Journal Papers

  1. Y. Du, G. Warnell, A. Gebremedhin, P. Stone and M. Taylor. Lucid Dreaming for Experience Replay: Refreshing Past States with Current Policy, Neural Computing and Applications (2021). https://doi.org/10.1007/s00521-021-06104-51R. Saeedi,
  2. K.S. Sajan, K. Davies, A. Srivastava and A.H. Gebremedhin. An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregationx, IEEE Transaction on Industrial Informatics, Vol 17, No 10, pp 7060-7069, 2021.
  3. R. Saeedi, K. Sasani and A.H. Gebremedhin, Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Archictectures, Algorithms and Evaluation, Sensors, 20(7), 1932, 2020.
  4. S. Norgaard, R. Saeedi and A.H. Gebremedhin, Multi-Sensor Time Series Classification for Activity Tracking Under Variable Length, IEEE Sensors Journal, Vol 20, No 5, 2701–2709, 2020.
  5. A.H. Gebremedhin and A. Walther, An Introduction to Algorithmic Differentiation, WIREs Data Mining and Knowledge Discovery, 2020; 10:e1334. https://doi.org/10.1002/widm.1334
  6. 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 (2018) 19:475.
  7. 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, Number 3, 513–527, 2020. DOI: 10.1109/TMC.2018.2878673.
  8. Yunshu Du, Assefaw H. Gebremedhin, and Matthew E. Taylor. 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.
  9. H. Catanese, C. Hauser and A.H. Gebremedhin. Evaluation of Native and Transfer Students’ Success in a Computer Science Course. ACM Inroads, 9(2), 53–57, 2018.
  10. 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, 28(5), 1240–1256, 2017.
  11. 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 in cattle indicate pathogen diversity, Parasites & Vectors (2018).
  12. 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, Journal of Clinical Microbiology 2016 54:10, 2503-2512.
  13. H.N. Catanese, K.A. Brayton and A.H. Gebremedhin, RepeatAnalyzer: a tool for analysing and managing short-sequence repeat data , BMC Genomics 2016 17:422. DOI: 10.1186/s12864-016-2686-2
  14. M. Wang, A.H. Gebremedhin and A. Pothen, Capitalizing on Live Variables: New Algorithms for Efficient Hessian Computation via Automatic Differentiation, Mathematical Programming Computation , 8(4), 393-433, 2016. DOI = 10.1007/s12532-016-0100-3.
  15. R.A. Rossi, D.F. Gleich and A.H. Gebremedhin, Parallel Maximum Clique Algorithms with Applications to Network Analysis, SIAM Journal on Scientific Cpmputing, 37(5), pages C589–C618, 2015.
  16. 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, Internet Mathematics, Vol 11, No 4-5, pp 421-448, 2015.
  17. A.H. Gebremedhin, D. Nguyen, M.M.A. Patwary and A. Pothen, ColPack: Software for Graph Coloring and Related Problems in Scientific Computing, ACM Transactions on Mathematical Software, Vol 40, No 1, pp 1–31, 2013. (http://dl.acm.org/citation.cfm?doid=2513109.2513110)
  18. U. Catalyurek, J. Feo, A.H. Gebremedhin, M. Halappanavar and A. Pothen, Graph Coloring Algorithms for Multi-core and Massively Multithreaded Architectures, Parallel Computing 38 (2012), 576-594.
  19. 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, SIAM Journal on Scientific Computing Vol 32, Issue 4, pp 2418–2446, 2010.
  20. A. Gebremedhin, A. Pothen, A. Tarafdar and A. Walther, Efficient Computation of Sparse Hessians Using Coloring and Automatic Differentiation, INFORMS Journal on Computing Vol 21, No 2, pp 209–223, 2009.
  21. D. Bozdag, A. Gebremedhin, F. Manne, E. Boman and U. Catalyurek, A framework for Scalable Greedy Coloring on Distributed Memory Parallel Computers,Journal of Parallel and Distributed Computing Vol 68, No 4, pp 515–535, 2008.
  22. A. Gebremedhin, A. Tarafdar, F. Manne and A. Pothen, New Acyclic and Star Coloring Algorithms with Applications to Hessian Computation, SIAM Journal on Scientific Computing, Vol 29, No 3, pp 1042–1072, 2007.
  23. 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, Nordic Journal of Computing, Vol 13, pp 1–25, 2006.
  24. A. Gebremedhin, F. Manne and A. Pothen, What Color Is Your Jacobian? Graph Coloring for Computing Derivatives, SIAM Review, Vol 47, No 4, pp 629–705, 2005.
  25. A. Gebremedhin, I.Guerrin-Lassous, J. Gustedt and J.A. Telle, Graph Coloring on Coarse Grained Multicomputers, Discrete Applied Mathematics, Vol 131, No 1, pp 179–198, 2003.
  26. A. Gebremedhin and F. Manne, Scalable Parallel Graph Coloring Algorithms, Concurrency: Practice and Expereince Vol 12, pp 1131–1146, 2000.