CAREER: Fast and Scalable Combinatorial Algorithms for Data Analytics (FASCADA)

PI: Assefaw Gebremedhin

School of Electrical Engineering and Computer Science

Washington State University

FASCADA Products:

  1. S. Ghosh, Y. Guo, P. Balaji, and A.H. Gebremedhin. “RMACXX: An Efficient High-Level C++ Interface over MPI-3 RMA,” IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), May 10-13, 2021, Melbourne, Australia.
  2. A.H. Gebremedhin, M. Patwary, and F. Manne. “Paradigms for Effective Parallelization of Inherently Sequential Graph Algorithms on Multi-Core Architectures,” in Handbook of Research on Methodologies and Applications of Supercomputing, edited by V. Milutinovic and M. Kot- lar, IGI Global, 2021.
  3. M. Ilic, R. Jaddivada, and A.H. Gebremedhin. “Unified Modeling for Emulating Electric Energy Systems: Toward Digital Twin That Might Work,” in Handbook of Research on Methodologies and Applications of Supercomputing, edited by V. Milutinovic and M. Kotlar, IGI Global, 2021.
  4. X. Liu, M. Halappanavar, K. Baker, A. Lumsdaine, and A.H. Gebremedhin. “Direction-optimizing Label Propagation and its Application to Community Detection,” Proceedings of the 17th ACM International Conference on Computing Frontiers , 2020 , p.192. DOI: https://doi.org/10.1145/3387902.3392634.
  5. A.H. Gebremedhin and A. Walther. “An Introduction to Algorithmic Differentiation,” WIREs Data Mining and Knowledge Discovery, 2020 , p.10:e1334. DOI: https://doi.org/10.1002/widm.1334
  6. R. Saeedi, K. Sasani, and A.H. Gebremedhin. “Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms and Evaluation,” Sensors , v.20 , 2020 , p.1932. DOI: https://doi.org/10.3390/s20071932
  7. S. Norgaard, R. Saeedi, and A.H. Gebremedhin. “Multi-sensor Time series Classification for Activity Tracking under Variable Length,” IEEE Sensors Journal , v.29 , 2020 , p.2701 DOI: https://doi.org/10.1109/JSEN.2019.2953938
  8. R. Saeedi and A.H. Gebremedhin. “A Signal-level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems,” IEEE Transactions on Mobile Computing, Vol 19, Num. 3, 513—527, 2020. DOI: https://doi.org/10.1109/TMC.2018.2878673
  9. Y. Du, G. Warnell, A.H. Gebremedhin, P. Stone, and M. Taylor. “Work-in-Progress: Corrected Self Learning via Demonstrations” Proceedings of the Adaptive and Learning Agents Workshop at AAMAS. May 2020.
  10. Y. Du, M. Taylor, and A.H. Gebremedhin. “Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction,” IEEE Transactions on Knowledge and Data Engineering, Vol 31, Issue 8, 1478-1490, 2019. DOI: https://doi.org/10.1109/TKDE.2018.2863705
  11. X. Liu, J. Faros, M. Zalewski, M. Halappanavar. K. Baker, A. Lumsdaine, and A.H. Gebremedhin. “Distributed Direction-optimizing Label Propagation for Community Detection,” 2019 IEEE High performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2019 , 2019. DOI: https://doi.org/10.1109/HPEC.2019.8916215
  12. S. Ghosh, M. Halappanavar, A. Kalyanaraman, and A.H. Gebremedhin. “Exploring MPI Communication Models for Graph Applications using Graph Matching as a Case Study,” IEEE International Parallel and Distributed Processing Symposium (IPDPS 2019), 2019.
  13. S. Ghosh, M. Halappanavar, A. Kalyanaraman, A. Khan, and A.H. Gebremedhin. “Exploring MPI Communication Models for Graph Applications using Graph Matching as a Case Study,” IEEE International Parallel and Distributed Processing Symposium (IPDPS 2019), May 2019, Rio de Janerio, Brazil , 2019. DOI: https://doi.org/10.1109/IPDPS.2019.00085
  14. H. Catanese, C. Hauser, and A.H. Gebremedhin. “Evaluation of Native and Transfer Students’ Success in a Computer Science Course,” ACM Inroads, v.9 , 53-57, 2018.
  15. H. Catanese, K. Brayton, and A.H. Gebremedhin. “A Nearest-Neighbors Network Model for Sequence Data Reveals New Insight into Genotype Distribution of a Pathogen,” BMC Bioinformatics , v.19 , 2018. DOI: https://doi.org/10.1186/s12859-018-2453-2
  16. K. Sasani, M. Namaki, and A.H. Gebremedhin. “Multi-metric Graph Query Performance Prediction,” International Conference on Database Systems for Advanced Applications (DASFAA 2018), 2018.
  17. K. Sasani, M. Namaki, and A.H. Gebremedhin. “Network Similarity Prediction in Time-evolving Graphs: A Machine Learning Approach” IPDPS Workshop on Intersection of Graph Algorithms and Machine Learning, 2018
  18. R. Saeedi, K. Sasani, S. Norgaard, and A.H. Gebremedhin. “Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer,” IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018), 2018.
  19. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, and A.H. Gebremedhin. “miniVite: A Graph Analytics Benchmarking Tool for Massively Parallel Systems,” ACM/IEEE Supercomputing (SC 2018) Workshop on Performance Modeling, Benchmarking and Simulation (PMBS 2018), 2018. DOI: https://doi.org/10.1109/PMBS.2018.8641631
  20. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, and A.H. Gebremedhin. “Scalable Distributed-memory Community Detection using Vite,” IEEE High Performance Extreme Computing Conference (HPEC 2018), 2018.
  21. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, H. Lu, D. Chavarri-Miranda, A. Khan, and A.H. Gebremedhin. “Distributed Louvain algorithm for graph community detection,” IEEE International Parallel and Distributed Processing Symposium (IPDPS 2018), 2018.
  22. S. Norgaard, R. Saeedi, K. Sasani, and A.H. Gebremedhin. “Synthetic Data Generation for Health Applications: A Supervised Deep Learning Approach,” IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018), 2018.
  23. H. Lu, M. Halappanavar, D. Chavarri a Miranda, A.H. Gebremedhin, A. Panyala, and A. Kalyanaraman. “Algorithms for balanced graph colorings with applications in parallel computing,” IEEE Transactions on Parallel and Distributed Systems , v.28 , 2017 , p.1240—1256. DOI: https://doi.org/10.1109/TPDS.2016.2620142
  24. R. Saeedi, K. Sasani, and A.H. Gebremedhin. “Co-MEAL: Cost-optimal Multi-Expert Active Learning architecture for mobile health monitoring,” ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2017) , 2017.
  25. R. Saeedi, S. Norgaard, and A.H. Gebremedhin. “A closed-loop deep learning architecture for robust activity recognition using wearable sensors,” IEEE International Conference on Big Data (BigData 2017) , 2017.
  26. H.N. Catanese, K.A. Brayton, and A.H. Gebremedhin. “RepeatAnalyzer: a tool for analyzing and managing short-sequence repeat data,” BMC Genomics , v.17 , 2016. DOI: https://doi.org/10.1186/s12864-016-2686-2
  27. R. Saeedi, H. Ghasemzadeh, and A.H. Gebremedhin. “Transfer learning algorithms for autonomous configuration of wearable systems,” 2016 IEEE International Conference on Big Data (BigData’16) , 2016.
  28. S. Ghosh and A.H. Gebremedhin. “Parallelization of bin packing on multicore systems,” 2016 IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC’16) , 2016.
  29. S. Ghosh, J.R. Hammond, A.J. Pena, P. Balaji, A.H. Gebremedhin, and B. Chapman. “One-sided interface for matrix operations using MPI-3 RMA: A case study with Elemental,” International Conference on Parallel Processing (ICPP 2016) , 2016.