Biography

Gail Rosen received all her degrees from the Georgia Institute of Technology. She heads the Ecological and Evolutionary Signal-processing and Informatics (EESI) lab at Drexel, heads Drexel's Center for Biological Discovery from Big Data, and is the chair of the board of the University Research Computing Facility.  Her interests are in machine learning and prokaryotic interactions and evolution.  She serves on the editorial board of the BMC Microbiome journal.

She is co-organizer or the AIME Scholarship program which seeks to increase diversity in electrical and mechanical engineering.

Degrees / Education

• PhD, Electrical and Computer Engineering, Georgia Institute of Technology, 2006
• MS, Electrical and Computer Engineering, Georgia Institute of Technology, 2002
• BS, Electrical and Computer Engineering, Georgia Institute of Technology, 1999

Research Areas

• Health and Medicine
• Computation
• Engineering Education
• Systems Engineering

Research Interests

Areas of Study

• Computer Engineering
• Electrical Engineering
• Environmental Engineering
• Machine Learning

Academic Distinctions

• National Science Foundation CAREER award
• Drexel Bridge Gap Funding Award
• Drexel Equipment Award
• Drexel Faculty Career Development Award
• Louis and Bessie Stein Family Fellowship

Select Publications

• A Sequential Learning Approach for Scaling Up Filter-Based Feature Subset Selection, G Ditzler, R Polikar, G Rosen, IEEE transactions on neural networks and learning systems 29 (6), 2530-2544, 2018
• Analysis Methods for Shotgun Metagenomics, S Woloszynek, Z Zhao, G Ditzler, JR Price, ER Reichenberger, Y Lan, ..., Theoretical and Applied Aspects of Systems Biology, 71-112, 2018
• 16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses, S Woloszynek, Z Zhao, J Chen, GL Rosen, bioRxiv, 314260, 2018
• theseus-An R package for the analysis and visualization of microbial community data, JR Price, S Woloszynek, GL Rosen, CM Sales, bioRxiv, 295675, 2018
• Opportunities and obstacles for deep learning in biology and medicine, T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ..., bioRxiv, 142760, 2018
• Metagenomic characterization of ambulances across the USA, NB O’Hara, HJ Reed, E Afshinnekoo, D Harvin, N Caplan, G Rosen, ..., Microbiome 5 (1), 125<, 2017
• Comprehensive benchmarking and ensemble approaches for metagenomic classifiers, ABR McIntyre, R Ounit, E Afshinnekoo, RJ Prill, E Hénaff, N Alexander, ..., Genome biology 18 (1), 182, 2017

For a complete list go to Google Scholar.