Demba Ba

Demba Ba is an Assistant Professor of Electrical Engineering and Bioengineering at Harvard University where he directs the CRISP group. He and his group develop mathematical and computational tools to elucidate the role of dynamic networks of neurons in phenomena such as anesthesia, sleep, the learning of fear and aging, and to enable more efficient signal representations that exploit the structure present in natural media such as audio, images, and video. In 2016, he received a Research Fellowship in Neuroscience from the Alfred P. Sloan Foundation. Prof. Ba received the B.Sc. degree in Electrical Engineering from the University of Maryland, College Park, 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 in 2006 and 2011, respectively. Prof. Ba is passionate about teaching, and eagerly incorporates Jupyter notebooks and the Python ecosystem in his teaching because of the unique opportunity they provide for interactive, web-based, teaching of content that has not traditionally leveraged scientific computing resources. In the School of Engineering and Applied Sciences, he spearheaded the development and deployment of the JupyterHub Notebook on Amazon AWS cloud for two classes ES155, Biological Signal Processing, and ES201, Decision Theory. His motivation was to bridge the traditional gap that has existed between theory/pen-and-paper and application/coding focused classes. ES155 and ES201 bridge this gap by bringing together under one umbrella data-friendly devices (a research-grade wearable), sophisticated signal processing/theory, and a Python-based scientific computing platform in the cloud powered by Jupyter notebooks. How can we enable under-served communities to develop their own tech services and solutions based on data? Prof. Ba believes that teaching data science in these communities, beginning at the high-school level, is one solution. The Jupyter Notebook is a cost-effective/affordable way to teach data science, which will help democratize access to data science and data-related tools.