Mathematical Data Science – Opportunities and Challenges
In this talk, I will give an overview of the Mathematical Data Science program and describe the ONR funding model. I will discuss some of the scientific challenges that the program is addressing including: new tools for the analysis of large and complex datasets, importance of discovering causal dependences, and the reproducibility crisis in science. I will also discuss some of the new opportunities including: advances in human computing and collaboration, personalized learning, and collective decision making.
About the lecturer
Dr. Pedja Neskovic is a program officer at ONR where he oversees the mathematical data science, and computational methods for decision making programs. He is also an adjunct associate professor of brain science at Brown University and a visiting professor at Johns Hopkins University. Within the scope of the mathematical data science program, he is addressing various basic research problems. These include methods for the analysis of big and small data, analysis of complex networks such as social and brain networks, reproducibility in science, and multi-modal and multi-scale information integration. He is also interested in developing methods for computational decision making that are utilizing novel crowdsourcing and collaborative techniques.