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Stephen Odaibo, MD, MS(Math), MS(Comp. Sci.)
CEO & Founder RETINA-AI Health, Inc. | Retina Specialist | Healthcare AI expert | Math/Comp Sci. | AI Engineer
Stephen Odaibo is a highly accomplished medical professional and entrepreneur with a diverse background in medicine, mathematics, and computer science. He is the CEO and Founder of RETINA-AI Health, Inc., a company focused on artificial intelligence applications in healthcare, particularly in ophthalmology.1
Educational Background
Stephen Odaibo holds multiple advanced degrees, including:
- MD (Doctor of Medicine)
- MS in Mathematics
- MS in Computer Science
He received his education from prestigious institutions, including Duke University.1
Professional Experience
RETINA-AI Health, Inc.: As the founder and CEO of RETINA-AI Health, Inc., Dr. Odaibo leads the company in developing AI-powered solutions for eye care.1 His role as Chief Software Architect likely involves overseeing the technical aspects of the company's AI systems and software development.
University of Houston College of Optometry: Dr. Odaibo also serves as an adjunct faculty member at the University of Houston College of Optometry.23 This position allows him to contribute his expertise to the academic field while maintaining his entrepreneurial pursuits.
Expertise and Skills
Stephen Odaibo's unique combination of medical knowledge and technical expertise makes him well-suited for his role in AI-driven healthcare:
- Medical Expertise: As an MD, he has a strong foundation in clinical medicine, particularly in ophthalmology.
- Technical Skills: His advanced degrees in mathematics and computer science provide him with the necessary background for developing AI applications in healthcare.
- Entrepreneurship: Founding and leading RETINA-AI Health demonstrates his business acumen and vision for applying AI to medical challenges.
Dr. Odaibo's work at RETINA-AI Health likely focuses on developing AI algorithms and software solutions to assist in the diagnosis and treatment of retinal diseases, leveraging his interdisciplinary background to bridge the gap between medicine and technology.
Highlights
JENSEN-SHANNON DIVERGENCE
In #AI and in other probabilistic sciences, we often need to compare two probability distributions, p and q. One nice way to do this is the Kullback-Leibler divergence (KL). However, KL is asymmetric in that KL(p|q) is typically not equal to KL(q|p). But sometimes we want symmetry though.
The Jensen-Shannon Divergence (DJS) is a symmetrization of KL. Think of DJS as the average of p and q's KL distances to the average of p and q. In fact, that's exactly what it is!
#Artificialintelligence #Mathematics
Honored to be serving as the Chair and as a speaker at the Cybersecurity for Medical Devices Summit. I am looking forward to engaging with the outstanding array of speakers who will be sharing their medical device cybersecurity expertise and experience.
My own talk will cover best practices for designing cybersecure and regulatory-compliant Artificial Intelligence medical devices.
It is going to be a great summit! See you all in Boston!
Find out more here: https://ter.li/uyuj6e
#MedicalDevices #Cybersecurity #Software
