COLLOQUIUM Rahmatollah Beheshti Abstract Most of the health conditions are directly or indirectly resulted from humans’ decisions. These decisions are affected by a wide range of personal and environmental factors. While understanding health decision- making processes can lead to significant breakthroughs in both treatment and prevention of different diseases, due to their complex nature, our knowledge about many of these processes is very limited. Computational and data-driven techniques are increasingly considered as powerful options to fuse various types of data (such as biological and behavioral data) to understand these complexities. In this talk, Dr. Beheshti will present several projects from the areas of smoking and obesity research in which he has used complex systems and AI methods to study health behaviors. Specifically, he will talk about one of his recent projects studying the role of price in food decision-making. Dr. Rahmatollah Beheshti is a fourth-year postdoctoral fellow at the Johns Hopkins Bloomberg School of Public Health, with a joint appointment in the Department of Applied Math & Statistics at Johns Hopkins. He has a PhD in Computer Science and a Master in Artificial Intelligence and has been working in the area of Computational Epidemiology and Health Data Analytics for the past eight years. He has close to 20 first author full articles in these areas. Specifically, he has worked extensively on two major public health epidemics: smoking and obesity and has focused on very different aspects of these two, including the social, economic, environmental, and lately biological factors that affect those epidemics. Date: Mar. 30 2018 Time: 10:15-11:15 am Place: Innovation Center, Room 2277