Total mentored projects: 9
Accurately filling in missing information in commodity flow data would allow us to design improved methods of systematic recycling.
Developing efficient and error-free machine learning algorithms would lead us to more equitable and informed decisions.
Studying how collecting and aggregating data from multiple people can best inform visual detection tasks.
Studying the benefit and cost structure of paper recycling methods will assist large corporations in improving their sustainability efforts.
Investigating better ways to collect opinions from people will improve data quality and motivate less-biased crowdsourcing results.
Modeling the optimizing techniques during extreme weather events will minimize the investment and operational costs of the electric power systems network.
Developing a prescriptive decision-making tool will help users set optimized economic, social and environmental goals for recycling waste materials.