{"id":897,"date":"2023-08-15T12:26:46","date_gmt":"2023-08-15T19:26:46","guid":{"rendered":"https:\/\/labs.wsu.edu\/scads\/?page_id=897"},"modified":"2025-06-22T19:53:29","modified_gmt":"2025-06-23T02:53:29","slug":"journal-papers","status":"publish","type":"page","link":"https:\/\/labs.wsu.edu\/scads\/publications\/journal-papers\/","title":{"rendered":"Journal Papers"},"content":{"rendered":"\n

Journal Papers<\/h1>\n\n\n\n
    \n
  1. C. Soss, A. Rajam, J. Layne, E. Serra, M. Halappanavar and A. Gebremedhin.<\/strong> ScaWL: Scaling k-WL (Weisfeiler-Leman) Algorithms in Memory and Performance on Shared and Distributed-Memory Systems<\/a>, ACM Transactions on Architecture and Code Optimization<\/em> (Accepted Dec 2024). <\/li>\n\n\n\n
  2. O. Oje, T. Stirewalt, O. Amram, P. Hystad, S. Amiri and A. Gebremedhin.<\/strong> HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics<\/a>, ACM Transactions on Spatial Algorithms and Systems<\/em> (Accepted Sep 2024). <\/li>\n\n\n\n
  3. O. Oje, O. Amram, P. Hystad, A. Gebremedhin and P. Monsivais.<\/strong> Use of Individual Google Location History Data to Identify Consumer Encounters with Food Outlets<\/a>, International Journal of Health Geographics<\/em> 24(1) (2025). <\/li>\n\n\n\n
  4. O. Amram, O. Oje, A. Larkin, K. Baokye, A. Avery, A. Gebremedhin, B. Williams, G. Duncan and P. Hystad.<\/strong> Smartphone Google Location History: A Novel Approach to Outdoor Physical Activity Research<\/a>, Journal of Physical Activity and Health<\/em> 19 (2024).<\/li>\n\n\n\n
  5. S. Patil, S. Roberts and A. Gebremedhin.<\/strong> Network Analysis of Driver Genes in Human Cancers<\/a>, Frontiers in Bioinformatics<\/em> 4 (2024).<\/li>\n\n\n\n
  6. J. Halvorsen, C. Izurieta, H. Cai and A. Gebremedhin.<\/strong> Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review<\/a>, ACM Computing Surveys<\/em> 56(10), Article 257 (2024). <\/li>\n\n\n\n
  7. J. Halvorsen and A. Gebremedhin.<\/strong> Generative Machine Learning for Cyber Security, Military Cyber Affairs<\/em> 7(1), Article 4 (2024).<\/li>\n\n\n\n
  8. J. Briscoe, C. DeSmet, K. Wuestney, A. Gebremedhin, R. Fritz and D. J. Cook.<\/strong> Exploring Geriatric Clinical Data and Mitigating Bias with Multi-Objective Synthetic Data Generation for Equitable Health Predictions, Journal of Biomedical Engineering and Biosciences<\/em> (2024).<\/li>\n\n\n\n
  9. P. Hystad, O. Amram, O. Oje, A. Larkin, K. Boakye, A. Avery, A. Gebremedhin and G. Duncan.<\/strong> Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research<\/a>, Environmental Health Perspectives<\/em> 130(11), CID 117005 (2022). <\/li>\n\n\n\n
  10. X. Liu, M. Halappanavar, K. Barker, A. Lumsdaine and A. H. Gebremedhin.<\/strong> Direction-Optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis<\/a>, ACM Journal of Experimental Algorithmics<\/em> 27(1.12), 1\u201331 (2022). <\/li>\n\n\n\n
  11. S. Patil, H. Catanese, K. Brayton, E. Lofgren and A. H. Gebremedhin.<\/strong> Sequence Similarity Network Analysis Provides Insight into the Temporal and Geographical Distribution of Mutations in SARS-CoV-2 Spike Protein<\/a>, Viruses<\/em> 14, 1672 (2022). <\/li>\n\n\n\n
  12. L. Wang, J. Halvorsen, S. Pannala, A. Srivastava, A. H. Gebremedhin and N. Schulz.<\/strong> CPSyNet: A Tool for Generating Customized Cyber-Power Synthetic Network for Distribution System with Distributed Energy Resources, IET Smart Grid<\/em> 5(6), 463\u2013477 (2022).<\/li>\n\n\n\n
  13. L. Wang, A. Dubey, A. H. Gebremedhin, A. Srivastava and N. Schulz.<\/strong> MPC-Based Decentralized Voltage Control in Power Distribution Systems with EV and PV Coordination<\/a>, IEEE Transactions on Smart Grid<\/em> 13(4), 2908\u20132919 (2022). <\/li>\n\n\n\n
  14. J. Stachofsky, A. H. Gebremedhin and R. Crossler.<\/strong> Cast to Vote: A Socio-technical Network Analysis of an Election Smartphone Application, Digital Government: Research and Practice<\/em> <\/a>3(1), Article 3, 117 (2022).<\/li>\n\n\n\n
  15. Y. Du, G. Warnell, A. Gebremedhin, P. Stone and M. Taylor.\u00a0Lucid Dreaming for Experience Replay: Refreshing Past States with Current Policy<\/a><\/strong>,\u00a0Neural Computing and Applications (2021)<\/em>. https:\/\/doi.org\/10.1007\/s00521-021-06104-51R. <\/li>\n\n\n\n
  16. R. Saeedi, K.S. Sajan, K. Davies, A. Srivastava and A.H. Gebremedhin. An Adaptive Machine Learning Framework for Behind-the-Meter Load\/PV Disaggregationx<\/strong>, IEEE Transaction on Industrial Informatics, Vol 17, No 10, pp 7060-7069, 2021.<\/em><\/li>\n\n\n\n
  17. R. Saeedi, K. Sasani and A.H. Gebremedhin, Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Archictectures, Algorithms and Evaluation<\/a><\/strong>, Sensors, 20(7), 1932, 2020.<\/li>\n\n\n\n
  18. S. Norgaard, R. Saeedi and A.H. Gebremedhin, Multi-Sensor Time Series Classification for Activity Tracking Under Variable Length<\/a><\/strong>, IEEE Sensors Journal, Vol 20, No 5, 2701\u20132709, 2020.<\/li>\n\n\n\n
  19. A.H. Gebremedhin and A. Walther, An Introduction to Algorithmic Differentiation<\/a><\/strong>, WIREs Data Mining and Knowledge Discovery, 2020; 10:e1334. https:\/\/doi.org\/10.1002\/widm.1334<\/li>\n\n\n\n
  20. 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,<\/strong><\/a> BMC Bioinformatics (2018) 19:475.<\/li>\n\n\n\n
  21. R. Saeedi and A.H. Gebremedhin, A Signal-level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems<\/a><\/strong>, IEEE Transactions on Mobile Computing, Vol 19, Number 3, 513\u2013527, 2020. DOI: 10.1109\/TMC.2018.2878673.<\/li>\n\n\n\n
  22. Yunshu Du, Assefaw H. Gebremedhin, and Matthew E. Taylor. Analysis of University Fitness Center Data Uncovers Interesting Patterns, Enables Prediction<\/strong><\/a>. IEEE Transactions on Knowledge and Data Engineering, Vol 31, Issue 8, 1478\u20131490, 2019.<\/li>\n\n\n\n
  23. H. Catanese, C. Hauser and A.H. Gebremedhin. Evaluation of Native and Transfer Students\u2019 Success in a Computer Science Course<\/strong>. ACM Inroads, 9(2), 53\u201357, 2018.<\/li>\n\n\n\n
  24. 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<\/strong><\/a>, IEEE Transactions on Parallel and Distributed Systems<\/em>, 28(5), 1240\u20131256, 2017.<\/li>\n\n\n\n
  25. P. Hove, M.E. Chaisi, K.A. Brayton, H. Ganesan, H.N. Catanese, M.S. Mtshali, A.M. Mutshembele, M.C. Oosthuizen, and N.E. Collins, Co-infections with multiple genotypes of Anaplasma marginale<\/em> in cattle indicate pathogen diversity<\/strong>, Parasites & Vectors (2018).<\/li>\n\n\n\n
  26. Z.T.H. Khumalo, H.N. Catanese, N. Leisching, P. Hove, N.E. Collins, M.E. Chaisit, A.H. Gebremedhin, M.C. Oosthuizen and K.A. Brayton, Characterization of Anaplasma marginale subspecies centrale using msp1aS genotyping reveals wildfire reservoir<\/strong>, Journal of Clinical Microbiology 2016 54:10, 2503-2512.<\/li>\n\n\n\n
  27. H.N. Catanese, K.A. Brayton and A.H. Gebremedhin, RepeatAnalyzer: a tool for analysing and managing short-sequence repeat data<\/strong> <\/a>, BMC Genomics <\/em>2016 17:422. DOI: 10.1186\/s12864-016-2686-2<\/li>\n\n\n\n
  28. M. Wang, A.H. Gebremedhin and A. Pothen, Capitalizing on Live <\/em>Variables: New Algorithms for Efficient Hessian Computation via Automatic Differentiation<\/strong><\/a>, Mathematical Programming Computation <\/em>, 8(4), 393-433, 2016. DOI = 10.1007\/s12532-016-0100-3.<\/li>\n\n\n\n
  29. R.A. Rossi, D.F. Gleich and A.H. Gebremedhin, Parallel Maximum Clique Algorithms with Applications to Network Analysis<\/strong><\/a>, SIAM Journal on Scientific Cpmputing<\/em>, 37(5), pages C589\u2013C618, 2015.<\/li>\n\n\n\n
  30. B. Pattabiraman, M.A. Patwary, A.H. Gebremedhin, W. Liao and A. Choudhary, Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection<\/strong><\/a>, Internet Mathematics<\/em>, Vol 11, No 4-5, pp 421-448, 2015.<\/li>\n\n\n\n
  31. A.H. Gebremedhin, D. Nguyen, M.M.A. Patwary and A. Pothen, ColPack: Software for Graph Coloring and Related Problems in Scientific Computing<\/strong><\/a>, ACM Transactions on Mathematical Software, Vol 40, No 1, pp 1\u201331, 2013<\/em>. (http:\/\/dl.acm.org\/citation.cfm?doid=2513109.2513110)<\/a><\/li>\n\n\n\n
  32. U. Catalyurek, J. Feo, A.H. Gebremedhin, M. Halappanavar and A. Pothen, Graph Coloring Algorithms for Multi-core and Massively Multithreaded Architectures<\/strong><\/a>, Parallel Computing 38 (2012), 576-594<\/em>.<\/li>\n\n\n\n
  33. D. Bozdag, U. Catalyurek, A. Gebremedhin, F. Manne, E. Boman and F. Ozguner, Distributed-memory Parallel Algorithms for Distance-2 Coloring and Related Problems in Derivative Computation<\/strong><\/a>, SIAM Journal on Scientific Computing Vol 32, Issue 4, pp 2418\u20132446, 2010<\/em>.<\/li>\n\n\n\n
  34. A. Gebremedhin, A. Pothen, A. Tarafdar and A. Walther, Efficient Computation of Sparse Hessians Using Coloring and Automatic Differentiation<\/strong><\/a>, INFORMS Journal on Computing Vol 21, No 2, pp 209\u2013223, 2009<\/em>.<\/li>\n\n\n\n
  35. D. Bozdag, A. Gebremedhin, F. Manne, E. Boman and U. Catalyurek, A framework for Scalable Greedy Coloring on Distributed Memory Parallel Computers<\/strong><\/a>,Journal of Parallel and Distributed Computing Vol 68, No 4, pp 515\u2013535, 2008<\/em>.<\/li>\n\n\n\n
  36. A. Gebremedhin, A. Tarafdar, F. Manne and A. Pothen, New Acyclic and Star Coloring Algorithms with Applications to Hessian Computation<\/strong><\/a>, SIAM Journal on Scientific Computing, Vol 29, No 3, pp 1042\u20131072, 2007<\/em>.<\/li>\n\n\n\n
  37. A. Gebremedhin, M. Essaidi, I. Guerin-Lassous, J. Gustedt, J.A. Telle, PRO: A Model for the Design and Analysis of Efficient and Scalable Parallel Algorithms<\/strong><\/a>, Nordic Journal of Computing, Vol 13, pp 1\u201325, 2006<\/em>.<\/li>\n\n\n\n
  38. A. Gebremedhin, F. Manne and A. Pothen, What Color Is Your Jacobian? Graph Coloring for Computing Derivatives<\/a><\/strong>, SIAM Review, Vol 47, No 4, pp 629\u2013705, 2005<\/em>.<\/li>\n\n\n\n
  39. A. Gebremedhin, I.Guerrin-Lassous, J. Gustedt and J.A. Telle, Graph Coloring on Coarse Grained Multicomputers<\/strong><\/a>, Discrete Applied Mathematics, Vol 131, No 1, pp 179\u2013198, 2003.<\/em><\/li>\n\n\n\n
  40. A. Gebremedhin and F. Manne, Scalable Parallel Graph Coloring Algorithms<\/strong><\/a>, Concurrency: Practice and Expereince Vol 12, pp 1131\u20131146, 2000<\/em>.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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