Selected Publications (DBLP List)

  1. Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Richard W. Vuduc, Haesun Park: Distributed-memory parallel symmetric nonnegative matrix factorization. SC 2020: 74
  2. Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Katharine Page, Sudip K. Seal: Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach. BigData 2020: Accepted
  3. Ramakrishnan Kannan, Piyush Sao, Hao Lu, Drahomira Hermannova, Vijay Thakkar, Robert Patton, Richard Vuduc, Thomas Potok: Scalable Knowledge Graph Analytics at 136 PetaFLOPS. International Conference for High Performance Computing, Networking, Storage and Analysis (SC’20): Accepted, Gordon Bell Finalist
  4. Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Michael Matheson, Heasun Park: PLANC: Parallel Low Rank Approximation with Non-negativity Constraints. ACM Transacation on Mathematical Software(TOMS): Accepted
  5. Piyush Sao, Ramakrishnan Kannan, Prasun Gera, Richard W. Vuduc: A supernodal all-pairs shortest path algorithm. PPoPP 2020: 250-261
  6. Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Katharine Page, Sudip K. Seal: Learning to Predict Material Structure from Neutron Scattering Data. BigData 2019: 4490-4497
  7. Piyush Sao, Ramakrishnan Kannan: Multifrontal Non-negative Matrix Factorization. PPAM (1) 2019: 543-554
  8. Koby Hayashi, Grey Ballard and Ramakrishnan. 2018. Parallel Nonnegative CP Decomposition of Dense Tensors. Accepted at 25th IEEE International Conference on High Performance Computing, Data, And Analytics (HiPC’18)
  9. Oguz Kaya, Ramakrishnan Kannan, and Grey Ballard. 2018. Partitioning and Communication Strategies for Sparse Non-negative Matrix Factorization. In Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018). ACM, New York, NY, USA, Article 90, 10 pages. DOI
  10. Kannan, R., Ievlev, A. V., Laanait, N., Ziatdinov, M. A., Vasudevan, R. K., Jesse, S., & Kalinin, S. V. (2018). Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform. Advanced structural and chemical imaging, 4(1), 6.
  11. Choo, J., Kim, H., Clarkson, E., Liu, Z., Lee, C., Li, F., Lee, H., Kannan, R., Stolper, C.D., Stasko, J. and Park, H., 2018. VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data. ACM Transactions on Knowledge Discovery from Data (TKDD), 12(1), p.8.
  12. R. Kannan, G. Ballard and H. Park, “MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization,” in IEEE Transactions on Knowledge and Data Engineering, vol. PP, no. 99, pp. 1-1. doi: 10.1109/TKDE.2017.2767592 Code
  13. Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park: Outlier Detection for Text Data. SDM 2017: 489-497 Presentation Code
  14. Ramakrishnan Kannan, Grey Ballard, and Haesun Park. 2016. A high-performance parallel algorithm for nonnegative matrix factorization. In Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP ‘16). ACM, New York, NY, USA, , Article 9 , 11 pages. DOI
  15. Ramakrishnan Kannan, Mariya Ishteva, Barry Drake, Haesun Park : Bounded Matrix Low Rank Approximation in Non-negative Matrix Factorization Techniques : Advances in Theory and Applications : 89-118 (2016)
  16. James P. Fairbanks, Ramakrishnan Kannan, Haesun Park, David A. Bader, Behavioral clusters in dynamic graphs, Parallel Computing, Volume 47, August 2015, Pages 38-50, ISSN 0167-8191, DOI.
  17. Ramakrishnan Kannan, Mariya Ishteva, Haesun Park: Bounded matrix factorization for recommender system. Knowl. Inf. Syst. 39(3): 491-511 (2014)
  18. Ramakrishnan Kannan, Mariya Ishteva and Haesun Park: Bounded Matrix Low Rank Approximation, Accepted at ICDM 2012
  19. Amol Ghoting, Prabhanjan Kambadur, Edwin Pednault, and Ramakrishnan Kannan: NIMBLE: An Infrastructure for the Rapid Implementation of Parallel Data Mining and Machine Learning Algorithms on MapReduce, KDD 2011:334-342
  20. Mahasweta Das, Prasad M Deshpande, Deepak S Padamnaban and Ramakrishnan Kannan: Fast Rule Mining Over Multi-dimensional Windows, SIAM International Conference on Data Mining, SDM 2011:582-593
  21. Joseph P Bigus, Upendra Chitnis, Prasad M Deshpande, Ramakrishnan Kannan, Mukesh K Mohania, Sumit Negi, Deepak P, Edwin Pednault, Soujanya Soni, Bipen K Telkar, Brian F White: CRM Analytics Framework, Accepted at COMAD 2009
  22. Ramakrishnan Kannan, Dinesh Garg, Karthik Subbian, Y. Narahari: Nash Bargaining Based Ad Networks for Sponsored Search Auctions, IEEE Conference on Commerce and Enterprise Computing CEC 2009:170-175
  23. Ramakrishnan Kannan, Dinesh Garg, Karthik Subbian, and Y. Narahari: A Nash Bargaining Approach to Retention Enhancing Bid Optimization in Sponsored Search Auctions with Discrete Bids, IEEE Conference on Automation Science and Engineering, IEEE-CASE , 2008:1007-1012
  24. Karthik Subbian, Ramakrishnan Kannan, Raghav Kumar Gautam, Y. Narahari: Incentive Compatible Mechanisms for Group Ticket Allocation in Software Maintenance Services. APSEC 2007:270-277

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