About

Rohit Ghosh is a seasoned AI and data science professional with over 8 years of relevant experience. He is a graduate of Indian Institute of Technology, Bombay, and currently serves as the Founding Member and Chief Strategy Officer of Qure.ai, a company that has developed deep learning algorithms for radiology image processing. In his current role, Rohit is responsible for business development and strategy for specific geographies. Prior to his current position, Rohit worked as an AI Scientist and was deeply involved in building R&D products in computer vision. He has over 15 publications in multiple journals and conference proceedings, including the prestigious The Lancet, on the application of AI for non-contrast head CT scans. In addition to his work at Qure.ai, Rohit has been widely involved in mentoring and teaching data science students. He has collaborated with GreyAtom to record 100+ lectures on ML & AI on YouTube to be accessed freely by everyone. He is also a Mentor Of Change for Atal Innovation Mission, an initiative launched by NITI Aayog for mentoring school students. Before joining Qure.ai, Rohit worked as a Data Scientist for ListUp, a P2P buy-sell platform, and as a freelance data scientist for Data Science Labs, an NY-based EdTech MOOC provider startup for teaching data science courses. He has also worked for a Japanese investment bank Nomura. Rohit has a diverse tech stack, including AI, Data Science, Research Science, ML, P2P, Deep Learning, Computer Vision, and Big Data. Rohit has also worked with Google as a Google Developer Expert and is a co-author of a book that provides a comprehensive survey of techniques, technologies, and applications of Big Data and its analysis. He has authored chapters on the use of deep learning for recommender systems and theoretical introduction of deep learning. Overall, Rohit Ghosh is a highly skilled AI and data science professional with a passion for mentoring and teaching students. He has a proven track record of building R&D products and developing deep learning algorithms for radiology image processing.