Researcher

Julian Maravilla

Flexible Wearable and Conformal RF

As IOT and body concentric circuits become more common, it's only natural that their electronics and RF
systems adapt to the world around them. The goal of my research is to apply different manufacturing
techniques to design high performance RF circuits and systems that are physically flexible and
conformal. Current applications surround antennas for Magnetic Resonance Imaging, Wireless
Communications, and Wireless Power Transfer.

Masoud Masoumizadeh

Yukio Miyasaka

Joosung Moon

Elizabeth Murray

Minh Nguyen

Dima Nikiforov

Computer Architecture for Real-time Robotics Systems

Developing tools such as RoSÉ for the design space exploration of robotics SoCs, covering compute,
sensing, and environmental simulation. Additionally, using robot protoyping as driving applications for
deploying SoCs developed at BWRC.

Hyeong-Seok Oh

Fully integrated Label-free biomolecular sensor

Label-free miniaturized optical sensors can have a tremendous impact on highly sensitive and scalable

Point-of-Care (PoC) diagnostics by monitoring in real-time molecular interactions without any labels. However, current biophotonic platforms are limited by complex optical and external readout equipment, precluding their use in a PoC setting. In this project, we address this challenge by developing a fully integrated electronic-photonic label-free molecular sensor utilizing microring resonators (MRRs) co-...

Harry Hyeong-Seok Oh

Harry Hyeong-Seok Oh (Graduate Student Member, IEEE) received the B.S. degree (summa cum laude) in electrical and computer engineering from Seoul National University, Seoul, South Korea, in 2021. He is currently pursuing the Ph.D. degree in electrical engineering and computer sciences with the University of California at Berkeley, Berkeley, CA, USA. He held an internship position at Ayar Labs, Emeryville, CA, USA, where he worked on high-speed analog/mixed-signal design. His current research interests include design and modeling of electronic–photonic integrated systems, analog and mixed-...

Aviral Pandey

Adaptive Frontends for Biosignal Recording Systems

Biosignal recording systems typically record electrical signals from the body from many channels at a high frequency. This data is then classified using machine learning approaches to create an output of interest, such as what gesture is the user attempting to create with their muscles or whether a person is sleeping
or not. Often, the classifier does not weight the data from every channel equally, and this per channel weight or importance cannot be predetermined. However, if a channel is not as important as...