I am currently an MSCA Global fellow at MIT, where I am exploring the application of nature-inspired algorithms to Deep Learning. The main idea is to devise new methods based on co-evolutionary algorithms to train and optimize Deep Neural Networks to improve the efficiency and efficacy of the state-of-the-art methodology, and therefore, allow the use of Deep Learning in problems that currently are difficult to address. We are focusing on Generative Models (such as GANs) and Recurrent Neural Networks. Besides, I am using this new Deep Learning methodology into two significant use cases for our society: cybersecurity and smart cities.
Postdoctoral Fellow in Massachusetts Institute of Technology
Jamal Toutouh
I am currently an MSCA Global fellow at MIT, where I am exploring the application of nature-inspired algorithms to Deep Learning. The main idea is to devise new methods based on co-evolutionary algorithms to train and optimize Deep Neural Networks to improve the efficiency and efficacy of the state-of-the-art methodology, and therefore, allow the use of Deep Learning in problems that currently are difficult to address. We are focusing on Generative Models (such as GANs) and Recurrent Neural Networks. Besides, I am using this new Deep Learning methodology into two significant use cases for our society: cybersecurity and smart cities.
