Vinay Kumar Sankarapu

Automating Medical Imaging and Preliminary Diagnosis using Virtual Assistants built on multi-functional deep learning networks.

Deep learning has been producing amazing applications in the areas of text, language, speech, images, videos & analytics. However, till now, the applications of Deep Learning always have been singular and point focused. Most of the times in medicine, the interpretations need multi-tasking. For example, while arriving at a diagnosis, a doctor has to verify and gather data sources from various formats like imaging, sensor inputs & feedback from the patient. Point based AI system limit deeper understanding of medical situations. A robot with unified multi-functional deep neural networks can cross communicate the learning and can analyze the situation better. But what are the complexities in developing such networks? What kind of frameworks might work initially? Is it possible to have a modular approach in constructing a multi functional medical assistant? is a developer platform for artificial intelligence with deep learning tools in language, vision, text, speech, dialogues and reasoning. Using the modules in the platform, developers can build multi-tasking virtual systems that can be customized to a vast number of tasks. has been named recently in the Forbes Asia 30 under 30 list, selected for 'International Innovation awards' by Paris&co - innovation agency from Paris, & has been named as a top 4 next generation technology startup by Silicon Valley Forum. has been working in Deep Learning to automate the AI building process for faster adoption of this technology.

Vinay Kumar is a researcher in Nano-tech from IIT Bombay, developed prediction models in formation of nano-lenses with nano centimeter scale and that has accurately predicted with over 80% accuracy. He founded an AI platform along with his co-founder Deekshith Marla who is a researcher in NLP from the same institute. He also authored two books when he was 20y and published them in national and international medium. He also received an excellence award for his research at 21y from ISHRAE.

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