Mathis Heyer
Ph.D. Student in Energy Science and Engineering, admitted Autumn 2024
Bio
Mathis Heyer, from Kiel, Germany, is pursuing a Ph.D. in Energy Science & Engineering at the Stanford Doerr School of Sustainability. He holds a bachelor’s degree in Mechanical Engineering and a master’s degree in Process Systems Engineering from RWTH Aachen University, Germany, as well as a master’s degree in Management Science and Engineering from Tsinghua University in Beijing.
His research in the Environmental Assessment and Optimization Group at Stanford (https://eao.stanford.edu/) focuses on advancing the understanding of complex energy and process systems through mathematical modeling and optimization. Mathis' work builds on his previous research experiences at the Climate Policy Lab at ETH Zurich and the Sustainable Reaction Engineering Group at Cambridge University. Outside of his academic pursuits, Mathis enjoys volunteering with organizations such as "Engineers Without Borders" and "Europe Meets School" both involved in promoting intercultural exchange.
Mathis has been recognized as a Klaus-Murmann Fellow by the Foundation of German Business (sdw) while at RWTH Aachen and is currently an ERP Fellow with the German Academic Scholarship Foundation and a recipient of the SGF Fellowship.
Education & Certifications
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B.Sc., RWTH Aachen University, Mechanical Engineering (2021)
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M.Sc., RWTH Aachen University, Process Systems Engineering (2024)
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M.Sc., Tsinghua University, Management Science and Engineering (2024)
All Publications
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Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part II: Compartmentalization and learning-based recalibration
COMPUTERS & CHEMICAL ENGINEERING
2026; 204
View details for DOI 10.1016/j.compchemeng.2025.109384
View details for Web of Science ID 001572492100001
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Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
IEEE INTERNET OF THINGS JOURNAL
2023; 10 (4): 3642-3663
View details for DOI 10.1109/JIOT.2022.3231363
View details for Web of Science ID 000967250600001
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Multilevel surrogate modeling of an amine scrubbing process for CO<sub>2</sub>capture
AICHE JOURNAL
2022; 68 (6)
View details for DOI 10.1002/aic.17705
View details for Web of Science ID 000782774500001
https://orcid.org/0009-0001-5061-4866