2020-2021 TOTAL Scholars Announced
We’re delighted to announce that three ICME students have been named 2020-2021 TOTAL Innovation Scholars.
The TOTAL Innovation Scholarship program supports several PhD students each year who are applying their studies to areas of high interest to TOTAL, such as physical modeling, predictive analytics, data science, and high performance computing.
The Scholarships for 2020 are focused on two pathways related to the COOLER project at Stanford. The COOLER project is a collaboration between Stanford Land, Buildings and Real Estate (LBRE) organization and academia at Stanford to unlock needed energy management innovations. COOLER aims to improve building and campus level chilled water and energy management by extracting additional value from the distributed data, analytics, and control actuators available in the Stanford campus buildings through the application of advanced computational methods.
The first pathway is the core COOLER research initiative, focused on current research projects. The second pathway involves COOLER-related research to address longer term goals, where students will work on research targeted at unlocking new possibilities for the
COOLER project.
This Scholarship is made possible with the generosity of TOTAL. As a global multi-energy leader, Total is the fourth oil and gas company worldwide and a major player in low-carbon energies, with the ambition to be carbon neutral in 2050 (including Scope 3 emissions in Europe). They operate in 130 countries and employ 98,000 people. Total explores for, produces, transforms, markets and distributes energy in a variety of forms, to serve the end customer. Total’s strategy is to become the responsible energy major, providing affordable, reliable and clean energy to as many people as possible.
Honglin Yuan
Honglin is a 4th year PhD student in ICME, advised by professor Tengyu Ma. He received dual B.S. in Applied Math and Computer Science from Peking University. His research interest focuses on machine learning theory, in particular federated learning, deep learning and optimization theory. Honglin was supported by the TOTAL innovation Scholarship during 2019-2020.
Devansh Jalota
Devansh is a second year PhD student in Computational and Mathematical Engineering at Stanford University and is advised by Professor Marco Pavone and Professor Yinyu Ye. His research interests span algorithmic game theory, market design and optimization.
Yizhou Qian
Yizhou Qian is a third-year PhD student in ICME at Stanford University, where he is working with Professor Eric Darve from Department of Mechanical Engineering. His research interests include physics-based learning, data assimilation, uncertainty quantification and distributed runtime systems.
