- Harnessing the science of networks for deep learning

Network science for algorithm analysis and design

Since the turn of the century, two fields have emerged to have an extraordinary impact: Network Science and Deep Learning. Networks arise in almost every field and are rigorously studied through emerging mathematical and statistical techniques. Network science has revolutionized medicine, engineering, sociology, and biology and we believe artificial intelligence is next. The neuroscience and cognitive-science communities have widely adopted it as a leading strategy to understand brain function at both the microscopic and macroscopic levels. We are applying similar techniques to develop theoretical and practical methodologies to crack open the black box of deep learning.

Deep Learning for COmplex Systems & Networks

The merger of mathematical and statistical foundations, high-performance computers, and large data repositories has led to integrated machine-learning infrastructures that achieve unprecedented — even super-human — performance. While this field has predominantly focused on traditional datasets such as images and videos, there is immense need for AI algorithms for network-structured datasets, such as those describing complex biological, social, and technological systems. Leveraging over a decade of theoretical expertise in network science, we are developing unmatched deep-learning algorithms that cater specifically to data describing complex networks and systems.

 

 

stay tuned….

 

 

Contact

If you interested in discussing network science and deep-learning — for consulting services, introductory inquiries, or advanced research topics —, please contact me through through the form here.