Data Scientist
Data Scientist

Biography

Ferdous is a data scientist in the Data Science and Machine Intelligence group at Pacific Northwest National Laboratory. He initially joined PNNL as a Linus Pauling Postdoctoral Fellow in June 2022. Ferdous’s research interest is in combinatorial scientific computing, where he develops efficient combinatorial algorithms for practical applications. He mainly focuses on computing under various constraints, including memory limitations, dynamic data, or approximation algorithms. The tools frequently employed by Ferdous are streaming, dynamic, parallel, and distributed algorithmic techniques. He is also interested in machine learning algorithms, especially how machine learning models can be applied or integrated with the traditional algorithmic framework. Ferdous is always keen to understand domain science problems and explore advanced computing techniques to solve the cutting-edge issues arising in domain science problems.

Research Interest

  • Graph analytics
  • Combinatorial optimization
  • Streaming algorithm
  • High-performance computing
  • Combinatorial scientific computing

Education

  • PhD in computer science, Perdue University
  • MS in computer science and engineering, Bangladesh University of Engineering and Technology
  • BS in computer science and engineering, Bangladesh University of Engineering and Technology

Publications

2024

  • Ferdous S.M., A. Pothen, and M. Halappanavar. 2024. "Streaming Matching and Edge Cover in Practice." In Proceedings of the 22nd International Symposium on Experimental Algorithms (SEA 2024), July 24-26, Vienna, Austria. Leibniz International Proceedings in Informatics (LIPIcs), 301, 12:1-12:22. PNNL-SA-197168. doi:10.4230/LIPIcs.SEA.2024.12
  • Ferdous S.M., R.W. Neff, B. Peng, S.S. Shuvo, M. Minutoli, S. Mukherjee, and K. Kowalski, et al. 2024. "Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing." In IEEE International Parallel and Distributed Processing Symposium (IPDPS 2024), May 27-31. 2024, San Francisco, CA, 241-252. Piscataway, New Jersey:IEEE. PNNL-SA-191110. doi:10.1109/IPDPS57955.2024.00029

2023

  • D'Ambra P., F. Durastante, S.M. Ferdous, S. Filippone, M. Halappanavar, and A. Pothen. 2023. "AMG Preconditioners based on parallel hybrid coarsening and multi-objective graph matching." In 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2023), March 1-3, 2023, Naples, Italy, 59-67. Piscataway, New Jersey:IEEE. PNNL-SA-181598. doi:10.1109/PDP59025.2023.00017
  • Xiang L., M.H. Khan, S.M. Ferdous, S. Aravind, and M. Halappanavar. 2023. "cuAlign: Scalable Network Alignment on GPU Accelerators." In Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W 2023), November 12-17, 2023, Denver, CO. New York Mls, New York:Association for Computing Machinery. PNNL-SA-170319. doi:10.1145/3624062.3625129