LIDS Graduate Students Andrea Goertzen and Sunbochen Tang with PI Navid Azizan have received the Best Paper Award at the 2026 Learning for Dynamics and Control (L4DC) Conference for their paper "ECO: Energy-Constrained Operator Learning for Chaotic Dynamics with Boundedness Guarantees.” Andrea and Sunbochen are equal-contributing student co-authors of the paper. The award was presented at the 8th Annual L4DC Conference, University of Southern California, on June 19, 2026.
Andrea Goertzen is a PhD student in LIDS and ChemE advised by Professor Azizan. Her research interests include machine learning for prediction and control of physical systems. In particular, she works on developing principled constraint-enforcement methods for physical fidelity, safety, and reliability in machine learning outputs. She is an NSF fellow and received her bachelor’s degree in Chemical Engineering from the University of Nebraska in 2023.
Sunbochen Tang is a PhD student in LIDS and AeroAstro advised by Professor Azizan. His research interests are broadly in machine learning, control theory, and optimization, with a focus on developing principled learning-based algorithms for trustworthy autonomous systems. Specifically, his recent work includes leveraging mathematical optimization and control theory to design hard-constrained neural networks, and improving the reliability and efficiency of meta-learning- and flow-matching-based decision-making algorithms, with applications to physical systems. He is a MathWorks fellow and obtained his MSc and BSc in mechanical engineering from the University of Michigan in 2021 and 2019, respectively.
The Alfred H. (1929) and Jean M. Hayes Career Development Professor in the Department of Mechanical Engineering and the Institute for Data, Systems & Society (IDSS), Navid Azizan is a Principal Investigator in LIDS and a core member of the Center for Computational Science and Engineering (CCSE).
Azizan’s research interests broadly lie in machine learning, systems and control, and mathematical optimization. His research lab focuses on various aspects of reliable intelligent systems, with an emphasis on principled learning and optimization algorithms, with applications to autonomy and sociotechnical systems. He obtained his PhD in Computing and Mathematical Sciences (CMS) from the California Institute of Technology (Caltech) in 2020, his MSc in electrical engineering from the University of Southern California in 2015, and his BSc in electrical engineering with a minor in physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University in 2021. Additionally, he was a research scientist intern at Google DeepMind in 2019. His work has been recognized by several awards, including the National Science Foundation CAREER Award and research awards from Amazon, Google, and MathWorks, among others. He was named in the list of Outstanding Academic Leaders in Data by the CDO Magazine for two consecutive years in 2024 and 2023, and received the 2020 Information Theory and Applications (ITA) “Sun” (Gold) Graduation Award. His teaching and mentorship have been recognized with the Joseph A. Martore (1975) Excellence in Teaching Award in 2026, the Frank E. Perkins Award for Excellence in Graduate Advising (MIT Institute Award) in 2025, and the UROP Outstanding Mentor Award in 2023.
About L4DC: The explosion of real-time data arising from devices that sense and control the physical world requires improving synergy in research areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking the foundations of the discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data-driven control and optimization of dynamical processes. The L4DC conference brings together a community of people who think rigorously across the disciplines, ask new questions, and develop the foundations of this new scientific area.
