Ebad Ahmadzadeh, Ph.D.
About Ebad Ahmadzadeh, Ph.D.
Ebad Ahmadzadeh, Ph.D., serves as the Principal Machine Learning Engineer at Cohere Health, where he has worked since 2022. He holds a Ph.D. in Computer Science from the Florida Institute of Technology and has extensive experience in developing algorithms for decision support based on customer feedback.
Work at Cohere Health
Ebad Ahmadzadeh has been serving as a Principal Machine Learning Engineer at Cohere Health since 2022. In this role, he focuses on developing advanced machine learning algorithms to enhance decision support systems. His expertise in extracting insights from customer reviews and social media contributes to the company's mission of improving healthcare outcomes through data-driven solutions.
Education and Expertise
Ebad Ahmadzadeh holds a Doctor of Philosophy (Ph.D.) in Computer Science from the Florida Institute of Technology, where he studied from 2013 to 2018. He also earned a Master of Science in Computer Science from Wyższa Szkoła Informatyki i Zarządzania w Rzeszowie between 2009 and 2012. Additionally, he obtained a Bachelor's Degree in Electrical and Electronics Engineering from Guilan University, studying from 2002 to 2008. His educational background provides a strong foundation for his work in machine learning and data science.
Professional Background
Prior to his current position, Ebad Ahmadzadeh worked at Accent Technologies as a Principal Data Scientist from 2018 to 2022. He began his career there as a Data Scientist for a brief period in 2016. His experience also includes roles at the Florida Institute of Technology, where he served as a Co-Principal Investigator and Research Assistant, contributing to various research projects in the field of computer science.
Research Contributions
Ebad Ahmadzadeh has made significant contributions to research in machine learning and network efficiency. He co-authored a paper on the impact of movement on mobile device keystroke dynamics, which was presented at the Thirteenth Symposium on Usable Privacy and Security (SOUPS) in 2017. Additionally, he presented research on improving efficiency in large sparse networks at the 2016 IEEE International Conference on Big Data, showcasing his expertise in algorithm development and network analysis.