Liwei Lin (Advisor)

Research Advised by Professor Liwei Lin

Lin Group:  List of Projects | List of Researchers

Fanping Sui

Alumni
Mechanical Engineering
Professor Liwei Lin (Advisor)
Ph.D. 2022

Fanping Sui received his B.S. in Theoretical and Applied Mechanics from University of Science and Technology of China in 2018 (ranked 1st) and M.S. in Mechanical Engineering from University of California, Berkeley in 2019. He is currently pursuing Ph.D. in MEMS/Nano in Mechanical Engineering at UC Berkeley under the supervision of Professor Liwei Lin.

BPN855: Flexible Energy Harvester, Sensor, and Actuator

Yu Long
2022

Flexible, wearable, and implantable devices are expected to become more abundant due to developments in materials and microfabrication technologies. In our previous work, we have successfully achieved a kind of flexible actuators that can work at low voltage and give haptic feedback. Now we are moving forward to develop energy harvesters with various sets of flexible materials and structures that can 1) harvest energy from outside stable environment; 2) generate electricity in a relatively long time; and 3) give enough energy output to power up small devices.

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BPN877: Pulse Acquisition and Diagnosis for Health Monitoring

Ruiqi Guo
2022

Traditional Chinese medicine(TCM) has existed for more than two thousand years and one of the important diagnostic methods is the pulse diagnosis. It generally takes decades of training for a practitioner to master this skill as pulse acquisition and diagnosis require long-term experiences and are very subjective. The project aims to use the combination of advanced sensor technology and artificial intelligence to emulate the TCM practice for health monitoring. A flexible piezoelectric film is designed to record the wrist pulse data. A group of representative pulse features has been...

Yu Long

Alumni
Mechanical Engineering
Professor Liwei Lin (Advisor)
Ph.D. 2022

Yu received her B.E. in Material Science & Engineering from Tsinghua University in 2018. She is currently pursuing a Ph.D. in Mechanical Engineering under the supervision of Prof. Liwei Lin and is expected to graduate in 2022. Her research interest lies in polymers and their applications in energy harvesters.

Dissertation title: Renewable Polymeric Energy Harvesters from Moisture and Heat

Ruiqi Guo

Alumni
Mechanical Engineering
Professor Liwei Lin (Advisor)
Ph.D. 2022

At present, Ruiqi is a Ph.D.student in Liwei Lin Lab. In 2019, Ruiqi earned his Masters degree in Advanced Energy Technology at UC Berkeley.

Dissertation Title: Machine Learning for MEMS Structure Design and Pulse Signal Analysis

Wei Yue

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

Wei Yue received his B.S. in Theoretical and Applied Mechanics from Peking University in 2021. He is currently pursuing Ph.D. in MEMS/Nano in Mechanical Engineering at UC Berkeley under the supervision of Professor Liwei Lin.

Sedat Pala

Alumni
Mechanical Engineering
Professor Liwei Lin (Advisor)
Ph.D. 2022

Sedat Pala received the B.S (1st ranked, Fall 2015) and M.Sc. (Most Successful Student) in Mechanical Engineering from Middle East Technical University (METU), Ankara, Turkey in 2015 and 2017, respectively. He also completed a minor degree in Business Administration in METU, 2015. He is currently pursuing a Ph.D. in MEMS/Nano in Mechanical Engineering at UC Berkeley under the supervision of Prof. Liwei Lin and expected to graduate in 2022.

Accelerating the MEMS Design Process via Deep Learning

Ruiqi Guo
2022
Spring 2022 BSAC Research Review Presentation View Slides View Presentation

BSAC Researchers Advised by Professor Liwei Lin

Customizing MEMS Designs via Conditional Generative Adversarial Networks

Fanping Sui
Ruiqi Guo
Wei Yue
Kamyar Behrouzi
Liwei Lin
2021

We present a novel systematic MEMS structure design approach based on a “deep conditional generative model”. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as...