Mahmoud Ebrahimkhani
About Mahmoud Ebrahimkhani
Mahmoud Ebrahimkhani is an AI Research Scientist I specializing in generative AI models for drug discovery and automated disease diagnosis.
Mahmoud Ebrahimkhani AI Research Scientist I
Mahmoud Ebrahimkhani holds the position of AI Research Scientist I. His professional focus is on developing generative AI models specifically tailored for structure-based small molecule drug discovery. This role involves leveraging advances in artificial intelligence to revolutionize the field of drug discovery by creating smarter and more efficient methodologies for identifying potential therapeutic molecules.
Mahmoud Ebrahimkhani Education Background
Mahmoud Ebrahimkhani earned both his M.Sc. and Ph.D. in Biomedical Engineering from Stony Brook University. This academic background provided a robust foundation in the principles of biomedical engineering, equipping him with the necessary skills to innovate in the intersecting fields of healthcare and technology. Additionally, he pursued postdoctoral studies at Northwestern University, further refining his expertise and contributing to cutting-edge research in the biomedical domain.
Mahmoud Ebrahimkhani Expertise in AI and Machine Learning
Mahmoud Ebrahimkhani’s expertise lies in the development of AI and machine learning workflows aimed at automated disease diagnosis and digital biomarker prediction. His work encompasses the creation of sophisticated algorithms and models that enhance the ability to diagnose diseases accurately and predict biomarkers digitally, significantly impacting the effectiveness and efficiency of modern healthcare practices.
Mahmoud Ebrahimkhani Generative AI Models
At the core of Mahmoud Ebrahimkhani’s research is the creation of generative AI models for structure-based small molecule drug discovery. This involves using AI to analyze and predict the structure and function of small molecules, which can then be used to design new drugs. His research aims to streamline the drug discovery process, making it faster and more cost-effective.