Title: Machine-learning Models, and High-Performance Computing (HPC) for Big Data Omics: Recent Progress and Challenges
Date and Time of Talk: | 16-December-2021 | 11:00 – 11:50 |
Abstract
Machine learning (ML) has emerged as a discipline that enables assistance to humans in making sense of big data sets from large and complex systems biology experiments. Drop in the cost of producing the data has made these large data sets accessible to researchers for various investigations. Analyzing these complex and big data sets is not trivial, and classical algorithms and models cannot fully explore the full potential that can move us closer to personalized medicine. Machine learning algorithms can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of medicine and improve health care. There is an increasing interest in the potential of ML to create predictive models and to identify complex patterns from omics datasets.
In this talk, we will discuss the challenges in developing machine-learning models for complex Mass Spectrometry based Proteomics data. The newly developed machine-learning tools are drastically changing the way that we analyze the proteomics data. Further, we will discuss the challenges, and opportunities in developing machine-learning models for big brains, and their role in quantifying, and diagnosing mental disorders. Lastly, we will discuss the opportunities and progress in developing high performance computing solutions for these machine-learning models for efficient, and timely computations.
Biography
Fahad Saeed is an Tenured Associate Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), Miami FL. His research interests include parallel and distributed algorithms and architectures, computational proteomics & genomics and big data problems in computational biology and bioinformatics. 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 is a Senior Member of ACM and also a Senior Member of IEEE. His honors include ThinkSwiss Fellowship (2007,2008), NIH Postdoctoral Fellowship Award (2010), Fellows Award for Research Excellence (FARE) at NIH (2012), NSF CRII Award (2015), WMU Outstanding New Researcher Award (2016), WMU Distinguished Research and Creative Scholarship Award (2018), NSF CAREER Award (2017), and FIU Excellence in Applied Research award (2020).