{"id":898,"date":"2023-08-15T12:46:06","date_gmt":"2023-08-15T19:46:06","guid":{"rendered":"https:\/\/labs.wsu.edu\/scads\/?page_id=898"},"modified":"2025-05-25T20:59:00","modified_gmt":"2025-05-26T03:59:00","slug":"conference-papers","status":"publish","type":"page","link":"https:\/\/labs.wsu.edu\/scads\/publications\/conference-papers\/","title":{"rendered":"Conference Papers"},"content":{"rendered":"\n

Conference Papers<\/h1>\n\n\n\n
    \n
  1. J. Briscoe, G. Kepler, D. Deford and A. Gebremedhin. Algorithmic Accountability in Small Data: Reliability and Fairness in Classification Metrics<\/strong>, International Conference on Artificial Intelligence and Statistics (AISTATS 2025), 2025.<\/li>\n\n\n\n
  2. J. Halvorsen, Y. Yan and A. Gebremedhin. Denoising Diffusion Implicit Models for Generating Cyber Defense Network Traffic<\/strong>, IEEE International Conference on Communications (ICC 2025), 2025.<\/li>\n\n\n\n
  3. J. Briscoe and A. Gebremedhin. Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning<\/a><\/strong>, ACM International Conference on Information and Knowledge Management (CIKM 2024), 2024.<\/li>\n\n\n\n
  4. J. Crabb, C. Hundhausen and A. Gebremedhin. A Critical Review of Cybersecurity Education in the United States<\/a><\/strong>, ACM Technical Symposium on Computer Science Education (SIGCSE 2024), 2024.<\/li>\n\n\n\n
  5. J. Briscoe, C. DeSmet, K. Wuestney, A. Gebremedhin, R. Fritz and D. J. Cook. Reducing Sample Selection Bias in Clinical Data through Generation of Multi-Objective Synthetic Data<\/strong>, 11th International Conference on Biomedical Engineering and Systems (ICBES 2024), 2024.<\/li>\n\n\n\n
  6. X. Liu, J. Firoz, S. Aksoy, I. Amburg, A. Lumsdaine, C. Joslyn, B. Praggastis and A. H. Gebremedhin. High-order Line Graphs of Non-uniform Hypergraphs: Algorithms, Applications, and Experimental Analysis<\/a><\/strong>, IEEE International Parallel and Distributed Processing Symposium (IPDPS 2022), 2022.<\/li>\n\n\n\n
  7. X. Liu, J. Firoz, A. H. Gebremedhin and A. Lumsdaine. NWHy: A Framework for Hypergraph Analytics: Representations, Data Structures, and Algorithms<\/a><\/strong>, IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW 2022), 2022.<\/li>\n\n\n\n
  8. X. Liu, J. Firoz, A. Lumsdaine, C. Joslyn, S. Aksoy, B. Praggastis and A. H. Gebremedhin. Parallel Algorithms for Efficient Computation of High-Order Line Graphs of Hypergraphs<\/a><\/strong>, IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2021), 2021.<\/li>\n\n\n\n
  9. G. Krishnamoorthy, A. Dubey and A. H. Gebremedhin. An Open-source Environment for Reinforcement Learning in Power Distribution Systems<\/a><\/strong>, IEEE Power & Energy Society General Meeting (PESGM 2022), 2022.<\/li>\n\n\n\n
  10. G. Krishnamoorthy, A. Dubey and A. H. Gebremedhin. Reinforcement Learning for Battery Energy Storage Dispatch Augmented with Model-based Optimizer<\/a><\/strong>, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm 2021), 2021.<\/li>\n\n\n\n
  11. J. Briscoe, A. Gebremedhin, L. Holder and D. Cook. Adversarial Creation of a Smart Home Testbed for Novelty Detection<\/strong>, AAAI Spring Symposium Series, 2022.<\/li>\n\n\n\n
  12. S. Ghosh, Y. Guo, P. Balaji and A.H. Gebremedhin.\u00a0RMACXX: An Efficient High-Level C++ Interface over MPI-3 RMA\u00a0<\/strong>, IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021), May 10-13, 2021, Melbourne, Australia.<\/li>\n\n\n\n
  13. X. Liu, M. Halappanavar, K. Baker, A. Lumsdaine and A.H. Gebremedhin, Direction-optimizing Label Propagation and its Application to Community Detection<\/a><\/strong>, Computing Frontiers, 2020.<\/li>\n\n\n\n
  14. Y. Du, G. Warnell, A.H. Gebremedhin, P. Stone and M. Taylor, Work-in-Progress: Corrected Self Learning via Demostrations<\/strong>, Proceedings of the Adaptive and Learning Agents Workshop at AAMAS. May 2020.<\/li>\n\n\n\n
  15. 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,<\/strong> IEEE International Parallel and Distributed Processing Symposium (IPDPS 2019).<\/li>\n\n\n\n
  16. X. Liu, J. Firos, M. Zalewski, M. Halappanavar, K. Baker, A. Lumsdaine and A.H. Gebremedhin, Distributed Direction-optimizing Label Propagation for Community Detection<\/a><\/strong>, 2019 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA, 2019, pp1\u2013 6. [2019 Graph Challenge Innovation Award].<\/li>\n\n\n\n
  17. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman and A.H. Gebremedhin. miniVite: A graph Analytics Benchmarking Tool for Massively Parallel Systems<\/strong>, IEEE International Workshop on Performance Modeling, Benchmarking and Simulation (PMBS\u201918), held in conjunction with ACM\/IEEE Supercomputing (SC\u201918).<\/li>\n\n\n\n
  18. Sayan Ghosh, Mahantesh Halappanavar, Antonino Tumeo, Ananth Kalyanaraman and Assefaw H. Gebremedhin. Scalable Distributed Memory Community Detection using Vite<\/strong><\/a>. 2018 IEEE High Performance Extreme Computing Conference (HPEC\u201918).<\/li>\n\n\n\n
  19. E. Khaledian, A.H. Gebremedhin, K. Brayton and S. Broschat. A Network Science Approach for Determining the Ancestral Phylum of Bacteria<\/strong>, ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2018).<\/li>\n\n\n\n
  20. S. Norgaard, R. Saeedi, K. Sasani, and A.H. Gebremedhin, Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach,<\/strong> IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018).<\/li>\n\n\n\n
  21. R. Saeedi, K. Sasani, S. Norgaard and A.H. Gebremedhin, Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer,<\/strong> IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018).<\/li>\n\n\n\n
  22. K. Sasani, MH. Namaki, Y. Wu, A.H. Gebremedhin, Multi-metric Graph Query Performance Prediction<\/strong><\/a>,  23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018).<\/li>\n\n\n\n
  23. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, H. Lu, D. Chavarria-Miranda, A. Khan and A. H. Gebremedhin, Distributed Louvain Algorithm for Graph Community Detection,<\/strong> IEEE International Parallel and Distributed Processing Symposium (IPDPS 2018).<\/li>\n\n\n\n
  24. K. Sasani, MH. Namaki, A.H. Gebremedhin, Network Similarity Prediction in Time-evolving Graphs: A Machine Learning Approach<\/strong><\/a>, 32nd IEEE International Parallel and Distributed Processing Workshop on the Intersection of Graph Algorithms and Machine Learning (GraML 2018).<\/li>\n\n\n\n
  25. R. Saeedi, S. Norgaard and A.H. Gebremedhin, A Closed-loop Deep Learning Architecture for Robust Activity Recognition using Wearable Sensors<\/strong><\/a>, 2017 IEEE International Conference on Big Data (BigData 2017).<\/li>\n\n\n\n
  26. R. Saeedi, K. Sasani and A.H. Gebremedhin, Co-MEAL: Cost-Optimal Multi-Expert Active Learning Architecture for Mobile Health Monitoring<\/strong><\/a>, ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2017).<\/li>\n\n\n\n
  27. M. H. Namaki, K. Sasani, Y. Wu and A.H. Gebremedhin, Performance Prediction for Graph Queries<\/strong><\/a>, ACM SIGMOD International Conference on Management of Data Workshop on Network Data Analytics (NDA 2017).<\/li>\n\n\n\n
  28. R. Saeedi, H. Ghasemzadeh and A.H. Gebremedhin Transfer Learning Algorithms for Autonomous Configuration of Wearable Systems<\/strong><\/a>, 2016 IEEE International Conference on Big Data (BigData 2016).<\/li>\n\n\n\n
  29. S. Ghosh and A.H. Gebremedhin Parallelization of Bin Packing on Multicore Systems<\/strong><\/a>, 2016 IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC\u201916).<\/li>\n\n\n\n
  30. 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<\/strong><\/a>, International Conference on Parallel Processing (ICPP 2016).<\/li>\n\n\n\n
  31. H. Lu, M. Halappanavar, D. Chavarri a-Miranda, A.H. Gebremedhin and A. Kalyanaraman, Balanced Coloring for Parallel Computing Applications<\/strong><\/a>, IEEE International Parallel and Distributed Processing Symposium (IPDPS 2015), pp 7\u201316, 2015.<\/li>\n\n\n\n
  32. R.A. Rossi, D.F. Gleich, A.H. Gebremedhin and M.M.A. Patwary, Fast maximum clique algorithms for large graphs <\/strong><\/a>, International Conference on World Wide Web (WWW\u201914), pages 365-366, 2014.<\/em><\/li>\n\n\n\n
  33. B. Pattabiraman, M.M.A Patwary, A.H. Gebremedhin, W.K. Liao, A. Choudhary, Fast Algorithms for the Maximum Clique Problem on Massive Sparse Graphs<\/strong> <\/a>, WAW 2013: Algorithms and Models for the Web Graph, Lecture Notes in Computer Science 8305, pp 156\u2013169, 2013.<\/em><\/li>\n\n\n\n
  34. B. Letschert, K. Kulshreshtha, A. Walther, D. Nguyen, A.H. Gebremedhin and A. Pothen, Exploiting Sparsity in Automatic Differentiation on Multicore Architectures<\/strong><\/a>, In S. Forth et al. (Eds.), Recent Advances in Algorithmic Differentiation (AD2012), Lecture Notes in Computational Science and Engineering 87, DOI 10.1007\/978-3-642-30023-3_14, 2012.<\/em><\/li>\n\n\n\n
  35. S.H.K Narayanan, B. Norris, P. Hovland and A.H. Gebremedhin. Implementation of Partial Separability in a Source to Source Transformation AD Tool<\/strong><\/a>, In S. Forth et al. (Eds.), Recent Advances in Algorithmic Differentiation (AD 2012), Lecture Notes in Computational Science and Engineering 87, DOI 10.1007\/978-3-642-30023-3_31, 2012.<\/em><\/li>\n\n\n\n
  36. M.M.A. Patwary, A.H. Gebremedhin and A. Pothen, New Multithreaded Ordering and Coloring Algorithms for Multicore Architectures<\/strong><\/a>, In E. Jeannot, R. Namyst and J. Roman, editors, Euro-Par 2011, Lecture Notes in Computer Science 6853, pages 250\u2013262, Springer, 2011.<\/em><\/li>\n\n\n\n
  37. S.H.K. Narayanan, B. Norris, P. Hovland, D. Nguyen and A.H. Gebremedhin, Sparse Jacobian Computation using ADIC2 and ColPack<\/strong><\/a>, Procedia Computer Science, 4:2115\u20132123, 2011. Proceedings of the International Conference on Computational Science, ICCS 2011.<\/em><\/li>\n\n\n\n
  38. U. Catalyurek, F. Dobrian, A. Gebremedhin, M. Halappanavar and A. Pothen, Distributed-memory Parallel Algorithms for Matching and Coloring<\/strong><\/a>,Proceedings of IEEE International Parallel and Distributed Processing Symposium, Workshops and PhD Forums (IPDPSW), Workshop on Parallel Computing and Optimization (PCO\u201911), pages 1966\u20131975, 2011<\/em>.<\/li>\n\n\n\n
  39. A. Gebremedhin, A. Pothen and A.Walther, Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: a Case Study in a Simulated Moving Bed Process<\/strong><\/a>, In C. Bischof et al. (Eds): Proceedings of the Fifth International Conference on Automatic Differentiation (AD 2008), Lecture Notes in Computational Science and Engineering 64, pp 339\u2013349, 2008. <\/em>.<\/li>\n\n\n\n
  40. E. Boman, D. Bozdag, U. Catalyurek, K. Devine, A. Gebremedhin, P. Hovland and A. Pothen Combinatorial Algorithms for Computational Science and Engineering<\/strong><\/a>, Journal of Physics: Conference Series 125 (2008) 5 pp; SciDAC 2008<\/em>.<\/li>\n\n\n\n
  41. E. Boman, D. Bozdag, U. Catalyurek, K. Devine, A. Gebremedhin, P. Hovland, A. Pothen and M.M. Strout, Enabling High Performance Computational Science through Combinatorial Algorithms<\/strong><\/a>, Journal of Physics: Conference Series 78 (2007) 012058 (10 pp); SciDAC 2007<\/em>.<\/li>\n\n\n\n
  42. S. Bhowmick, E. Boman, K. Devine, A. Gebremedhin, B. Hendrickson, P. Hovland, T. Munson and A. Pothen, Combinatorial Algorithms Enabling Computational Science: Tales from the Front<\/strong><\/a>, Journal of Physics: Conference Series 46 (2006), 453\u2013457; SciDAC 2006<\/em>.<\/li>\n\n\n\n
  43. D. Bozdag, U. Catalyurek, A.H. Gebremedhin, F. Manne, E. G. Boman and F. Ozguner, A Parallel Distance-2 Graph Coloring Algorithm for Distributed Memory Computers<\/strong><\/a>, Lecture Notes in Computer Science, vol 3726, 2005, pages 796 \u2013 806,Springer. Proceedings of HPCC 2005, Sept 21 \u2013 25, 2005, Sorrento, Italy.<\/em><\/li>\n\n\n\n
  44. E.G. Boman, D. Bozdag, U. Catalyurek, A.H. Gebremedhin and F. Manne, A Scalable Parallel Graph Coloring Algorithm for Distributed Memory Computers<\/strong><\/a>, Lecture Notes in Computer Science, vol 3648 , 2005, pages 241 \u2013 251, Springer. Proceedings of EuroPar 2005, August 30\u2013September 2, 2005, Lisboa, Portugal.<\/em><\/li>\n\n\n\n
  45. A.H. Gebremedhin, F.Manne and T. Woods, Speeding up Parallel Graph Coloring<\/strong><\/a>, Lecture Notes in Computer Science, vol 3732, pp 1079-1088, 2005, Springer. Proceedings of Para 2004, June 20\u201323, 2004, Lyngby, Denmark.<\/em><\/li>\n\n\n\n
  46. A.H. Gebremedhin, F. Manne and A. Pothen, Parallel Distance-k Coloring Algorithms for Numerical Optimization<\/strong><\/a>, In B. Monien and R. Feldmann (Eds.): EuroPar 2002, >Lecture Notes in Computer Science 2400, pp. 912-921, Springer-Verlag 2002<\/em>.<\/li>\n\n\n\n
  47. A.H. Gebremedhin, I. G. Lassous, J. Gustedt and J.A. Telle, PRO: a Model for Parallel Resource-Optimal Computation<\/strong><\/a>, In Proceedings of Symposium on High Performance Computing Systems and Applications (HPCS 2002), Moncton, NB, Canada, June 17\u201319, 2002, pages 106-113, IEEE Compter Society Press<\/em>.<\/li>\n\n\n\n
  48. A.H. Gebremedhin, I.G. Lassous, J. Gustedt and J.A. Telle, Graph Coloring on a Coarse Grained Multiprocessor<\/strong><\/a>, In Brandes, Ulrik, Wagner and Dorothea (Eds.): Workshop on Graph-Theoretic Concepts in Computer Science (WG 2000), Lecture Notes in Computer Science 1928, pp. 184-195, 2000, Springer-Verlag.<\/em><\/li>\n\n\n\n
  49. A.H. Gebremedhin and F. Manne, Parallel Graph Coloring Algorithms using OpenMP<\/strong><\/a>, In Proceedings of the First European Workshop on OpenMP (EWOMP\u201999), Sept.30 \u2013 Oct. 1, 1999, Lund, Sweden.<\/em><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Conference Papers<\/p>\n","protected":false},"author":12156,"featured_media":0,"parent":862,"menu_order":2,"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\/898"}],"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\/12156"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/comments?post=898"}],"version-history":[{"count":6,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/898\/revisions"}],"predecessor-version":[{"id":1372,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/898\/revisions\/1372"}],"up":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/pages\/862"}],"wp:attachment":[{"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/media?parent=898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/categories?post=898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/tags?post=898"},{"taxonomy":"wsuwp_university_category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/university_category?post=898"},{"taxonomy":"wsuwp_university_location","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/location?post=898"},{"taxonomy":"wsuwp_university_org","embeddable":true,"href":"https:\/\/labs.wsu.edu\/scads\/wp-json\/wp\/v2\/organization?post=898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}