Robert Mieth
Research
Interests:
• Power system reliability, operation, and planning
• Electricity markets and energy economics
• Renewable energy integration
• Economy-wide decarbonization and climate change mitigation
Methods:
• Optimization + machine learning
• Data-driven decision making
• Bilevel and equilibrium programming
Selected Honors and Awards
• Postdoctoral Fellowship, German Academy of Sciences Leopoldina, 2022
• Doctoral Fellowship, Reiner Lemoine-Foundation, Berlin, Germany, 2018-2019
• Doctoral mobility award, German Academic Exchange Service (DAAD), 2017-2018
• Fellow of the German National Academic Foundation, Bonn, Germany (Alumnus since)
Education
• 2021 PhD, Electrical Engineering, Technical University of Berlin, Germany, and New York University, NY, USA
• 2017 MS, Industrial Engineering, Technical University of Berlin, Germany
• 2017 MS, Electrical Engineering, Technical University of Berlin, Germany
• 2013 BS, Industrial Engineering, Technical University of Berlin, Germany
Brief Bio
Robert Mieth is an Assistant Professor in the Industrial and Systems Engineering Department at Rutgers University and the founder and PI of the Reliability, Operation, and Planning of Power and Energy Systems (ROPES) Lab. Before joining Rutgers in fall 2023, he was a Leopoldina Postdoctoral Fellow in the Electrical and Computer Engineering Department at Princeton University. From 2021 to 2022 he was a Postdoc in the Department of Electrical and Computer Engineering of New York University’s Tandon School of Engineering as part of the ARPA-E funded PERFOM project. Robert Mieth received the Doctorate in Engineering from the Technical University of Berlin in cooperation with NYU, where he was a visiting researcher from 2017 through 2020. His research and academic trajectory have been supported by prestigious fellowships including the German National Academic Foundation, the Rainer-Lemoine Foundation, and the German Academy of Sciences (Leopoldina). His research mainly focuses on facilitating clean electric power systems using optimization and machine learning methods.