Daniel joined IDeA Labs in January 2012. He completed a B.S. with honors in Computer Science with a minor in Mathematics in 2014. Daniel also received the Nile R & Gail M Brown Scholarship. Daniel has interned at Google, Print Fulfillment Services, and Applied Minds. While at Google, he developed software to perform regression analysis of server utilization data across multiple dimensions. At Print Fulfillment Services, he researched and developed software to minimize the total cost of production in a factory by optimizing a mathematical formulation of their production process. At Applied Minds, Daniel’s work used machine learning techniques on large data sets in conjunction with an existing mechanistic model. Daniel’s research interests include mathematical optimization, dynamic systems, control theory, machine learning, artificial intelligence, and operations research. Daniel was accepted to a PhD program at Yale where he plans to study distributed control.