From building trips using Google Maps to finding common friends or colleagues in LinkedIn or Facebook, and to ensuring your favorite video streaming platform delivers your binge-worthy series without any lag, graphs and search algorithms play a key role in all of these.
Understanding how individuals move and interact are key to better modeling of global pandemics and allow us to better understand complex systems such as ant colonies. Here, computer science, through large scale simulations, allows us to model complexity at scale.
In this session, we will start by looking at several computer science concepts such as path planning, graph searches and parallel computation together with examples on how we can use them in real world scenarios and consider how such topics can be included in classroom activities

Facilitator: Dr. Stefan Robila is a Professor of Computer Science and the Director of the Computational Sensing Laboratory at Montclair State University. Dr. Robila has worked extensively with collection and analysis of hyperspectral data, and the development and implementation of computationally efficient feature extraction algorithms that use high performance computing. This work has now expanded into more general research and applications for large data sets. Robila is actively involved in the dissemination of knowledge related to undergraduate research. Involving CS faculty from eight different universities, he organized panel discussions on undergraduate research presented at computing conferences, in addition to presenting on multidisciplinary research. He has also engaged in research students at levels (K-12 including the Weston program, undergraduate and graduate) guiding them to publications and presentations and advising them as they sought graduate program admissions and scholarship awards.