The Institute of Transportation Studies Berkeley hosted its affiliated student PhD talks, highlighting innovative research from graduating doctoral candidates working at the intersection of transportation, energy, and urban systems.
From stabilizing traffic with a few automated vehicles to optimizing large-scale electric vehicle charging, the presentations explored real-world solutions to pressing mobility challenges. Additional talks examined policy pathways for aviation decarbonization and the role of public charging access in increasing EV adoption in dense cities.
The event showcased the breadth and impact of student research at ITS Berkeley, emphasizing data-driven, interdisciplinary approaches to building more sustainable and efficient transportation systems.
Zhe Fu
Ph.D. Candidate in Transportation Engineering
Physics-Informed Learning and Control for Mixed-Autonomy Systems: Enabling Traffic Smoothing with a Few Automated Vehicles
Abstract: Mixed-autonomy systems, where automated and human agents coexist, are already emerging in real-world cyber-physical systems. A key challenge in these systems is how to leverage a small number of automated agents to influence overall system behavior under nonlinear dynamics, behavioral uncertainty, and partial observability.
In this talk, I present a model-informed co-design framework that integrates physics-informed learning, control design, and real-world experimentation for mixed-autonomy systems. Using traffic flow smoothing as a specific case, I show how Neural Finite Volume Methods enable accurate and data-efficient modeling of traffic dynamics, and how kernel-based and imitation-learning control strategies allow a few automated vehicles to dissipate stop-and-go waves while maintaining throughput. These methods are validated through the largest scientific traffic field experiment to date, involving 100 automated vehicles on public highways, demonstrating measurable improvements in traffic stability and energy efficiency.
Bio: Zhe Fu is a final-year Ph.D. candidate in Transportation Engineering and M.S. candidate in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, advised by Prof. Alexandre Bayen at Berkeley Artificial Intelligence Research (BAIR) Lab. Her research focuses on learning, control, and modeling for distributed parameter systems, with an emphasis on mixed autonomy. She develops physics-informed neural models of hyperbolic PDEs, designs both model-based and data-driven control algorithms, and validates them through large-scale field experiments. Zhe has been recognized as a 2025 Eno Fellow and was the Runner-up in the 2025 Berkeley Grad Slam. Her research has received honors across communities, including First Place in the INFORMS Poster Competition (2023) and Rising Stars awards in Mechanical Engineering (2024 by CMU), EECS (2025 by MIT), and CPS (2025 by NSF). Her leadership, mentorship, and teaching efforts have been recognized by UC Berkeley and organizations such as ITS, CTF, WTS, EDGE in Tech, H2H8 and AAa/e. Learn more at https://fu-zhe.com/
Yi Ju
Ph.D. Candidate in Systems Engineering
Unveiling the full potential of mobility-aware coordinated electric vehicle charging in distribution power networks
Abstract: As electric vehicle (EV) adoption grows, charging demand becomes a major new source of stress on distribution power networks. In this talk, we ask a critical power infrastructure planning question: what is the maximum benefit that coordinated EV charging can provide for mitigating grid overloading risk at a regional scale? Answering this question is challenging because the problem is both behaviorally and computationally complex, involving millions of vehicles, charging decisions coupled over time, and vehicle movement across many feeders. We introduce MAC, a mobility-aware coordinated EV charging framework that models flexibility over the entire mobility trajectories. We develop an ADMM-based approach with custom subproblem solvers to solve the extremely large-scale problem efficiently. Using realistic Bay Area mobility trajectories and feeder data, we optimize one week of charging for about 2 million EVs across 1300+ feeders. The results show that the value of flexibility and coordination is enormous, with the potential to defer most overload-driven upgrades and avoid roughly 6 billion dollars in infrastructure costs.
Bio: Yi Ju is a PhD candidate in Systems Engineering at UC Berkeley, advised by Professor Scott Moura, while also pursuing an MS in Electrical Engineering and Computer Science. His research interests broadly lie in sustainable and intelligent energy systems, with applications in buildings, microgrids, transportation and other societal systems. His recent work focuses on electric vehicle (EV) charging infrastructure, from both technical (controls) and social (incentives, equity) perspectives. He received his bachelor’s degree from Tsinghua University in 2020, and was a visiting student at MIT in Spring 2025. He is a recipient of CTF Lipman Family Foundation Fellowship, and Berkeley Outstanding GSI Award.
Yati Liu
Ph.D. Candidate in Transportation Engineering. Department of Civil and Environmental Engineering
U.S. State Initiatives to Promote Aviation Decarbonization
Abstract: Aviation represents one of the most difficult sectors to decarbonize, particularly when subnational actors must take the lead in the absence of strong national policy. In the near term, sustainable aviation fuel (SAF) is widely viewed as the most viable pathway for emissions reduction, while longer-term transitions may rely on electric- and hydrogen-powered aircraft. This study examines how subnational policies can shape the development of sustainable aviation, with a primary focus on SAF as an early-stage and scalable mitigation strategy. This study proposes a unified welfare-economics framework to evaluate alternative policy instruments, including fuel taxes, blending mandates, and subsidies. Using a stylized economic model, the study first develops analytical insights, which are then evaluated using empirical inputs to assess how different policies affect emissions, costs, and overall welfare. The study contributes to understanding how subnational initiatives can accelerate decarbonization in hard-to-abate sectors such as aviation.
Bio: Yati Liu is a Ph.D. candidate in Transportation Engineering at the University of California, Berkeley, under the supervision of Professor Mark Hansen. She holds an M.S. in Transportation Engineering from UC Berkeley. Her research focuses on sustainable aviation, with an emphasis on the policy, economic, and operational challenges of decarbonizing the aviation sector. Her current work examines the technical and political feasibility of deploying sustainable aviation fuel (SAF), as well as electric- and hydrogen-powered aircraft in commercial aviation. She is the recipient of the 2024 Robert P. Wadell Endowed Fellowship for Engineering Innovation.
Michael A. Montilla
PhD Candidate in City & Regional Planning
Planning for Electric Vehicles in Cities
Abstract: Despite chargers clustering in metropolitan regions, there remains a scarcity of chargers in urban neighborhoods where residents lack dedicated parking. Ironically, urban drivers may be more inclined to use EVs due to low range anxiety, but without at-home charging, many forego adoption. I examined if public chargers relate to EV adoption in dense urban areas, a geography overlooked in existing research on EVs. Using panel regression to analyze comprehensive vehicle registrations and charger data from New York City, I find that the number of level 2 chargers by population at the Zip Code level related to higher EV registration rates relative to all vehicles. In contrast to previous research focusing on broader geographies, I do not find any significant indication that faster chargers related to EV adoption. These results suggest that residential proximity to public chargers influence EV adoption more than charger speeds in cities with limited residential parking.
Bio: Michael A. N. Montilla is a PhD candidate and instructor in the Department of City and Regional Planning at the University of California, Berkeley, and a researcher at the California Partners for Advanced Transportation Technology (PATH). His research focuses on how emerging transportation technologies such as ride-hailing, electric vehicles, and autonomous vehicles affect cities, urban transportation, and planning/design processes. He previously served as Urban Planning Program Manager at Columbia University, and will begin a new role as Assistant Professor of Urban and Regional Planning at the University of Florida in Fall 2026.