Akshay Rajeev
About Akshay Rajeev
Akshay Rajeev is a Deep Learning Research Scientist currently developing predictive models for cardiovascular event prediction at Elucid. He has a background in Electronics and Communications Engineering and Computer Science, with previous roles in academia and industry.
Current Role as Deep Learning Research Scientist
Akshay Rajeev currently works at Elucid as a Deep Learning Research Scientist. He has been in this role since 2021 and has focused on developing predictive models specifically for cardiovascular event prediction. His expertise in deep learning contributes to advancements in healthcare technology.
Previous Experience at Elucid
Prior to his current position, Akshay Rajeev worked at Elucid as a Deep Learning Engineer for four months in 2021. During this time in Boston, Massachusetts, he contributed to various projects that leveraged deep learning techniques to enhance predictive analytics in healthcare.
Academic Background and Education
Akshay Rajeev earned a Bachelor's degree in Electronics and Communications Engineering from Sir M Visvesvaraya Institute of Technology in Bangalore, where he studied from 2013 to 2017. He later pursued a Master of Science in Computer Science at the Illinois Institute of Technology, completing his studies from 2019 to 2021.
Contributions to Research and Development
During his academic tenure at the Illinois Institute of Technology, Akshay Rajeev served as a Graduate Student Assistant and a Graduate Teaching Assistant, where he supported various research initiatives. He also played a significant role in the HDHRP and FFRct prognostic modeling project, which resulted in a Series B investment of $27 million.
Professional Experience in Software Development
Before joining Elucid, Akshay Rajeev worked as a Software Engineer at Danske IT and Support Services India Pvt Ltd from 2017 to 2019. He also held positions at the Illinois Institute of Technology as a Software Developer and contributed to the design of a data curation pipeline for unlabelled data, involving over 6000 vessels for unsupervised model preprocessing.