Naresh Rajendra Shah

Naresh Rajendra Shah

Naresh has over 8 years of development experience in the relatively nascent field of Deep Learning. He has worked with different types of solutions in the Deep Learning space from the days prior to Tensorflow and Pytorch where he used to build Deep Learning models on top of Theano/Caffe as frameworks. He is currently focused on nimble, agile solutions to accelerate value recognition of Generative AI solutions at Grid Dynamics. He has a Bachelor's in Electrical Engineering, BITS-Hyderabad, and a Master's in Business Analytics and Big Data, IE Business School. He has previously worked with different companies across fields like P&G, GE Healthcare, and his own startup, Untangle AI in the Explainable AI Space.

Naresh Rajendra Shah

Naresh has over 8 years of development experience in the relatively nascent field of Deep Learning. He has worked with different types of solutions in the Deep Learning space from the days prior to Tensorflow and Pytorch where he used to build Deep Learning models on top of Theano/Caffe as frameworks. He is currently focused on nimble, agile solutions to accelerate value recognition of Generative AI solutions at Grid Dynamics. He has a Bachelor's in Electrical Engineering, BITS-Hyderabad, and a Master's in Business Analytics and Big Data, IE Business School. He has previously worked with different companies across fields like P&G, GE Healthcare, and his own startup, Untangle AI in the Explainable AI Space.

Naresh Rajendra Shah

Generative AI in pharma and life sciences: Pragmatic applications and outcomes

The pharma and life sciences industry is experiencing a data revolution. As everything becomes more digital, the amount of biomedical data is exploding. This presents an opportunity to tap into valuable insights to advance R&D, manufacturing, marketing, and more. However, companies struggle to effectively analyze all this data. Generative AI is the solution. Unlike analytical