Mahmud Hossain

Chemistry and Computer Science Building
El Paso Texas, 79968
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Curriculum Vitae


Mahmud Hossain
Assistant Professor, Computer Science

Dr. Hossain's interests are in data mining and machine learning with a focus on big data, artificial intelligence, text analytics, transfer learning, computational biology, visual analytics, and distributed and parallel computing. Along with the development of theoretical algorithmic frameworks, Dr. Hossain is interested in building prototypes to solve data mining problems in different disciplines. His experience in big data analytics and algorithmics spans application areas in biomedical science, biology, intelligence analysis, social network analysis, mechanical engineering, sustainability, and software engineering.

While advanced technology plays a major role in gathering as much data possible to construct richer databases for analysis in modern scientific, business and government sectors the collected data itself becomes overwhelming for the domain experts due to the massive information space to process. In addition to enormous data, heterogeneity hinders the expert's ability to discover useful information from the data sources. Dr. Hossain, in his research, focuses on automated algorithmic frameworks to aide experts analyze heterogeneous and multimodal data. One of his recent projects involve analysis of gene databases, millions of full research articles collected from multiple sources, and cancer ontologies to extend and explain existing cancer signaling pathways. Another project focuses on analyzing scientific trends using publication information collected from multiple digital libraries and prediction of emerging trends. Other recent projects involve a diverse set of datasets including twitter, news articles, social network, environmental impact factor datasets, and geospatial data. Dr. Hossain is interested in designing different types of unsupervised, supervised, and semi-supervised algorithmic models based on the data and expert's needs. Dr. Hossain has participated in many big data analytics projects funded by government entities including DARPA, NSF, DHS, and IARPA and business sectors including HP, NEC Laboratories America, Inc., ABB Inc., Rolls-Royces, and Chromalloy.


  1. Data mining (big data)
  2. Information retrieval
  3. Machine learning


  1. Cross-cutting: Emerging Technologies: Information Technology, Biotechnology & Nanotechnology