Candidates for the multiple biomedical and data science faculty position openings in the College of Computing will be visiting campus this semester, including Safa Elkefi.
Bio
Dr. Safa Elkefi is a distinguished scholar specializing in medical informatics, with a specific emphasis on cancer care support for marginalized and underserved populations. She earned her Ph.D. in Engineering Management from the Stevens Institute of Technology in May 2023, where she received the prestigious Fabrycky-Blanchard Award in recognition of her exemplary academic performance and outstanding research contributions.
In addition to her doctorate, Dr. Elkefi holds two master’s degrees—one in Industrial Engineering and another in Health Analytics and Healthcare Systems—along with a bachelor’s degree in Industrial Engineering.
Currently, Dr. Elkefi serves as a project director and Postdoctoral Research Scientist at Columbia University’s School of Nursing, where she is actively involved in advancing health informatics and patient-centered care. Her commitment to sharing knowledge extends beyond academia; she engages in public speaking and community outreach initiatives aimed at promoting STEM education and awareness. Dr. Elkefi’s contributions to her field have been acknowledged through numerous prestigious awards that celebrate her research achievements and leadership efforts. Furthermore, her dedication to education is evident in her role as an Adjunct Professor at the Stevens Institute of Technology, where she has developed and delivered courses aligned with her areas of expertise.
Candidate: Safa Elkefi
Dates of visit: January 23-24, 2025
Abstract
Accelerating Drug Discovery with Machine Learning
The design space of drug-like small molecules is of the order of 10^63. Optimizing this parameter space is a computationally intensive and near impossible challenge because it is large, discrete, and unstructured. However, recent advancements in machine learning, particularly deep learning, have enabled the development of continuous, data-driven molecular representations, transforming complex molecular structures into machine-readable formats.
In this talk, I will discuss how I conceptualize and build machine and deep learning frameworks capable of understanding, navigating, and optimizing this immense design space to discover and design novel drug candidates. I will also discuss how I leverage generative machine learning to generate novel RNAs optimized for specific functions. The foundational models grasp the chemical language of drugs and the biological language of RNAs, leading to the development of fine-tuned models that accelerate drug discovery and delivery. The recent application of machine learning to drug discovery benefits pharmaceutical researchers, chemists, and computational biologists by speeding up and optimizing the process, ultimately benefiting patients through the development of innovative and targeted therapies. In addition, I will discuss my research background and future research plans.
About the College of Computing
The Michigan Tech College of Computing, established in 2019, is the first academic unit in Michigan dedicated solely to computing, and one of only a handful such academic units in the United States. The college is composed of two academic departments. The Computer Science department offers four bachelor of science programs in computer science, cybersecurity, data science, and software engineering; four master of science programs in applied computer science, computer science, cybersecurity, and data science; and a doctoral program in computer science. The Applied Computing department offers four bachelor of science programs in cybersecurity, electrical engineering technology, information technology, and mechatronics; two master of science programs in health informatics and mechatronics; and a doctoral program is in computational science and engineering.
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