Michel M. Maharbiz (Advisor)

Research Advised by Professor Michel M. Maharbiz

Mauricio J. Bustamante

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Professor Kristofer S.J. Pister (Advisor)
Ph.D. 2023 (Anticipated)

Oliver Chen

Alumni
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Professor Kristofer S.J. Pister (Advisor)
Ph.D. 2022

Oliver graduated with the B.S. degree in Electrical Engineering from the California Institute of Technology in 2016. He is currently working on hydrogel actuated carbon fiber microelectrode arrays in as an EECS PhD student at the University of California, Berkeley with Prof. Michel Maharbiz. He is a recipient of the NSF Graduate Research Fellowship.

Oliver is interested in biomedical device technology related to both systems-level design and MEMS devices. This includes microfabrication and implementation of implantable devices, in particular, neural probes for brain-machine...

Wentian Mi

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Professor Kristofer S.J. Pister (Advisor)
Ph.D. 2023 (Anticipated)

Jordan L. Edmunds

Alumni
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Ph.D. 2022

BPN970: Rotary Inchworm Motor for Underwater Microrobot Propulsion

Mauricio J. Bustamante
2022

Autonomous swimming microrobots for biomedical applications and distributed sensing require locally controllable swimming mechanisms. This project aims to develop underwater, rotary electrostatic inchworm motors for artificial flagella. Our proposed design uses gap closing actuators with an angle arm design, similar to existing inchworm motors, to drive a central rotor, all fabricated with an SOI process. An artificial flagella is attached the rotor, converting the rotational motion into propulsion. Major challenges include efficient operation of electrostatic motors underwater and...

BPN871: An Ultrasonic Implantable for Continuous In Vivo Monitoring of Tissue Oxygenation

Soner Sonmezoglu
2022

Our group previously demonstrated a “neural dust” system for neural recording which includes an implantable device and external ultrasonic transducers to power and communicate with the implantable. In this work, we extend that paradigm, demonstrating an implantable that can measure and report tissue oxygenation. Oxygenation state is a key parameter when assessing the metabolic state of cells and tissues, tissue and organ viability, tumor state, among many examples in both basic science and clinical care. Various types of methods for the detection of oxygen have appeared in recent...

Soner Sonmezoglu

Alumni
Electrical Engineering and Computer Sciences
Professor Michel M. Maharbiz (Advisor)
Ph.D. 2017, PostDoc 2022

Soner received the B.S. and M.S. degrees (with high honors) in Electrical and Electronics Engineering from Middle East Technical University (METU), Ankara, Turkey, in 2010 and 2012, respectively. He received his Ph.D. degree in Electrical Engineering from the University of California, Davis, in 2017. During his Ph.D., he worked on micromachined inertial sensors and their interface electronics.

He is a postdoctoral researcher in Electrical Engineering and Computer Sciences department at the University of California, Berkeley working with Prof. Michel M. Maharbiz. Soner’s current...

BPN890: Hydrogel Actuated Carbon Fiber Microelectrode Array

Oliver Chen
2022

Glial scarring and passivization of long-term implanted neural probes is one bottleneck in brain- machine interface technology. However, ultraflexible probes with similar mechanical properties as tissue have been shown to minimize scarring and other biological responses. We propose a flexible, microscale neural probe that can be actuated using an expanding hydrogel. This device is designed to be able to record neural signals up to hundreds of microns away from the insertion site. This design can allow for high-density, accurate neural recordings for a wide variety of clinical applications...

BPN924: Multimodality Platform for Neurogenesis and Neural Signal Recording After Stroke

Wentian Mi
2022

Stroke is a leading cause of disability in the United States. Recovery from stroke is complex and ultimately limited by the brains limited ability to regenerate damaged tissue. Ideally, we would want to drive neurogenesis and angiogenesis in a stroke lesion to aid in recovery. We propose a multimodality platform for stimulating neurogenesis which simultaneously allows for electrophysiological recording of neurons in the lesion area after stroke. Our aim is to provide a paradigm for making complex substrates for nervous tissue. With various devices integrated, multiple functions can be...