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ORCIDRamakrishnan Kannan
This is just a disambiguation page, and is not intended to be the bibliography of an actual person. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
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2020 – today
- 2024
[c52]Bharat Srikishan, Anika Tabassum, Srikanth Allu, Ramakrishnan Kannan, Nikhil Muralidhar:
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation. AAAI 2024: 15066-15074- [c51]Yongseok Soh
, Ramakrishnan Kannan, Piyush Sao, Jee W. Choi:
Accelerated Constrained Sparse Tensor Factorization on Massively Parallel Architectures. ICPP 2024: 107-116 - [c50]Emre Eftelioglu, Bistra Dilkina, Naoki Abe, Ramakrishnan Kannan, Yuzhou Chen, Yulia R. Gel, Kathleen Buckingham, Auroop R. Ganguly, James Hodson, Jiafu Mao:
Fragile Earth: Generative and Foundational Models for Sustainable Development. KDD 2024: 6710-6711 - [i11]Bharat Srikishan, Anika Tabassum, Srikanth Allu, Ramakrishnan Kannan, Nikhil Muralidhar:
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation. CoRR abs/2402.11760 (2024) - [i10]Zhi-Feng Wei, Pablo Moriano, Ramakrishnan Kannan:
Robustness of graph embedding methods for community detection. CoRR abs/2405.00636 (2024) - 2023
- [j13]Shruti Shivakumar, Jiajia Li, Ramakrishnan Kannan, Srinivas Aluru:
Sparse Symmetric Format for Tucker Decomposition. IEEE Trans. Parallel Distributed Syst. 34(6): 1743-1756 (2023) - [c49]Ramakrishnan Kannan, Cristina Garcia-Cardona, Balasubramaniam Radhakrishnan, Sudip K. Seal:
A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations. IEEE Big Data 2023: 1744-1753 - [c48]Srinivas Eswar, Benjamin Cobb, Koby Hayashi, Ramakrishnan Kannan, Grey Ballard, Richard W. Vuduc, Haesun Park:
Distributed-Memory Parallel JointNMF. ICS 2023: 301-312 - [c47]Tianle Wang, Sudip K. Seal, Ramakrishnan Kannan, Cristina Garcia-Cardona, Thomas Proffen, Shantenu Jha:
A Parallel Machine Learning Workflow for Neutron Scattering Data Analysis. IPDPS Workshops 2023: 795-798 - [c46]Naoki Abe, Kathleen Buckingham, Yuzhou Chen, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, Yulia R. Gel, James Hodson, Ramakrishnan Kannan, Huikyo Lee, Jiafu Mao, Rose Yu:
Fragile Earth: AI for Climate Sustainability - From Wildfire Disaster Management to Public Health and Beyond. KDD 2023: 5845-5846 - [c45]Hao Lu, Piyush Sao, Michael A. Matheson, Ramakrishnan Kannan, Feiyi Wang, Thomas E. Potok:
Optimizing Communication in 2D Grid-Based MPI Applications at Exascale. EuroMPI 2023: 9:1-9:11 - [d2]Srinivas Eswar, Benjamin Cobb, Koby Hayashi, Ramakrishnan Kannan, Grey Ballard, Rich Vuduc, Haesun Park:
AminerMag S Dataset. Zenodo, 2023
- [d1]Srinivas Eswar, Benjamin Cobb, Koby Hayashi, Ramakrishnan Kannan, Grey Ballard, Rich Vuduc, Haesun Park:
AminerMag X Dataset. Zenodo, 2023 - 2022
- [c44]Anika Tabassum, Nikhil Muralidhar, Ramakrishnan Kannan, Srikanth Allu:
MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning. IEEE Big Data 2022: 1936-1941 - [c43]Naoki Abe, Kathleen Buckingham, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, James Hodson, Ramakrishnan Kannan, Rose Yu:
Fragile Earth: AI for Climate Mitigation, Adaptation, and Environmental Justice. KDD 2022: 4866-4867 - [c42]Ramakrishnan Kannan, Piyush Sao, Hao Lu, Jakub Kurzak, Gundolf Schenk, Yongmei Shi, Seung-Hwan Lim, Sharat Israni, Vijay Thakkar, Guojing Cong, Robert M. Patton, Sergio E. Baranzini, Richard W. Vuduc, Thomas E. Potok:
Exaflops Biomedical Knowledge Graph Analytics. SC 2022: 6:1-6:11 - [i9]Khalid Ibrahim Adem, Ramakrishnan Kannan, Kousalya Govardhanan, Rathimala Kannan:
Taking facial expression recognition outside the lab and into the wild by using challenging datasets and improved performance metrics. F1000Research 11: 349 (2022) - 2021
- [j12]Francis J. Alexander, James A. Ang, Jenna A. Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, Sutanay Choudhury, Bert J. Debusschere, Anthony M. DeGennaro, Nikoli Dryden, J. Austin Ellis, Ian T. Foster, Cristina Garcia-Cardona, Sayan Ghosh, Peter Harrington, Yunzhi Huang, Shantenu Jha, Travis Johnston, Ai Kagawa, Ramakrishnan Kannan, Neeraj Kumar, Zhengchun Liu, Naoya Maruyama, Satoshi Matsuoka, Erin McCarthy, Jamaludin Mohd-Yusof, Peter Nugent, Yosuke Oyama, Thomas Proffen, David Pugmire, Sivasankaran Rajamanickam, Vinay Ramakrishnaiah, Malachi Schram, Sudip K. Seal, Ganesh Sivaraman, Christine Sweeney, Li Tan, Rajeev Thakur, Brian Van Essen, Logan T. Ward, Paul M. Welch, Michael Wolf, Sotiris S. Xantheas, Kevin G. Yager, Shinjae Yoo, Byung-Jun Yoon:
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35(6): 598-616 (2021) - [j11]Srinivas Eswar, Ramakrishnan Kannan, Richard W. Vuduc, Haesun Park:
ORCA: Outlier detection and Robust Clustering for Attributed graphs. J. Glob. Optim. 81(4): 967-989 (2021) - [j10]Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Michael A. Matheson, Haesun Park:
PLANC: Parallel Low-rank Approximation with Nonnegativity Constraints. ACM Trans. Math. Softw. 47(3): 20:1-20:37 (2021) - [c41]Shruti Shivakumar, Jiajia Li, Ramakrishnan Kannan, Srinivas Aluru:
Efficient Parallel Sparse Symmetric Tucker Decomposition for High-Order Tensors. ACDA 2021: 193-204 - [c40]Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, Stephan Priebe:
Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. IEEE BigData 2021: 1809-1816 - [c39]Seung-Hwan Lim, Junghoon Chae, Guojing Cong, Drahomira Herrmannova, Robert M. Patton, Ramakrishnan Kannan, Thomas E. Potok:
Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis. IEEE BigData 2021: 4381-4386 - [c38]Piyush Sao, Hao Lu, Ramakrishnan Kannan, Vijay Thakkar, Richard W. Vuduc, Thomas E. Potok:
Scalable All-pairs Shortest Paths for Huge Graphs on Multi-GPU Clusters. HPDC 2021: 121-131 - [c37]Kuldeep R. Kurte, Neena Imam, Ramakrishnan Kannan, S. M. Shamimul Hasan, Srikanth B. Yoginath:
Co-design of Advanced Architectures for Graph Analytics using Machine Learning. IPDPS Workshops 2021: 298-307 - [c36]Catherine D. Schuman, Bill Kay, Prasanna Date, Ramakrishnan Kannan, Piyush Sao, Thomas E. Potok:
Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers. IPDPS Workshops 2021: 308-311 - [c35]S. M. Shamimul Hasan, Neena Imam, Ramakrishnan Kannan, Srikanth B. Yoginath, Kuldeep R. Kurte:
Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications. IPDPS Workshops 2021: 439-448 - [c34]Naoki Abe, Kathleen Buckingham, Bistra Dilkina, Emre Eftelioglu, Auroop R. Ganguly, James Hodson, Ramakrishnan Kannan:
Fragile Earth: Accelerating Progress towards Equitable Sustainability. KDD 2021: 4102-4103 - [c33]Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan, Seung-Hwan Lim, Thomas E. Potok, Jordan B. Chipka, Priyantha Mudalige, Mark Coletti, Sajal Dash, Arnab Kumar Paul, Sarp Oral, Feiyi Wang, Bill Kay, Melissa R. Allen-Dumas, Christa Brelsford, Joshua R. New, Andy Berres, Kuldeep R. Kurte, Jibonananda Sanyal, Levi Sweet, Chathika Gunaratne, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin, Olivera Kotevska, Jean C. Bilheux, Hassina Z. Bilheux, Garrett E. Granroth, Thomas Proffen, Rick Riedel, Peter F. Peterson, Shruti R. Kulkarni, Kyle P. Kelley, Stephen Jesse, Maryam Parsa:
Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. SMC 2021: 361-382 - [i8]Aydin Buluç, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony M. DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E. Lopes, Per-Gunnar Martinsson, Kary L. Myers, Jelani Nelson, Juan M. Restrepo, C. Seshadhri, Draguna L. Vrabie, Brendt Wohlberg, Stephen J. Wright, Chao Yang, Peter Zwart:
Randomized Algorithms for Scientific Computing (RASC). CoRR abs/2104.11079 (2021) - [i7]Rathimala Kannan, Ivan Zhi Wei Wang, Hway Boon Ong, Ramakrishnan Kannan, Andry Alamsyah:
COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques. F1000Research 10: 932 (2021) - [i6]Rathimala Kannan, Yonesh Reddiar, Ramakrishnan Kannan, Marrynal S. Eastaff, Shobana Ramesh:
Job characteristics of a Malaysian bank's anti-money laundering system and its employees' job satisfaction. F1000Research 10: 1052 (2021) - 2020
- [j9]M. Todd Young, Jacob D. Hinkle, Ramakrishnan Kannan, Arvind Ramanathan:
Distributed Bayesian optimization of deep reinforcement learning algorithms. J. Parallel Distributed Comput. 139: 43-52 (2020) - [c32]Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Sudip K. Seal:
Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach. IEEE BigData 2020: 1147-1155 - [c31]Lawton Manning, Grey Ballard, Ramakrishnan Kannan, Haesun Park:
Parallel Hierarchical Clustering using Rank-Two Nonnegative Matrix Factorization. HiPC 2020: 141-150 - [c30]Ravdeep S. Pasricha, Pravallika Devineni, Evangelos E. Papalexakis, Ramakrishnan Kannan:
Tensorized Feature Spaces for Feature Explosion. ICPR 2020: 6298-6304 - [c29]Piyush Sao, Ramakrishnan Kannan, Prasun Gera, Richard W. Vuduc:
A supernodal all-pairs shortest path algorithm. PPoPP 2020: 250-261 - [c28]Ramakrishnan Kannan, Piyush Sao, Hao Lu, Drahomira Herrmannova, Vijay Thakkar, Robert M. Patton, Richard W. Vuduc, Thomas E. Potok:
Scalable knowledge graph analytics at 136 petaflop/s. SC 2020: 6 - [c27]Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Richard W. Vuduc, Haesun Park:
Distributed-memory parallel symmetric nonnegative matrix factorization. SC 2020: 74
2010 – 2019
- 2019
- [j8]Husayn Ahmed P., Vidhya V., Ravi Prabhakar More, Biju Viswanath, Sanjeev Jain, Mahendra S. Rao, Odity Mukherjee, Naren P. Rao, Janardhanan C. Narayanaswamy, Palanimuthu T. Sivakumar, Arun Kandaswamy, Muralidharan Kesavan, Urvakhsh-Meherwan Mehta, Ganesan Venkatasubramanian, John P. John, Meera Purushottam, Ramakrishnan Kannan, Bhupesh Mehta, Thennarasu Kandavel, B. Binukumar, Jitender Saini, Deepak Jayarajan, A. Shyamsundar, Sydney Moirangthem, Kumar G. Vijay, Jagadisha Thirthalli, Prabha S. Chandra, Bangalore N. Gangadhar, Pratima Murthy, Mitradas M. Panicker, Upinder S. Bhalla, Sumantra Chattarji, Vivek Benegal, Mathew Varghese, Janardhan Y. C. Reddy, Padinjat Raghu:
INDEX-db: The Indian Exome Reference Database (Phase I). J. Comput. Biol. 26(3): 225-234 (2019) - [c26]Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Katharine Page, Sudip K. Seal:
Learning to Predict Material Structure from Neutron Scattering Data. IEEE BigData 2019: 4490-4497 - [c25]S. M. Shamimul Hasan, Drew Schmidt, Ramakrishnan Kannan, Neena Imam:
A Scalable Graph Analytics Framework for Programming with Big Data in R (pbdR). IEEE BigData 2019: 4783-4792 - [c24]Piyush Sao, Ramakrishnan Kannan, Xiaoye Sherry Li, Richard W. Vuduc:
A communication-avoiding 3D sparse triangular solver. ICS 2019: 127-137 - [c23]Piyush Sao, Ramakrishnan Kannan:
Multifrontal Non-negative Matrix Factorization. PPAM (1) 2019: 543-554 - [i5]Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Michael A. Matheson, Haesun Park:
PLANC: Parallel Low Rank Approximation with Non-negativity Constraints. CoRR abs/1909.01149 (2019) - 2018
- [j7]Jaegul Choo, Hannah Kim, Edward Clarkson, Zhicheng Liu, Changhyun Lee, Fuxin Li, Hanseung Lee, Ramakrishnan Kannan, Charles D. Stolper, John T. Stasko, Haesun Park:
VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data. ACM Trans. Knowl. Discov. Data 12(1): 8:1-8:20 (2018) - [j6]Ramakrishnan Kannan, Grey Ballard, Haesun Park:
MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization. IEEE Trans. Knowl. Data Eng. 30(3): 544-558 (2018) - [c22]Md Mosharaf Hossain, Thomas M. Hines, Sheikh K. Ghafoor, Sheikh Rabiul Islam, Ramakrishnan Kannan, Sreenivas R. Sukumar:
A Flexible-blocking Based Approach for Performance Tuning of Matrix Multiplication Routines for Large Matrices with Edge Cases. IEEE BigData 2018: 3853-3862 - [c21]Minsuk Choi, Dear Sungbok Shin, Jinho Choi, Scott Langevin, Christopher Bethune, Philippe Horne, Nathan Kronenfeld, Ramakrishnan Kannan, Barry L. Drake, Haesun Park, Jaegul Choo:
TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media. CHI 2018: 583 - [c20]Luna Xu, Ali Raza Butt, Seung-Hwan Lim, Ramakrishnan Kannan:
A Heterogeneity-Aware Task Scheduler for Spark. CLUSTER 2018: 245-256 - [c19]Grey Ballard, Koby Hayashi, Ramakrishnan Kannan:
Parallel Nonnegative CP Decomposition of Dense Tensors. HiPC 2018: 22-31 - [c18]Oguz Kaya, Ramakrishnan Kannan, Grey Ballard:
Partitioning and Communication Strategies for Sparse Non-negative Matrix Factorization. ICPP 2018: 90:1-90:10 - [c17]M. Todd Young, Jacob D. Hinkle, Arvind Ramanathan, Ramakrishnan Kannan:
HyperSpace: Distributed Bayesian Hyperparameter Optimization. SBAC-PAD 2018: 339-347 - [i4]Grey Ballard, Koby Hayashi, Ramakrishnan Kannan:
Parallel Nonnegative CP Decomposition of Dense Tensors. CoRR abs/1806.07985 (2018) - 2017
- [j5]A. Mohamed Uvaze Ahamed, Eswaran Chikkannan, Ramakrishnan Kannan:
Lossy image compression based on prediction error and vector quantisation. EURASIP J. Image Video Process. 2017: 35 (2017) - [j4]C. Eswaran, Ramakrishnan Kannan, A. Mohamed Uvaze Ahamed:
CBIR System Based On Prediction Errors. J. Inf. Sci. Eng. 33(2): 347-365 (2017) - [c16]Luna Xu, Seung-Hwan Lim, Min Li, Ali Raza Butt, Ramakrishnan Kannan:
Scaling up data-parallel analytics platforms: Linear algebraic operation cases. IEEE BigData 2017: 273-282 - [c15]Dear Sungbok Shin, Minsuk Choi, Jinho Choi, Scott Langevin, Christopher Bethune, Philippe Horne, Nathan Kronenfeld, Ramakrishnan Kannan, Barry L. Drake, Haesun Park, Jaegul Choo:
STExNMF: Spatio-Temporally Exclusive Topic Discovery for Anomalous Event Detection. ICDM 2017: 435-444 - [c14]Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park:
Outlier Detection for Text Data. SDM 2017: 489-497 - [i3]Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park:
Outlier Detection for Text Data : An Extended Version. CoRR abs/1701.01325 (2017) - 2016
- [c13]Sreenivas R. Sukumar, Ramakrishnan Kannan, Seung-Hwan Lim, Michael A. Matheson:
Kernels for scalable data analysis in science: Towards an architecture-portable future. IEEE BigData 2016: 1026-1031 - [c12]Sreenivas R. Sukumar, Michael A. Matheson, Ramakrishnan Kannan, Seung-Hwan Lim:
Mini-apps for high performance data analysis. IEEE BigData 2016: 1483-1492 - [c11]Ramakrishnan Kannan, Grey Ballard, Haesun Park:
A high-performance parallel algorithm for nonnegative matrix factorization. PPoPP 2016: 9:1-9:11 - [c10]Luna Xu, Seung-Hwan Lim, Ali Raza Butt, Sreenivas R. Sukumar, Ramakrishnan Kannan:
FatMan vs. LittleBoy: Scaling Up Linear Algebraic Operations in Scale-Out Data Platforms. PDSW-DISCS@SC 2016: 25-30 - [i2]Ramakrishnan Kannan, Grey Ballard, Haesun Park:
MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization. CoRR abs/1609.09154 (2016) - 2015
- [j3]James P. Fairbanks, Ramakrishnan Kannan, Haesun Park, David A. Bader:
Behavioral clusters in dynamic graphs. Parallel Comput. 47: 38-50 (2015) - [i1]Ramakrishnan Kannan, Grey Ballard, Haesun Park:
A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization. CoRR abs/1509.09313 (2015) - 2014
- [j2]Ramakrishnan Kannan, Mariya Ishteva, Haesun Park:
Bounded matrix factorization for recommender system. Knowl. Inf. Syst. 39(3): 491-511 (2014) - [c9]Richard Fujimoto, Angshuman Guin, Michael Hunter, Haesun Park, Gaurav Kanitkar, Ramakrishnan Kannan, Michael Milholen, Sabra Neal, Philip Pecher:
A Dynamic Data Driven Application System for Vehicle Tracking. ICCS 2014: 1203-1215 - [c8]Jaegul Choo, Changhyun Lee, Hannah Kim, Hanseung Lee, Zhicheng Liu, Ramakrishnan Kannan, Charles D. Stolper, John T. Stasko, Barry L. Drake, Haesun Park:
VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data. IEEE VAST 2014: 243-244 - 2012
- [c7]Ramakrishnan Kannan, Mariya Ishteva, Haesun Park:
Bounded Matrix Low Rank Approximation. ICDM 2012: 319-328 - 2011
- [c6]Amol Ghoting, Prabhanjan Kambadur, Edwin P. D. Pednault, Ramakrishnan Kannan:
NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce. KDD 2011: 334-342 - [c5]Mahashweta Das, Deepak Padmanabhan, Prasad Deshpande, Ramakrishnan Kannan:
Fast Rule Mining Over Multi-Dimensional Windows. SDM 2011: 582-593
2000 – 2009
- 2009
- [c4]Joseph P. Bigus, Upendra Chitnis, Prasad M. Deshpande, Ramakrishnan Kannan, Mukesh K. Mohania, Sumit Negi, Deepak Padmanabhan, Edwin P. D. Pednault, Soujanya Soni, Bipen K. Telkar, Brian F. White:
CRM Analytics Framework. COMAD 2009 - [c3]Ramakrishnan Kannan, Dinesh Garg, Karthik Subbian, Y. Narahari:
Nash Bargaining Based Ad Networks for Sponsored Search Auctions. CEC 2009: 170-175 - 2008
- [c2]Ramakrishnan Kannan, Dinesh Garg, Karthik Subbian, Yadati Narahari:
A Nash bargaining approach to retention enhancing bid optimization in sponsored search auctions with discrete bids. CASE 2008: 1007-1012 - 2007
- [j1]Ramakrishnan Kannan, C. Eswaran:
Lossless Compression Schemes for ECG Signals Using Neural Network Predictors. EURASIP J. Adv. Signal Process. 2007 (2007) - [c1]Karthik Subbian, Ramakrishnan Kannan, Raghav Kumar Gautam, Y. Narahari:
Incentive Compatible Mechanisms for Group Ticket Allocation in Software Maintenance Services. APSEC 2007: 270-277
Coauthor Index
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