Dr. Zeehasham Rasheed

Senior Partner, Mentor, Educator, Data Scientist, Advisor of Artificial Intelligence (AI) & Machine Learning (ML)

Dr. Zeehasham is a Principal Data Scientist with over 10 years of experience working in industry, educational institutes along with non-profit as well as commercial organizations. Zeehasham completed his PhD from the Department of Computer Science, George Mason University in May 2013.

He is also an Adjunct Professor at George Mason University and teaches graduate as well as undergraduate courses in Computer Science (CS) and Information Sciences and Technology (IST) departments, specifically in the area of Artificial Intelligence, Machine Learning and Big Data Analytics. He is currently working at Verizon, Virginia USA and leading a team of data scientists and engineers.

Publications
  1. Rangwala, H., Charuvaka, A., & Rasheed, Z. (2014, September). Machine learning approaches for metagenomics. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 512-515). Springer, Berlin, Heidelberg.

2. Rasheed, Z., & Rangwala, H. (2013, May). A map-reduce framework for clustering metagenomes. In 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (pp. 549-558). IEEE.

3. Rasheed, Z., & Rangwala, H. (2013, May). Mc-minh: Metagenome clustering using minwise based hashing. In Proceedings of the 2013 SIAM International Conference on Data Mining (pp. 677-685). Society for Industrial and Applied Mathematics.

4. Rasheed, Z., Rangwala, H., & Barbara, D. (2012, October). LSH-Div: Species diversity Estimation using locality sensitive hashing. In 2012 IEEE International Conference on Bioinformatics and iomedicine (pp. 1-6). IEEE.

5. Rasheed, Z., & Rangwala, H. (2012). Metagenomic taxonomic classification using extreme learning machines. Journal of bioinformatics and computational biology, 10(05), 1250015.

6. Rasheed, Z., Rangwala, H., & Barbara, D. (2012, April). Efficient clustering of metagenomic sequences using locality sensitive hashing. In Proceedings of the 2012 SIAM International Conference on Data Mining (pp. 1023-1034). Society for Industrial and Applied Mathematics.

7. Rasheed, Z., & Rangwala, H. (2011). TAC-ELM: Metagenomic Taxonomic Classification with Extreme Learning Machines. In BICoB (pp. 92-97).