Research at the Interface of Applied Mathematics and Machine Learning
CBMS Conference
Department of Mathematics, University of Houston
The Department of Mathematics at the University of Houston will be hosting the CBMS Conference: Research at the Interface of Applied Mathematics and Machine Learning from 12/08/2025 to 12/12/2025.
Principal Lecturer
The principal lecturer of the conference is Dr. Lars Ruthotto from Emory University.
Show Narrative CV
Dr. Ruthotto is an internationally recognized leading researcher in scientific computing, inverse problems, PDE-constrained optimization, machine learning, and mathematical and numerical methods for deep learning. He is a Winship Distinguished Research Associate Professor in the Department of Mathematics and the Department of Computer Science at Emory University. He received his Ph.D. in Mathematics from the University of Münster in Germany in 2012. After his PhD, he joined the University of British Columbia for a Postdoctoral Research Fellowship from 2013 through 2014. He became a faculty at Emory University in 2014. He was a Senior Fellow at the Institute for Pure and Applied Mathematics in the long program on Machine Learning for Physics and Physics of Learning in 2019. He also held a position as Senior Consultant at XTract Technologies Inc., a company that provides artificial intelligence technologies to telecommunications, transportation, health care, and environmental sectors, from 2017 through 2019. He leads the Emory REU/RET site for Computational Mathematics for Data Science. Dr. Ruthotto received the NSF Career Award in 2018.
Dr. Ruthotto has a track record of top-tier publications in areas such as computational mathematics, optimal control, machine learning and optimization. He has given over 60 invited talks at top research universities and flagship meetings in applied and computational mathematics. He has also contributed to several conferences and workshops. He was the co-chair of the SIAM Conference on Mathematics of Data Science in 2022 and a co-organizer of the workshops on Theory of Deep Learning at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge and Optimization under Uncertainty: Learning and Decision Making at the Banff International Research Station both in 2021.
His professional service includes chairing the SIAM Activity Group on the Mathematics of Data Science, being a section editor in the Machine Learning Methods for Scientific Computing section at the SIAM Journal on Scientific Computing, and being an associate editor at SIAM Review in the research spotlight section.