Ming C. Wu (Advisor)

Research Advised by Professor Ming C. Wu

Chun-Yuan Fan

Postdoctoral Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)

Chun-Yuan Fan received Ph.D. in Photonics and Optoelectronics from National Taiwan University in 2021. He is a professional in photonics and optical system design. His doctoral research field was related to metasurface with an optimization algorithm for advanced optical systems, including the electrically modulated metalens, ultrawide angle, and broadband achromatic metalens. He also established and designed a deep learning system for the company. After his Ph.D., he did a half-year postdoc at the original lab and proposed advanced optimization for the optical component, such as the...

BSAC's Best: Spring 2023 Awards Announced

April 20, 2023

BSAC would like to thank all of the researchers who presented their research during BSAC's Spring 2023 Research Review on April 19th.

BSAC Industrial Members voted for the outstanding paper and presentations and the results are in. Please join BSAC in congratulating the recipients of the Spring 2023 Best of BSAC honors, Peisheng He, Daniel Klawson, and Kevin Zheng!

Peisheng He (Liwei Lin Group), Best of BSAC S2023 ...

BPN986: Integrated Microlens Coupler for Photonic Integrated Circuits

Jianheng Luo
Johannes Henriksson
2023

We design and experimentally demonstrate a new silicon photonic fiber coupling method using integrated microlens couplers. Efficient, broadband and polarization-insensitive coupling to a single mode fiber with a best coupling loss of 0.8 dB is achieved.

Project currently funded by: Industry Sponsor

BPN991: Autolabeling for Large-Scale Detection Datasets (New Project)

Philip L. Jacobson
2023

3D perception is an essential task for autonomous driving, and thus building the most accurate, computationally efficient, fast, and label efficient models is of great interest. My research is particularly aimed at building detection models in the label-efficient (semi/self-supervised) and offline (auto-labeling) settings, areas which have both been under-explored in the literature. To improve 3D detection in these settings, I look to leverage sensor fusion (camera and LiDAR especially) and temporal fusion. In the off-board setting, 3D detection can be greatly enhanced by leveraging...

BPN974: Lidar-Camera Fusion for Autonomous Driving

Philip L. Jacobson
2022

Within the past few decades, the goal of fully-autonomous vehicles has moved from a thought experiment to a potential reality thanks to advances in machine intelligence. One of the key challenges to still be overcome is the building of robotic perception systems which can achieve performance on-par with or surpassing that of humans. Currently, most autonomous driving researchers rely on several different modalities for collecting visual information, namely lidar, radar, and cameras. Although relying on lidar for perception has the drawback of high cost, maturing lidar technology has opened...

BPN961: Integrated Photonics for Scalable Trapped Ion Quantum Computing

Daniel Klawson
Rohan Kumar
2023

Quantum computing is a new paradigm of computing that promises exponential performance increases for certain tasks as compared to classical computers. Trapped ions have been identified as a favorable medium – trapped ion quantum computers perform operations on singular atoms with precisely aimed laser pulses calibrated to state transitions within the ions’ energy levels. Bulk free space optics are currently used for qubit manipulation, but the large amount of optical equipment required hinders scalability. Recent pushes to build higher bit systems have identified photonic integrated...

Daniel Klawson

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

Daniel graduated with honors from the University of Maryland, College Park in 2020 with a B.S. in electrical engineering. He is a first-year doctoral student in the Ming Wu Integrated Photonics lab researching photonic integrated circuits for scalable trapped ion quantum computers.

Fabrication Tolerant Inverse Design Grating Couplers for Scalable Trapped Ion Quantum Computing

Daniel Klawson
Mizuki Shirao
Sara Mouradian
Ming C. Wu
2022

A fabrication tolerant Si grating coupler for 1.762 µm operation is optimized with inverse design, allowing for −30 dB crosstalk between a pair of 133Ba+ trapped ion qubits within expected fabrication variation.

Jianheng Luo

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2022 (Anticipated)

Jianheng Luo is currently working towards his Ph.D. degree at the University of California, Berkeley with Prof. Ming Wu. He received his B.S. degree in Engineering Physics from the University of California, Berkeley in 2017.