” in IEEE Software, vol. 36, no. 4, pp. 76-80, July-Aug. 2019. doi: 10.1109/MS.2019.2908514
  5. D. Wei, K. N. Ramamurthy, and K. R. Varshney, “Distribution preserving k-anonymity,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 2018.
  6. F. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, K. R. Varshney, “Data Pre-Processing for Discrimination Prevention: Information-Theoretic Optimization and Analysis,” IEEE Journal of Selected Topics in Signal Processing, 12(5), 1106-1119, 2018.
  7. C. Kuhlman, K. N. Ramamurthy, P. Sattigeri, A. C. Lozano, L. Cao, C. Reddy, A. Mojsilovic, and K. R. Varshney , “How to Foster Innovation: A Data-Driven Approach to Measuring Economic Competitiveness,” IBM Journal of Research and Development, 2017.
  8. K. R. Varshney, D. Wei, K. N. Ramamurthy, and A. Mojsilovi'{c}, “Data Challenges in Disease Response: The 2014 Ebola Outbreak and Beyond”, ACM Journal of Data and Information Quality, 2015.
  9. K. N. Ramamurthy, L. A. Hinnov, and A. Spanias, “Teaching Earth Signals Analysis using the Java-DSP Earth Systems Edition: Modern and Past Climate Change,” Journal of Geoscience Education, 2014.
  10. J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias, “Learning Stable Multilevel Dictionaries for Sparse Representations,” IEEE Transactions on Neural Networks and Learning Systems, 2014.
  11. J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias, “Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning,” IEEE Transactions on Image Processing, 2014.
  12. J. J. Thiagarajan, K. N. Ramamurthy, D. Rajan, A. Puri, D. Frakes, and A. Spanias, “Kernel sparse models for automated tumor segmentation,” International Journal on Artificial Intelligence Tools, 2014 (invited paper).
  13. K. N. Ramamurthy, J. J. Thiagarajan, and A. Spanias, “Recovering Non-negative and Combined Sparse Representations,” Digital Signal Processing, 2013.
  14. J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias, “Mixing matrix estimation using discriminative clustering for blind source separation,” Digital Signal Processing, 2012.
  15. J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias, “Optimality and Stability of the K-hyperline Clustering Algorithm,” Pattern Recognition Letters, 2011.
  16. K. N. Ramamurthy, J. J. Thiagarajan, P. Sattigeri, and A. Spanias, “Transform Domain Features for Ion-channel Signal Classification”, Biomedical Signal Processing and Control, 2010.

Conference Proceedings (from 2018 - see CV for the full list)

  1. D. Wei, K. N. Ramamurthy, and F. Calmon “Optimized Score Transformation for Fair Classification,” AISTATS, 2020 (to appear). paper code
  2. P. Zhao, P.-Y. Chen, Payel Das, K. N. Ramamurthy, and X. Lin, “Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness,” ICLR, 2020.
  3. M. Oh, P. Olsen, and K. N. Ramamurthy, “Crowd Counting with Decomposed Uncertainty,” AAAI, 2020.
  4. S. Subramanian, I. Baldini, S. Ravichandran, D. A. Katz-Rogozhnikov, K. N. Ramamurthy, P. Sattigeri, K. R. Varshney, A. Wang, P. Mangalath, and L. B. Kleiman, “A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications,” IAAI, 2020.
  5. M. Singh, and K. N. Ramamurthy, “Understanding racial bias in health using the Medical Expenditure Panel Survey data,” NeurIPS Workshop on Fair ML for Health, 2019 (Spotlight presentation).
  6. S. Subramanian et al., “Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies,” NeurIPS 2019 Workshop on Machine Learning for Health, 2019.
  7. N. Codella, M. Hind, K. N. Ramamurthy, M. Campbell, A. Dhurandhar, K. Varshney, D. Wei, and A. Mojsilovic, “Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning”, ICML Workshop on Human In the Loop Learning, 2019.
  8. K. N. Ramamurthy, K. R. Varshney, and K. Mody, “Topological Data Analysis of Decision Boundaries with Application to Model Selection,” ICML, 2019.
  9. P. K. Lohia, K. N. Ramamurthy, M. Bhide, D. Saha, K. R. Varshney, and R. Puri, “Bias Mitigation Post-processing for Individual and Group Fairness,” IEEE ICASSP, 2019.
  10. A. Coston, K. N. Ramamurthy, D. Wei, K. Varshney, S. Speakman, Z. Mustahsan, and S. Chakraborty, “Fair Transfer Learning with Missing Protected Attributes,” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, Jan. 2019.
  11. N. Codella, M. Hind, K. N. Ramamurthy, M. Campbell, A. Dhurandhar, K. Varshney, D. Wei, and A. Mojsilovic, “TED: Teaching AI to Explain its Decisions,” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, Jan. 2019.
  12. P. Sattigeri, S. Ghosh, A. Kumar, K. N. Ramamurthy, S. Hoffman, Y. Drissi, and I. Padhi, “Probabilistic Mixture of Model-Agnostic Meta-Learners,” Proc. NeurIPS Workshop on Bayesian Deep Learning, Dec. 2018.
  13. P. Olsen, K. N. Ramamurthy, J. Ribera, Y. Chen, A. Thompson, R. Luss, M. Tuinstra, and N. Abe, “Detecting and Counting Panicles in Sorghum Images,” Proc. IEEE DSAA, Oct. 2018.
  14. P. Olsen, K. N. Ramamurthy, J. Ribera, Y. Chen, A. Thompson, R. Luss, M. Tuinstra, and N. Abe, “Learning to Detect and Count Panicles in Sorghum Images,” Proc. KDD FEED Workshop, Aug. 2018.
  15. A. Som, K. Thopalli, K. N. Ramamurthy, V. Venkataraman, A. Shukla, and P. Turaga, “Perturbation Robust Representations of Topological Persistence Diagrams,” Proc. of European Conference on Computer Vision, Sep. 2018.
  16. W. Zhang, R. Horesh, K. N. Ramamurthy, L. Wu, J. Yi, K. Anderson, and K. R. Varshney, “Financial Forecasting and Analysis for Low-Wage Workers,” Bloomberg Data for Good Exchange, Sep. 2018.
  17. J. J. Thiagarajan, S. Liu, K. N. Ramamurthy, and P-T. Bremer. “Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections,” In Computer Graphics Forum, 37(3), pp. 241-251, Jul. 2018.

Book Chapters

  1. A. Som, K. N. Ramamurthy, and P. Turaga, “Geometric Metrics for Topological Representations.” In Handbook of Variational Methods for Nonlinear Geometric Data, Springer, 2020.
  2. C.-C. Lin, K. N. Ramamurthy, and S. U. Pankanti, “Moving Camera Analytics: Computer Vision Applications.” In Embedded, Cyber-Physical, and IoT Systems, pp. 89-113. Springer, Cham, 2020.

Books

  1. J. J. Thiagarajan, K. N. Ramamurthy, and A. Spanias, “Sparse Representations for Image Understanding,” Synthesis Lectures on Image, Video and Multimedia, Morgan and Claypool Publishers, 2014.
  2. K. N. Ramamurthy and A. Spanias, “MATLAB Software for the Code Excited Linear Prediction Algorithm,” Synthesis Lectures on Algorithms and Software in Engineering, Morgan and Claypool Publishers, 2010.