Liwei Lin (Advisor)

Research Advised by Professor Liwei Lin

Lin Group:  List of Projects | List of Researchers

AI-Assisted Hyperspectral Interferometry and Single-Cell Dispersion Imaging

Kamyar Behrouzi
Tanveer Ahmed Siddique
Megan Teng
Walid Redjem
Liwei Lin
Boubacar Kante
2026

Interferometry techniques are essential for extracting phase information from optical systems, enabling precise measurements of dispersion and highly sensitive detection of perturbations. While phase sensing offers enhanced sensitivity compared to conventional spectroscopy methods, this sensitivity often makes systems more vulnerable to external factors such as vibrations, introducing instability and noise. In this work, we demonstrate a broadband and AI-enhanced interferometry method, denoted general polarization common-path interferometry (GPCPI), that relaxes the polarization...

Peggy Tsao

Graduate Student Researcher
Mechanical Engineering
Professor Liwei Lin (Advisor)
Ph.D. 2026 (Anticipated)

Peggy Tsao received a B.S. in Mechanical Engineering from National Taiwan University in 2020. She is currently pursuing a Ph.D., major in Design and minor in MEMS/Nano in Mechanical Engineering at UC Berkeley under the supervision of Prof. Liwei Lin and is expected to graduate in 2027.

Zihan Wang

Postdoctoral Researcher
Mechanical Engineering
Professor Liwei Lin (Advisor)
PostDoc 2024 to 2026

Zihan Wang received his Ph.D. degree in Data Science and Information Technology at Tsinghua University in 2024. He is currently a postdoctoral researcher in Professor Liwei Lin's lab focusing on liquid metal-based sensors and actuators.

BSAC Seminar Committee Member - Fall 2025

BPNX1061: 3D Imaging Using PMUTs (New Project)

Nikita Lukhanin
Divij Muthu
Mostafa Sedky
Megan Teng
Tofic Esses
Linda Liu
Suraj Chamakura
Ryan Johnson
Chun-Ming Chen
2026

We have used PMUTs for 3D imaging. based on a 4×4 bimorph, pinned dual-electrode PMUTs array with the transmission beamforming scheme. This project advances the ultrasound-based imaging technique by using the compressed sensing scheme. The goal is to achieve 3D imaging with a low number of transducers and minimum computation for various applications in robotics, wearable electronics, autonomous navigation, and medical diagnostics.

Project is currently funded by: Member Fees

Declan M. Fitzgerald

Graduate Student Researcher
Mechanical Engineering
Professor Liwei Lin (Advisor)
Ph.D. 2029 (Anticipated)

Declan is a PhD student studying mechanical engineering in Professor Liwei Lin's research group. Prior to joining BSAC and the Berkeley community, he earned his BS/MS in mechanical engineering and his BS in psychology at the University of Maryland, College Park (UMD). He performed research in UMD's Bioinspired Advanced Manufacturing laboratory, as well as in the Division of Biomedical Physics at the Food and Drug Administration. His ongoing interests involve the use of advanced micro/nanofabrication techniques for the development of medical devices.

Ultra-Sensitive Nanosensor for Rapid Detection of PFAS in Simulated Drinking Water

Nikita Lukhanin
Keming Bai
Mia Wang
Declan M. Fitzgerald
Grigory Tikhomirov
Liwei Lin
2026

Per- and polyfluoroalkyl substances (PFAS) are a class of persistent synthetic compounds, often called “forever chemicals,” that pose a significant threat to public health and the environment. Standard detection methods primarily rely on liquid chromatography and mass spectrometry [1], which is expensive, time-intensive, and requires trained personnel and laboratory infrastructure. While emerging approaches using metal-organic frameworks (MOFs), molecularly imprinted polymers, and lateral flow assays have been explored, they have yet to provide a solution that simultaneously offers part-...

3D Imaging via Four PMUT Receivers by Compressed Sensing

Nikita Lukhanin
Divij Muthu
Chaoying Gu
Megan Teng
Kamyar Behrouzi
Chun-Ming Chen
Laura Waller
Liwei Lin
2026

We have successfully demonstrated three-dimensional (3D) ultrasound imaging via compressed sensing using piezoelectric micromachined ultrasonic transducers (pMUTs). This work reports the first experimental demonstration of compressed sensing with pMUTs, achieving 3D image reconstruction near the acoustic wavelength limit by only four receiving elements. Pseudo-random transmission signatures were encoded through frequency and delay modulation to enable high-fidelity reconstruction with a peak signal contrast of 28.7 dB. A 16-element lithium-niobate pMUT array operating at a resonant...

BPNX1069: Programmable Self-Assembly of Microparticles (New Project)

Umut Can Yener
Declan M. Fitzgerald
Mostafa Sedky
Huicong Deng
2026

Piezoelectric micromachined ultrasonic transducer (PMUT) arrays provide a compact and scalable platform for synthesizing programmable acoustic pressure fields. In this project, we investigate PMUT-enabled programmable microparticle self-assembly in fluidic environments using dynamically controlled ultrasonic standing waves. Unlike conventional cymatics, which relies on fixed vibrational modes of a single acoustic source, individually addressable PMUT elements enable active control of frequency, phase, and amplitude to generate reconfigurable pressure landscapes. By tailoring...

Alexander Alvara

Graduate Student Researcher
Mechanical Engineering
Electrical Engineering and Computer Sciences
Professor Liwei Lin (Advisor)
Professor Kristofer S.J. Pister (Advisor)
Ph.D. 2025 (Anticipated)

Alexander Alvara is a final year Ph.D. Candidate in mechanical engineering who earned his 3 BS degrees from UC Irvine '17 concurrently in mechanical engineering, aerospace engineering, and materials science and engineering. Alexander is interested in extreme conditions applications and performance of MEMS devices as well as nanoscale materials engineering that investigates the interplay of materials with electromagnetism and light.

BPNX1036: Enhanced Gas Sensing with Machine Learning

Yuan Gao
Wei Yue
2026

Accurate and real-time gas detection is crucial for applications ranging from environmental monitoring to industrial processes. Traditional methods are often limited by low accuracy, slow response times, and high costs. This project introduces a scalable machine learning fusion system that integrates sensor fusion techniques to enhance detection performance. With encoder-decoder architectures and a decision fusion model, our approach significantly improves the accuracy of carbon dioxide sensing, achieving a mean absolute percentage error (MAPE) of 2.97% while reducing response and recovery...