Jungwon Choi
Assistant Professor of Electrical & Computer Engineering Jungwon Choi's research interests include high-frequency power converters, wireless power transfer for battery-powered vehicles, industrial and biomedical applications, magnetic designs, controls at high-frequencies, energy storage, and wide bandgap devices. In 2017, she was selected to the Rising Stars in EECS, received Unlock Idea awards from Lam Research in 2019 and 2020, and the National Science Foundation (NSF) CAREER award in 2021. She is an Associate Editor of the IEEE Journal of Emerging and Selected Topics in Industrial Electronics and a member of several IEEE committees. Email | LinkedIn...
Matthew Motoki
I am a second-year Ph.D. student in Electrical & Computer Engineering at the University of Washington, advised by Professor Baosen Zhang. My research interests lie at the intersection of machine learning and distributed energy resources. Outside of my academic pursuits, I enjoy participating in machine learning competitions on Kaggle where I am a Competitions Grandmaster. ...
June Lukuyu
June Lukuyu is an assistant professor of Electrical & Computer Engineering (ECE) at the University of Washington. She joined the UW in January 2023 after completing her Ph.D. at the University of Massachusetts-Amherst. She is an Energy for Growth Hub Fellow. Lukuyu's research focuses on developing and planning for inclusive energy systems and innovative technologies in underserved communities, centering on promoting sustainability, social development, and human empowerment. Her work uses a wide range of data analytics, computing techniques, and social science methods to build models for integrated energy development, and systems planning, with model outputs aimed at informing energy, climate, and development decision-making. LinkedIn...
Dan Sturm
My research focuses on mitigating the rapidly growing energy cost of artificial intelligence computation by developing a new ultra-low power compute approach using photonic integrated circuits (PICs). PICs - microchips that let light flow instead of electricity - have become increasingly popular since light can carry higher data bandwidths while consuming far less power. As a result, they offer a promising path towards powerful but energy-efficient AI accelerators. We minimize energy consumption by using programmable phase change material that can store data on-chip at zero energy, and organize PCM-based compute cells in a specialized architecture (systolic array) for efficient data reuse. In the past...
Rose Johnson
The study of perovskite materials for clean energy harvesting and light emission is an emergent and highly promising field that has realized groundbreaking progress in efficiency over the past several years. For energy-efficient displays, cesium-based perovskite quantum dots are a well-suited and desirable material due to their high photoluminescence quantum yield, color tunability, and facile solution processibility. I plan to develop a direct photolithography process to pattern and create a balanced, full-color, and ultra-bright micro-LED display. Incorporation of polymer ligands will address stability and enable direct patterning of the perovskite active layer, thus greatly simplifying the micro-LED fabrication process and increasing production throughput. This project...
Aaron Gehrke
Thin-film photovoltaics such as Cu(In,Ga)Se2 (CIGS) and CdTe are among the most promising solar cell technologies. We study these materials using computational materials methodologies, such as density functional theory (DFT), molecular dynamics (MD), kinetic lattice Monte Carlo (KLMC), and continuum-scale models. One of my primary goals is developing atomistic models of diffusion in these materials, for both native species and extrinsic dopants. Knowledge and control of diffusion is necessary for optimizing device performance, such as In/Ga interdiffusion in CIGS, which must be understood to spatially tune the bandgap. I use DFT to predict defect formation energies, defect complex binding, and defect migration barriers. These results...
Jiayi Li
I will work on large-scale integration of renewable energy resources such as rooftop solar PV: specifically, I will develop state-of-the-art tools at the intersection of machine learning and power system control to safely and efficiently integrate distributed energy resources. A motivating application for me is the integration of solar PV into the distribution system. They can both create large and rapid voltage fluctuations, as well as correcting them using inverter interfaces. Optimizing the actions of inverters is complex and computationally very challenging. Machine learning can be very helpful in designing the controllers but due to the uncertainty and volatility of the system, existing methods do...
Xingyi Wang
Semiconductor crystals that behave exactly as researchers expect are rare, if nonexistent in nature or in lab. Local environment within crystals like defects or surface discontinuities may dominate the properties of semiconductor materials, and often these intractable local impurities limit the performance of clean energy harvest or generation devices like solar cells or LEDs. As part of a collaboration, my project takes a step towards understanding photon generation and loss mechanisms bottom-up by isolating and directly observing single defects within ZnO nanoparticles at atomic level, and correlating their optical properties with theoretical calculations based upon the local environment of single defects. If the observed...
David Rosser
Materials science forms the foundation for advancement in modern technologies. The advent of quantum simulation to supplant the need for trial and error of experimental material discovery or large-scale supercomputer simulations for seemingly simple molecules is altogether appealing. However, the near-term prospects of useful noisy intermediate-scale quantum technologies is limited. An alternative paradigm, termed quantum emulation, proposes to map difficult problems to simplified lattice models which describe the relevant physics. My research focuses on the integration of monolayer van der Waals materials onto patterned, foundry-compatible dielectric materials for study of the interaction between optical transitions, provided by the van der Waals materials, and the...
Mareldi Ahumada Paras
My research is focused on resiliency of power systems, specifically in recovery optimization after outages caused by extreme events such as hurricanes, earthquakes and storms. 1) Use data driven case study to optimize utility schedules of repair crews and enhance the recovery phase of a power outage. 2) Quantify the impact of natural gas faults on power generating units in a co-optimized framework. Advisor: Daniel Kirschen - Electrical & Computer Engineering ...
Xiaofeng Xiang
Photovoltaic devices are important for the renewable clean energy system. Today, silicon-based solar modules keep dominating the market, but various emerging techniques based on thin-film inorganic semiconductors are rapidly developing. Among thin-film technologies, chalcopyrite Cu(In, Ga)Se2 (CIGS) shows excellent light conversion efficiency. The primary goal of my research is to develop modeling tools for design and optimization of CIGS fabrication and device operation processes. To achieve this goal, the first step is to understand the fundamental chemical reactions or physical phenomenons happening during manufacturing processes. Next, I would apply first principle calculations and use device simulation software to understand thermodynamic or kinetic aspects of the materials. Finally,...
Wenqi Cui
Renewable energy sources have brought more uncertainties to the operation of energy systems. Two challenges emerge. Firstly, we generally do not know the exact model and parameters of these energy resources. Secondly, robustly incorporating the uncertainties in operations is intractable using traditional control methods. Machine learning, especially reinforcement learning techniques, can potentially overcome these challenges by interacting with the environment to find good control strategies. Despite its potential, reinforcement learning does not readily apply to critical physical systems with hard constraints. My research focuses on closing these gaps to allow for the robust control of inverter-interfaced devices in energy systems. I will analyze problems with...