Harvard John A. Paulson School of Engineering and Applied Sciences
Demba Ba is currently an Associate Professor of Electrical Engineering and Bioengineering with Harvard University, where he directs the CRISP group. His research interests lie at the intersection of high-dimensional statistics, optimization, and time-series analysis, with applications to neuroscience and multimedia signal processing. Recently, he has taken a keen interest in the connection between artificial neural networks and sparse signal processing, as a means to understand the principles of hierarchical representations of sensory signals in the brain, and to develop explainable AI. The aim of this research is to explain neural networks as inference algorithms in biologically-plausible mechanistic, generative, statistical models. On the one hand, the connection to mechanistic models lets us interpret neural networks and enables a theoretical study of their properties, via a study of the properties of the mechanistic model associated with a given architecture. On the other hand, the connection of mechanistic models to neural networks lets us leverage GPUs, and the computational infrastructure that has been developed to train neural networks, to solve inference and estimation problems that rely on mechanistic models of data. In 2016, he was the recipient of a Research Fellowship in Neuroscience from the Alfred P. Sloan Foundation.
Demba Ba received the B.Sc. degree in electrical engineering from the University of Maryland, College Park, MD, USA, in 2004, and the M.Sci. and Ph.D. degrees in electrical engineering and computer science with a minor in mathematics from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2006 and 2011, respectively. In 2006 and 2009, he was a Summer Research Intern with the Communication and Collaboration Systems Group, Microsoft Research, Redmond, WA, USA. From 2011 to 2014, he was a Postdoctoral Associate with the MIT/Harvard Neuroscience Statistics Research Laboratory, where he developed theory and efficient algorithms to assess synchrony among large assemblies of neurons.