Assefaw Gebremedhin, Publications

Journal Papers
Conference Proceedings
Volumes Edited
Book Chapters
Other Publications

• 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/s00521-021-06104-5
• 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, IEEE Transaction on Industrial Informatics, Vol 17, No 10, pp 7060-7069, 2021.
• R. Saeedi, K. Sasani and A.H. Gebremedhin. Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architectures, Algorithms and Evaluation, Sensors, 20(7), 1932, 2020.
• 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.
• 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, No 3, 513--527, 2020.
• A.H. Gebremedhin and A. Walther. An Introduction to Algorithmic Differentiation, WIREs Data Mining and Knowledge Discovery, 2020; 10:e1334.
• Y. Du, M. Taylor and A.H. Gebremedhin, 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.
• 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. https://doi.org/10.1186/s12859-018-2453-2.
• 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.
• 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.
• 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.
• 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
• 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.
• 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.
• 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.
• 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)
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• A. Gebremedhin and F. Manne, Scalable Parallel Graph Coloring Algorithms, Concurrency: Practice and Expereince Vol 12, pp 1131--1146, 2000.

• 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.
• 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.
• Y. Du, G. Warnell, A.H. Gebremedhin, P. Stone and M. Taylor, Work-in-progress: Corrected Self Learning via Demonstrations, Proceedings of the Adaptive and Learning Agents Workshop at AAMAS, 2020.
• S. Ghosh, M. Halappanavar, A. Kalyanaraman 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).
• X. Liu, J. Firos, M. Zalewski , M. Halapannavar, K. Baker, A. and A.H. Gebremedhin. Distributed Direction-optimizing Label Propagation for Community Detection, Proceedings of the 2019 IEEE High performance Extreme Computing using Conference (HPEC 2019). [2019 Graph Challenge Innovation Award] .
• S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman and A.H. Gebremedhin. miniVite: A Graph Analytics Benchmarking Tool for Massively Parallel Systems, ACM/IEEE Supercomputing (SC 2018) Workshop on Performance Modeling, Benchmarking and Simulation (PMBS 2018).
• S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman and A.H. Gebremedhin. Scalable Distributed-memory Community Detection using Vite, IEEE High Performance Extreme Computing Conference (HPEC 2018). [Student Innovation Award] .
• 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).
• 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).
• 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).
• S. Ghosh, M. Halappanavar, A. Tumeo, A. Kalyanaraman, H. Lu, D. Chavarria-Miranda, A, Kahn and A.H. Gebremedhin, Distributed Louvain Algorithm for Graph Community Detection, IEEE International Parallel and Dsitributed Processing Symposium (IPDPS 2018).
• K. Sasani, M. Namaki, Y. Wu and A.H. Gebremedhin. Multi-metric Graph Query Perfromance Prediction, International Conference on Database Systems for Advanced Applications (DASFAA 2018).
• K. Sasani, M. Namaki and A.H. Gebremedhin. Network Similarity Prediction in Timeevolving Graphs: A Machine Learning Approach, IPDPS Workshop on the Intersection of Graph Algorithms and Machine Learbing (GraML 2018).
• R. Saeedi, S. Norgarrd and A.H. Gebremedhin, A Closed-loop Deep Learning Architecture for Robust Activity Recognition using Wearable Sensors, IEEE International Conference on Big Data (IEEE BigData 2017).
• 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).
• 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).
• 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).
• 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).
• 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).
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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. .
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

• A.H. Gebremedhin, M. Patwary and F. Manne. Paradigms for Effective Parallelization of Inherently Sequential Graph Algorithms on Multi-Core Architectures in Handbook of Research on Methodolgies and Applications of Supercomputing, edited by V. Milutinovic and M. Kotlar, IGI Global, 2021.
• M. Ilic, R. Jaddivada and A.H. Gebremedhin. Unified Modeling for Emulating Electric Energy Systems: Toward Digital Twin That Might Work in Handbook of Research on Methodolgies and Applications of Supercomputing, edited by V. Milutinovic and M. Kotlar, IGI Global, 2021.

• A.H. Gebremedhin, E. Boman and B. Ucar (Eds.), 2016 Proceedings of the Seventh SIAM Workshop on Combinatorial Scientific Computing.
• T. Sørevik, F. Manne, R. Moe, and A. H. Gebremedhin (eds.), Applied Parallel Computing. New Paradigms for HPC in Industry and Academia, Para2000, Bergen, Norway, June 18--20, 2000, Proceedings, Lecture Notes in Computer Science 1947, Springer 2001.

• Erik Boman, Assefaw Gebremedhin and Sivan Toledo, SIAM Workshop on Combinatorial Scientific Computing Inaguratees Proceedings and Best Paper Award, SIAM News Dec 2016.
• M. Wang, A.H. Gebremedhin and A. Pothen, Performance Evaluation of Automatic Differentiation Algorithms for Hessian Computation, The Seventh International Conference on Algorithmic Differentiation (AD2016), Christ Church Oxford, UK, September 2016.
• M. Wang, A.H. Gebremedhin and A. Pothen, An Efficient Automatic Differentiation Algorithm for Hessians: Working with Live Variables , The Sixth SIAM Workshop on Combinatorial Scientific Computing (CSC14), Lyons, France, July 2014.
• A.H. Gebremedhin and A. Pothen, Combinatorial Mathematics and Algorithms at Exascale: Challenges and Promising Directions , SIAM Workshop on Exascale Applied Mathematics Challenges and Opportunities (EX14), Chicago, Illinois, USA, July 2014.
• A.H. Gebremedhin, U. Catalyurek, J. Feo, M. Halappanavar and A. Pothen, Multithreaded Graph Coloring Algorithms, The Fifth SIAM Workshop on Combinatorial Scientific Computing (CSC11), Dramstadt, Germany, May 2011.
• A.H. Gebremedhin, D. Nguyen, A. Tarafdar and A. Pothen, Ordering for Coloring and More, The Fourth SIAM Workshop on Combinatorial Scientific Computing (CSC09), Monterey, CA, Oct 2009.
• A.H. Gebremedhin, The Enabling Power of Graph Coloring Algorithms in Automatic Differentiation and Parallel Processing, Dagstuhl Seminar on Combinatorial Scientific Computing, Germany, Feb 2009.
• A.H. Gebremedhin, A. Pothen, A. Tarafdar and A. Walther, Sparse Hessian Computation using Automatic Differentiation, The Third SIAM Workshop on Combinatorial Scientific Computing, Costa Mesa, CA, February 2007.
• Assefaw Gebremedhin, The Third SIAM Workshop on Combinatorial Scientific Cmputing, SIAM News Volume 40, Number 4, May 2007.
• A. Pothen, A.H. Gebremedhin, F. Dobrian; E.G. Boman, K.D. Devine, B.A. Hendrickson; P. Hovland, B. Norris, J. Utke; U. Catalyurek; M.M. Strout; Combinatorial Algorithms for Petascale Science, SciDAC Review, Issue 5, pp 26--35, Fall 2007.

• A.H. Gebremedhin, Practical Parallel Algorithms for Graph Coloring Problems in Numerical Optimization, PhD Thesis, Dept of Informatics, University of Bergen, Norway, February 2003.
• A.H. Gebremedhin, Parallel Graph Coloring, MS Thesis, Dept of Informatics, University of Bergen, May 1999.