Conference Papers

  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. X. Liu, M. Halappanavar, K. Baker, A. Lumsdaine and A.H. Gebremedhin, Direction-optimizing Label Propagation and its Application to Community Detection, Computing Frontiers, 2020.
  3. Y. Du, G. Warnell, A.H. Gebremedhin, P. Stone and M. Taylor, Work-in-Progress: Corrected Self Learning via Demostrations, Proceedings of the Adaptive and Learning Agents Workshop at AAMAS. May 2020.
  4. 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).
  5. X. Liu, J. Firos, 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, pp1– 6. [2019 Graph Challenge Innovation Award].
  6. S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman and A.H. Gebremedhin. miniVite: A graph Analytics Benchmarking Tool for Massively Parallel Systems, IEEE International Workshop on Performance Modeling, Benchmarking and Simulation (PMBS’18), held in conjunction with ACM/IEEE Supercomputing (SC’18).
  7. Sayan Ghosh, Mahantesh Halappanavar, Antonino Tumeo, Ananth Kalyanaraman and Assefaw H. Gebremedhin. Scalable Distributed Memory Community Detection using Vite. 2018 IEEE High Performance Extreme Computing Conference (HPEC’18).
  8. E. Khaledian, A.H. Gebremedhin, K. Brayton and S. Broschat. A Network Science Approach for Determining the Ancestral Phylum of Bacteria, ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2018).
  9. S. Norgaard, R. Saeedi, K. Sasani, and A.H. Gebremedhin, Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach, IEEE Engineering in Medicine and Biology Society Conference (EMBC 2018).
  10. 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).
  11. K. Sasani, MH. Namaki, Y. Wu, A.H. Gebremedhin, Multi-metric Graph Query Performance Prediction,  23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018).
  12. 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, IEEE International Parallel and Distributed Processing Symposium (IPDPS 2018).
  13. K. Sasani, MH. Namaki, A.H. Gebremedhin, Network Similarity Prediction in Time-evolving Graphs: A Machine Learning Approach, 32nd IEEE International Parallel and Distributed Processing Workshop on the Intersection of Graph Algorithms and Machine Learning (GraML 2018).
  14. R. Saeedi, S. Norgaard and A.H. Gebremedhin, A Closed-loop Deep Learning Architecture for Robust Activity Recognition using Wearable Sensors, 2017 IEEE International Conference on Big Data (BigData 2017).
  15. 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).
  16. M. H. Namaki, K. Sasani, Y. Wu and A.H. Gebremedhin, Performance Prediction for Graph Queries, ACM SIGMOD International Conference on Management of Data Workshop on Network Data Analytics (NDA 2017).
  17. 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 2016).
  18. 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).
  19. 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).
  20. H. Lu, M. Halappanavar, D. Chavarri a-Miranda, A.H. Gebremedhin and A. Kalyanaraman, Balanced Coloring for Parallel Computing Applications, IEEE International Parallel and Distributed Processing Symposium (IPDPS 2015), pp 7–16, 2015.
  21. R.A. Rossi, D.F. Gleich, A.H. Gebremedhin and M.M.A. Patwary, Fast maximum clique algorithms for large graphs , International Conference on World Wide Web (WWW’14), pages 365-366, 2014.
  22. 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 , WAW 2013: Algorithms and Models for the Web Graph, Lecture Notes in Computer Science 8305, pp 156–169, 2013.
  23. B. Letschert, K. Kulshreshtha, A. Walther, D. Nguyen, A.H. Gebremedhin and A. Pothen, Exploiting Sparsity in Automatic Differentiation on Multicore Architectures, 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.
  24. S.H.K Narayanan, B. Norris, P. Hovland and A.H. Gebremedhin. Implementation of Partial Separability in a Source to Source Transformation AD Tool, 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.
  25. M.M.A. Patwary, A.H. Gebremedhin and A. Pothen, New Multithreaded Ordering and Coloring Algorithms for Multicore Architectures, In E. Jeannot, R. Namyst and J. Roman, editors, Euro-Par 2011, Lecture Notes in Computer Science 6853, pages 250–262, Springer, 2011.
  26. S.H.K. Narayanan, B. Norris, P. Hovland, D. Nguyen and A.H. Gebremedhin, Sparse Jacobian Computation using ADIC2 and ColPack, Procedia Computer Science, 4:2115–2123, 2011. Proceedings of the International Conference on Computational Science, ICCS 2011.
  27. U. Catalyurek, F. Dobrian, A. Gebremedhin, M. Halappanavar and A. Pothen, Distributed-memory Parallel Algorithms for Matching and Coloring,Proceedings of IEEE International Parallel and Distributed Processing Symposium, Workshops and PhD Forums (IPDPSW), Workshop on Parallel Computing and Optimization (PCO’11), pages 1966–1975, 2011.
  28. 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, 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 339–349, 2008. .
  29. E. Boman, D. Bozdag, U. Catalyurek, K. Devine, A. Gebremedhin, P. Hovland and A. Pothen Combinatorial Algorithms for Computational Science and Engineering, Journal of Physics: Conference Series 125 (2008) 5 pp; SciDAC 2008.
  30. 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, Journal of Physics: Conference Series 78 (2007) 012058 (10 pp); SciDAC 2007.
  31. 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, Journal of Physics: Conference Series 46 (2006), 453–457; SciDAC 2006.
  32. 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, Lecture Notes in Computer Science, vol 3726, 2005, pages 796 – 806,Springer. Proceedings of HPCC 2005, Sept 21 – 25, 2005, Sorrento, Italy.
  33. E.G. Boman, D. Bozdag, U. Catalyurek, A.H. Gebremedhin and F. Manne, A Scalable Parallel Graph Coloring Algorithm for Distributed Memory Computers, Lecture Notes in Computer Science, vol 3648 , 2005, pages 241 – 251, Springer. Proceedings of EuroPar 2005, August 30–September 2, 2005, Lisboa, Portugal.
  34. A.H. Gebremedhin, F.Manne and T. Woods, Speeding up Parallel Graph Coloring, Lecture Notes in Computer Science, vol 3732, pp 1079-1088, 2005, Springer. Proceedings of Para 2004, June 20–23, 2004, Lyngby, Denmark.
  35. A.H. Gebremedhin, F. Manne and A. Pothen, Parallel Distance-k Coloring Algorithms for Numerical Optimization, In B. Monien and R. Feldmann (Eds.): EuroPar 2002, >Lecture Notes in Computer Science 2400, pp. 912-921, Springer-Verlag 2002.
  36. A.H. Gebremedhin, I. G. Lassous, J. Gustedt and J.A. Telle, PRO: a Model for Parallel Resource-Optimal Computation, In Proceedings of Symposium on High Performance Computing Systems and Applications (HPCS 2002), Moncton, NB, Canada, June 17–19, 2002, pages 106-113, IEEE Compter Society Press.
  37. A.H. Gebremedhin, I.G. Lassous, J. Gustedt and J.A. Telle, Graph Coloring on a Coarse Grained Multiprocessor, 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.
  38. A.H. Gebremedhin and F. Manne, Parallel Graph Coloring Algorithms using OpenMP, In Proceedings of the First European Workshop on OpenMP (EWOMP’99), Sept.30 – Oct. 1, 1999, Lund, Sweden.