Kareem Aggour

PhD, Computer Science (Rensselaer Polytechnic Institute) MS, Computer Engineering (Rensselaer Polytechnic Institute) BS, Computer Science (University of Maryland, College Park) BS, Electrical Engineering (University of Maryland, College Park)

Kareem is a Principal Scientist in the AI technology discipline at GE Research. He joined GE in 1998 and transferred to GE Research in 2000.

Kareem develops Big Data and knowledge discovery solutions to solve a wide range of digital industrial challenges. For example, he led the development of a scalable Big Data infrastructure accelerating Aviation Digital Twin analytic executions by 100-2,000x. In addition, Kareem led the development of a Big Data platform reducing the runtime of next-generation DNA sequencing data analysis pipelines from weeks to hours. Currently, he is working with GE Additive leading the development of a knowledge-driven federated Big Data storage and analytics platform for capturing and analyzing data across the additive manufacturing lifecycle.

Kareem holds two BS degrees from the University of Maryland, College Park.  He earned an MS in Computer Engineering and a PhD in Computer Science from RPI.  He has over 30 refereed publications and over 15 issued patents.

I love working with smart people to tackle challenging multidisciplinary problems in order to improve peoples lives.

Publications

  1. Kumar, V.S., Williams, J.W., Aggour, K.S., Sarachan, B., Al-Kofahi, Y. and Santamaria-Pang, A., 2015. Collaborative Analysis of High-Content Image Data. In NIST BioImage Informatics Conference.
  2. Cuddihy, P., McHugh, J., Williams, J.W., Mulwad, V. and Aggour, K., SemTK: A Semantics Toolkit for User-friendly SPARQL Generation and Semantic Data Management.
  3. Williams, J.W., Cuddihy, P., McHugh, J., Aggour, K.S., Menon, A., Gustafson, S.M. and Healy, T., 2015, October. Semantics for Big Data access & integration: Improving industrial equipment design through increased data usability. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 1103-1112). IEEE.
  4. Cuddihy, P., McHugh, J., Williams, J.W., Mulwad, V. and Aggour, K.S., 2017. SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs. arXiv preprint arXiv:1710.11531.
  5. Williams, J.W., Aggour, K.S., Interrante, J., McHugh, J. and Pool, E., 2014, October. Bridging high velocity and high volume industrial big data through distributed in-memory storage & analytics. In 2014 IEEE International Conference on Big Data (Big Data) (pp. 932-941). IEEE.
  6. Aggour, K.S., Kumar, V.S., Sangurdekar, D.P., Newberg, L.A. and Kodira, C.D., 2015, November. A highly parallel next-generation DNA sequencing data analysis pipeline in Hadoop. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on (pp. 756-763). IEEE.
  7. Aggour, K.S. and Yener, B., 2016, December. Adapting to data sparsity for efficient parallel PARAFAC tensor decomposition in Hadoop. In Big Data (Big Data), 2016 IEEE International Conference on (pp. 294-301). IEEE.
  8. Aggour, K.S., Williams, J.W., McHugh, J. and Kumar, V.S., 2017. Colt: concept lineage tool for data flow metadata capture and analysis. Proceedings of the VLDB Endowment, 10(12), pp.1790-1801.
  9. McHugh, J., Cuddihy, P.E., Williams, J.W., Aggour, K.S., Kumar, V.S. and Mulwad, V., 2017, December. Integrated Access to Big Data Polystores through a Knowledge-driven Framework. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 1494-1503). IEEE.

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