iCarbonX's has decided to locate its main development center at Imagu Vision in Israel.
Chinese big data company iCarbonX hasAmitai founded Imagu Vision in 2005 with former Compugen employee Naomi Keren, who will be CEO of iCarbonX Israel. Compugen, whose current market market cap is $346 million, was based on a system that performed machine learning on a large quantity of information before this segment acquired the titles of "big data" and "machine learning," and that is more or less what Imagu Vision does.
Amitai: "In contrast to Compugen, Imagu Vision was financed by our personal resources, and never raised capital. At the beginning, we adjusted our capabilities to a variety of industries, including semiconductors, e-commerce (for example, identifying products for purchase and taking body measurements on order to fit clothes, and a little for the medical field, and we got financing from our revenue. Over the past two years, we have been focusing more on identifying medical images.
"For me, the combination of big data and medicine is an old dream from when I was at Compugen, and here I was able to close a circle. Joining iCarbonX, with its large databases and its vision, is making a dream come true in a way I never conceived."
Amitai: "Six months ago, Naom (Keren) met iCarbonX founder and CEO Dr. Jun Wang in a framework that made grants to combinations of entrepreneurs and researchers: 10 Chinese and 10 Israelis. It was a short step from there, and in my opinion, already at the first meetings, Jun himself made the decision. We have been employees of the Chinese company since August."
The entire company is in the stage of collecting information, creating partnerships, and development, and is not yet producing insights for its customers.
Among the companies doing big data for medicine, what is special about you?
"What is special is the integration of information collection and analyzing the information in a single entity. This enables iCarbonX to collect and store the information in a form that facilitates more effective analysis at a later stage