{"id":1062,"date":"2023-08-15T15:45:17","date_gmt":"2023-08-15T22:45:17","guid":{"rendered":"https:\/\/labs.wsu.edu\/scads\/?page_id=1062"},"modified":"2025-06-02T11:49:36","modified_gmt":"2025-06-02T18:49:36","slug":"fascada-publications","status":"publish","type":"page","link":"https:\/\/labs.wsu.edu\/scads\/fascada\/fascada-publications\/","title":{"rendered":"FASCADA Products"},"content":{"rendered":"\n

CAREER: Fast and Scalable Combinatorial Algorithms for Data Analytics (FASCADA)<\/h1>\n\n\n\n

PI: Assefaw Gebremedhin<\/a><\/h2>\n\n\n\n

School of Electrical Engineering and Computer Science<\/p>\n\n\n\n

Washington State University<\/p>\n\n\n\n

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FASCADA Products:<\/h2>\n\n\n\n
    \n
  1. J. Stachofsky, A. H. Gebremedhin, and R. Crossler. \u201cCast to Vote: A Socio-technical Network Analysis of an Election Smartphone Application<\/a>,\u201d Digital Government: Research and Practice, Vol. 3, 2022, pp. 1\u201317. https:\/\/doi.org\/10.1145\/3501031<\/li>\n\n\n\n
  2. Shruti Patil, Helen Catanese, Kelly Brayton, Eric Lofgren, and A. H. Gebremedhin. \u201cSequence Similarity Network Analysis Provides Insight into the Temporal and Geographical Distribution of Mutations in SARS-CoV-2 Spike Protein<\/a>,\u201d Viruses, Vol. 14, 2022, Art. 1672. https:\/\/doi.org\/10.3390\/v14081672<\/li>\n\n\n\n
  3. Y. Du, G. Warnell, A. H. Gebremedhin, P. Stone, and M. Taylor. \u201cLucid Dreaming for Experience Replay: Refreshing Past States with Current Policy<\/a>,\u201d Neural Computing and Applications, Vol. 34, 2022, pp. 1687\u20131712. https:\/\/doi.org\/10.1007\/s00521-021-06104-5<\/li>\n\n\n\n
  4. S. Ghosh, Y. Guo, P. Balaji, and A.H. Gebremedhin. \u201cRMACXX: An Efficient High-Level C++ Interface over MPI-3 RMA,\u201d IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), May 10-13, 2021, Melbourne, Australia.<\/li>\n\n\n\n
  5. A.H. Gebremedhin, M. Patwary, and F. Manne. \u201cParadigms for Effective Parallelization of Inherently Sequential Graph Algorithms on Multi-Core Architectures,\u201d<\/a> in Handbook of Research on Methodologies and Applications of Supercomputing, edited by V. Milutinovic and M. Kot- lar, IGI Global, 2021.<\/li>\n\n\n\n
  6. M. Ilic, R. Jaddivada, and A.H. Gebremedhin. \u201cUnified Modeling for Emulating Electric Energy Systems: Toward Digital Twin That Might Work,\u201d<\/a> in Handbook of Research on Methodologies and Applications of Supercomputing, edited by V. Milutinovic and M. Kotlar, IGI Global, 2021.<\/li>\n\n\n\n
  7. X. Liu, M. Halappanavar, K. Baker, A. Lumsdaine, and A.H. Gebremedhin. \u201cDirection-optimizing Label Propagation and its Application to Community Detection,\u201d<\/a> Proceedings of the 17th ACM International Conference on Computing Frontiers , 2020 , p.192. DOI: https:\/\/doi.org\/10.1145\/3387902.3392634.<\/li>\n\n\n\n
  8. A.H. Gebremedhin and A. Walther. \u201cAn Introduction to Algorithmic Differentiation,\u201d<\/a> WIREs Data Mining and Knowledge Discovery, 2020 , p.10:e1334. DOI: https:\/\/doi.org\/10.1002\/widm.1334<\/li>\n\n\n\n
  9. R. Saeedi, K. Sasani, and A.H. Gebremedhin. \u201cCollaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms and Evaluation,\u201d<\/a> Sensors , v.20 , 2020 , p.1932. DOI: https:\/\/doi.org\/10.3390\/s20071932<\/li>\n\n\n\n
  10. S. Norgaard, R. Saeedi, and A.H. Gebremedhin. \u201cMulti-sensor Time series Classification for Activity Tracking under Variable Length,\u201d<\/a> IEEE Sensors Journal , v.29 , 2020 , p.2701 DOI: https:\/\/doi.org\/10.1109\/JSEN.2019.2953938<\/li>\n\n\n\n
  11. R. Saeedi and A.H. Gebremedhin. \u201cA Signal-level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems,\u201d<\/a> IEEE Transactions on Mobile Computing, Vol 19, Num. 3, 513\u2014527, 2020. DOI: https:\/\/doi.org\/10.1109\/TMC.2018.2878673<\/li>\n\n\n\n
  12. Y. Du, G. Warnell, A.H. Gebremedhin, P. Stone, and M. Taylor. \u201cWork-in-Progress: Corrected Self Learning via Demonstrations\u201d Proceedings of the Adaptive and Learning Agents Workshop at AAMAS. May 2020.<\/li>\n\n\n\n
  13. Y. Du, M. Taylor, and A.H. Gebremedhin. \u201cAnalysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction,\u201d<\/a> IEEE Transactions on Knowledge and Data Engineering, Vol 31, Issue 8, 1478-1490, 2019. DOI: https:\/\/doi.org\/10.1109\/TKDE.2018.2863705<\/li>\n\n\n\n
  14. X. Liu, J. Faros, M. Zalewski, M. Halappanavar. K. Baker, A. Lumsdaine, and A.H. Gebremedhin. \u201cDistributed Direction-optimizing Label Propagation for Community Detection,\u201d<\/a> 2019 IEEE High performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2019 , 2019. DOI: https:\/\/doi.org\/10.1109\/HPEC.2019.8916215<\/li>\n\n\n\n
  15. S. Ghosh, M. Halappanavar, A. Kalyanaraman, and A.H. Gebremedhin. \u201cExploring MPI Communication Models for Graph Applications using Graph Matching as a Case Study,\u201d IEEE International Parallel and Distributed Processing Symposium (IPDPS 2019), 2019.<\/li>\n\n\n\n
  16. S. Ghosh, M. Halappanavar, A. Kalyanaraman, A. Khan, and A.H. Gebremedhin. \u201cExploring MPI Communication Models for Graph Applications using Graph Matching as a Case Study,\u201d<\/a> 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<\/li>\n\n\n\n
  17. H. Catanese, C. Hauser, and A.H. Gebremedhin. \u201cEvaluation of Native and Transfer Students\u2019 Success in a Computer Science Course,\u201d ACM Inroads, v.9 , 53-57, 2018.<\/li>\n\n\n\n
  18. H. Catanese, K. Brayton, and A.H. Gebremedhin. \u201cA Nearest-Neighbors Network Model for Sequence Data Reveals New Insight into Genotype Distribution of a Pathogen,\u201d<\/a> BMC Bioinformatics , v.19 , 2018. DOI: https:\/\/doi.org\/10.1186\/s12859-018-2453-2<\/li>\n\n\n\n
  19. K. Sasani, M. Namaki, and A.H. Gebremedhin. \u201cMulti-metric Graph Query Performance Prediction,\u201d International Conference on Database Systems for Advanced Applications (DASFAA 2018), 2018.<\/li>\n\n\n\n
  20. K. Sasani, M. Namaki, and A.H. Gebremedhin. \u201cNetwork Similarity Prediction in Time-evolving Graphs: A Machine Learning Approach\u201d IPDPS Workshop on Intersection of Graph Algorithms and Machine Learning, 2018<\/li>\n\n\n\n
  21. R. Saeedi, K. Sasani, S. Norgaard, and A.H. Gebremedhin. \u201cPersonalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer,\u201d IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018), 2018.<\/li>\n\n\n\n
  22. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, and A.H. Gebremedhin. \u201cminiVite: A Graph Analytics Benchmarking Tool for Massively Parallel Systems,\u201d<\/a> ACM\/IEEE Supercomputing (SC 2018) Workshop on Performance Modeling, Benchmarking and Simulation (PMBS 2018), 2018. DOI: https:\/\/doi.org\/10.1109\/PMBS.2018.8641631<\/li>\n\n\n\n
  23. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, and A.H. Gebremedhin. \u201cScalable Distributed-memory Community Detection using Vite,\u201d IEEE High Performance Extreme Computing Conference (HPEC 2018), 2018.<\/li>\n\n\n\n
  24. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, H. Lu, D. Chavarri-Miranda, A. Khan, and A.H. Gebremedhin. \u201cDistributed Louvain algorithm for graph community detection,\u201d IEEE International Parallel and Distributed Processing Symposium (IPDPS 2018), 2018.<\/li>\n\n\n\n
  25. S. Norgaard, R. Saeedi, K. Sasani, and A.H. Gebremedhin. \u201cSynthetic Data Generation for Health Applications: A Supervised Deep Learning Approach,\u201d IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018), 2018.<\/li>\n\n\n\n
  26. H. Lu, M. Halappanavar, D. Chavarri a Miranda, A.H. Gebremedhin, A. Panyala, and A. Kalyanaraman. \u201cAlgorithms for balanced graph colorings with applications in parallel computing,<\/a>\u201d IEEE Transactions on Parallel and Distributed Systems , v.28 , 2017 , p.1240\u20141256. DOI: https:\/\/doi.org\/10.1109\/TPDS.2016.2620142<\/li>\n\n\n\n
  27. R. Saeedi, K. Sasani, and A.H. Gebremedhin. \u201cCo-MEAL: Cost-optimal Multi-Expert Active Learning architecture for mobile health monitoring,\u201d ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2017) , 2017.<\/li>\n\n\n\n
  28. R. Saeedi, S. Norgaard, and A.H. Gebremedhin. \u201cA closed-loop deep learning architecture for robust activity recognition using wearable sensors,\u201d IEEE International Conference on Big Data (BigData 2017) , 2017.<\/li>\n\n\n\n
  29. H.N. Catanese, K.A. Brayton, and A.H. Gebremedhin. \u201cRepeatAnalyzer: a tool for analyzing and managing short-sequence repeat data,\u201d<\/a> BMC Genomics , v.17 , 2016. DOI: https:\/\/doi.org\/10.1186\/s12864-016-2686-2<\/li>\n\n\n\n
  30. R. Saeedi, H. Ghasemzadeh, and A.H. Gebremedhin. \u201cTransfer learning algorithms for autonomous configuration of wearable systems,\u201d 2016 IEEE International Conference on Big Data (BigData\u201916) , 2016.<\/li>\n\n\n\n
  31. S. Ghosh and A.H. Gebremedhin. \u201cParallelization of bin packing on multicore systems,\u201d 2016 IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC\u201916) , 2016.<\/li>\n\n\n\n
  32. S. Ghosh, J.R. Hammond, A.J. Pena, P. Balaji, A.H. Gebremedhin, and B. Chapman. \u201cOne-sided interface for matrix operations using MPI-3 RMA: A case study with Elemental,\u201d International Conference on Parallel Processing (ICPP 2016) , 2016.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    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:<\/p>\n","protected":false},"author":5234,"featured_media":0,"parent":1054,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"categories":[],"tags":[],"university_category":[],"location":[],"organization":[],"_links":{"self":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1062"}],"collection":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/users\/5234"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/comments?post=1062"}],"version-history":[{"count":10,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1062\/revisions"}],"predecessor-version":[{"id":1496,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1062\/revisions\/1496"}],"up":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/1054"}],"wp:attachment":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/media?parent=1062"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/categories?post=1062"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/tags?post=1062"},{"taxonomy":"wsuwp_university_category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/university_category?post=1062"},{"taxonomy":"wsuwp_university_location","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/location?post=1062"},{"taxonomy":"wsuwp_university_org","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/organization?post=1062"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}