Ming C. Wu (Advisor)

Research Advised by Professor Ming C. Wu

Arkadev Roy

Postdoctoral Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
PostDoc 2023 to 2025

Arkadev Roy obtained his Ph.D. Degree in electrical engineering from California Institute of Technology in 2023. He completed his BTech degree in Electronic and Electrical communication engineering from the Indian Institute of Technology Kharagpur in 2018. Currently, he is a postdoc scholar at UC Berkeley and his work focuses on opto-electro-mechanical systems and heterogeneous integration.

BSAC Spring 2024 Research Review Presenter

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Integrated Optical MEMS for Scalable Trapped Ion Quantum Computing

Daniel Klawson
Ming C. Wu
2025

Quantum computing has emerged as a revolutionary field promising unprecedented computational power and transformative applications. Trapped ions have emerged as an encouraging platform for quantum computation due to their long coherence times, high-fidelity qubit operations, and the ability to achieve scalable entanglement and error correction. However, the practical realization of large-scale quantum systems faces significant challenges, including the need for efficient and scalable control of qubits. Integrated photonics has gained substantial attention as a promising platform for...

Johannes Henriksson

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2025

Johannes graduated from Lund University, Sweden, with a Masters degree in Engineering Physics which also included one year at UCLA as an exchange student. He has work experience from BorgWarner, ON Semiconductor and Lawrence Livermore National Laboratory and Apple. Johannes is currently pursuing his PhD degree in Electrical Engineering and Computer Science at UC Berkeley where he is working with Prof. Ming Wu on MEMS and silicon photonics.

Kyungmok Kwon

Alumni
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
PostDoc 2021

Dr. Kyungmok Kwon graduated from KAIST, Korea, with a Ph.D degree in Electrical Engineering. His research area is silicon-photonics, nanophotonics, nanoengineering and nanomaterial. Kyungmok is currently a postdoctoral researcher in Electrical Engineering and Computer Science at UC Berkeley where he is working with Prof. Ming Wu on MEMS and silicon photonics.

Biomimetic, Polymeric Transistor-Based Biosensor Technology

Jim C. Cheng
Albert P. Pisano
Ming C. Wu
Liwei Lin
2009

The goal of this research is the creation of robust, flexible, polymer sensors and circuits fabricated partially from the low cost biopolymer, chitosan, the deacetylated form of chitin which is the second most abundant polyssacharide in nature. Chitin is found in crustaceans, insects, bacteria and fungi. The sensors will detect diatomic gases and DNA to more complex macro molecules (e.g. exotoxins) in a fluidic or dry environment. Polymer-nanoparticle (e.g. Ge) hybrid films allow for development of robust polymer thin-film transistors and, with optimization of the hybrid film,...

Wafer-Level Integrated Microlens Couplers for Efficient and Scalable Photonic Circuit I/O

Jianheng Luo
Ming C. Wu
2024

Photonic integrated circuits (PICs) hold significant promise in applications such as data center networks, artificial intelligence, high-performance computing, quantum computing, and LiDAR, thanks to their energy efficiency and large communication bandwidth. By leveraging the infrastructure and technologies of conventional CMOS foundries, the development and manufacturing costs of PIC chips are significantly reduced. However, the commercialization of PIC products faces a critical challenge in optical packaging, which currently accounts for three times the cost of the PIC itself. Among...

Efficient 3D Vision for Autonomous Driving

Philip L. Jacobson
Ming C. Wu
2024

Self-driving vehicles have long been envisioned as a massive leap forward in transportation technology. Although several efforts to developing fully autonomous vehicles are currently being undertaken in both industry and academia, so far none have achieved the promise of full self-driving. Of the challenges in building the autonomous software for self-driving cars, one of the most prominent is perception, or the ability for the vehicle to sense the world around it. To meet the requirements for practical deployment onto autonomous vehicles, perception systems must meet four key metrics of...