High-Performance Algorithms for Mass Spectrometry-Based Omics

lt;p>Fahad Saeed is an Associate Professor with Tenure in the School of Computing and Information Sciences at Florida International University (FIU), Miami FL and is the director of Saeed Lab which is a parallel computing and data science group at FIU. Dr. Saeed's research interests are at the intersection of machine-learning, high performance computing and real-world applications, especially in computational biology.

Dr. Saeed has published 80+ peer-reviewed research papers in leading peer-reviewed proceedings, and journals, 1 Book Chapter, and edited 4 Conference Proceedings, and 3 special issue journals. His research is supported by highly competitive grants mainly from National Science Foundation (NSF) and National Institutes of Health (NIH). He has secured over US$ 2.7 Million (directly went to his lab) in external research funds as principal investigator and about US$ 2.61 Million overall since 2015. He was awarded the NSF Research Initiation Initiative (CRII) Award bestowed to young and promising scientists in the first two years of their tenure-track position. Most recently he was awarded the NSF Faculty Early Career Development (CAREER) Award which is NSF's most prestigious award in support of early-career faculty who have the potential to serve as academic role models in research and education. His research has been supported by WMU, NVIDIA, Intel/Altera, National Science Foundation (NSF) and National Institutes of Health (NIH).

Prior to joining FIU, Prof. Saeed was a tenure-track Assistant Professor in the Department of Electrical & Computer Engineering and Department of Computer Science at Western Michigan University (WMU), Kalamazoo Michigan since Jan 2014. He was tenured and promoted to the rank of Associate Professor at WMU in August 2018. Dr. Saeed was a Post-Doctoral Fellow and then a Research Fellow in the Systems Biology Center at National Institutes of Health (NIH), Bethesda MD from Aug 2010 to June 2011 and from June 2011 to January 2014 respectively. He received his PhD in the Department of Electrical and Computer Engineering, University of Illinois at Chicago (UIC) in 2010. He has served as a visiting scientist in world-renowned prestigious institutions such as Department of Bio-Systems Science and Engineering (D-BSSE), ETH Zürich, Swiss Institute of Bioinformatics (SIB) and  Epithelial Systems Biology Laboratory (ESBL) at National Institutes of Health (NIH) Bethesda, Maryland.

Dr. Saeed has established a global profile as an Independent Researcher and leader in the field, and has been sought as panelist at the National and International funding agencies. These include serving as panelist at various study sections at National Institutes of Health (NIH), National Science Foundation (NSF), NIH NIDDK, National Nuclear Security Administration (NNSA) Department of Energy (DOE), and as International expert and panelist for Croatian Science Foundation (CSF), University of Queensland Diamantina Institute in Australia, Belgium Fund for Scientific Research (F.R.S.- FNRS), and Natural Sciences & Engineering Research Council of Canada. He has served as the program co-chair of the Bioinformatics and Computational Biology (BICoB) Conference and IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). He is also a founding chair of IEEE Workshop on HPC solutions to Big Data Computational Biology (IEEE HPC-BCB). He also serves on the editorial board of Springer Journal of Network Modeling Analysis in Health Informatics and Bioinformatics, on the Editorial board of Journal of the American Society of Nephrology, and as Associate Editor for Frontiers of Digital Public Health (specialty section of Frontiersin Public Health, Frontiers in ICT and Frontiers in Computer Science). He has served on numerous IEEE/ACM program committees and is peer-reviewer for more than a two dozen journals.

Dr. Saeed is a Senior Member of ACM and also a Senior Member of IEEE. His honors

1. Need for High Performance Computing for Big MS Data.- 2. Introduction to Mass Spectrometry Data.- 3. A Review of Spectral Pre-processing.- 4. MS-REDUCE: An Ultra Data Reduction Algorithm.- 5. GPU-DAEMON: A Template to Support Development of GPU Algorithms.- 6. GPU-ArraySort: GPU Based Array Sorting Technique.- 7. G-MSR: A GPU Based Dimensionality Reduction Algorithm.- 8. Simulator Driven Proteomics.- 9. Future and Proposed Work.