News

Sept. 2024 We are excited to continue our collaboration with Sony SSUP (2024-25) on the project “Efficient and Robust Deep Learning Models for Neural-Machine Interfaces using the Spresense Microcontroller”!

Thanks, Prof. Zhang!

Aug. 2024 Our research was funded by the National Science Foundation to develop a prototype of an AI-driven platform for guiding neurodiverse teamwork!

Thanks, Prof. Wang and Prof. Requa!

July. 2024 One new undergraduate-level course “ENGR413 AI in Engineering” will be offered at SF State!

Apr. 2024 One collaborative paper was accepted by the International Symposium on Quality Electronic Design (ISQED 2024)!

Apr. 2024 Two papers were accepted by the IEEE RAS EMBS 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2024)!

Congratulations Ankita and Jimmy!

Arp. 2024 Our research was funded by the SFSU CoSE Ken Fong Translational Award!

Thanks, Prof. Quintero, and Prof. Zhang!

Dec. 2023 Our research was funded by SF State Research and Scholarly Activity Fund 2023–24!

Arp. 2023 Our research was funded by the Sony Sensing Solution University Collaboration Program (SSUP)!

Jan. 2023 One new graduate-level course “ENGR859 On-device Machine Learning” was offered at SF State!

Dec. 2022 Two papers were accepted by the 11th International IEEE EMBS Conference on Neural Engineering (NER 2023)!

Congratulations Zhenyu and Jimmy!

Dec. 2022 Our research was funded by SF State Research and Scholarly Activity Fund 2022–23!

Dec. 2022 Our research was funded by the CSU STEM-NET Faculty Interdisciplinary Collaborative Research SEED Grant Program!

Aug. 2022 Our team won Sony’s Spresense developer challenge 2022 Grand Prize!

Congratulations Summer Team!

May. 2022 Our research was funded by CSUPERB: Response to Industry Partnership Initiative Seed Grant Program!

Feb. 2021 Our member has been awarded the NSF participation grant to attend IEEE PerCom 2021 conference!

Congratulations Kory!

Welcome!

The MIC Lab at SFSU performs research in efficient mobile computing, deep learning acceleration, and intelligent application development. A key focus of MIC Lab is to achieve computational acceleration for deep learning on resource-constrained mobile/edge devices, such as smartphones and microcontrollers. In addition, our research implements efficient deep-learning algorithms with practical applications such as robotic, medical rehabilitation by establishing interdisciplinary collaborations.

Notice

We are looking for Graduate/Undergraduate Research Assistants in topics:

1. Efficient Deep Learning Algorithm: Neural Network Compression, Neural Network Interpretation
2. Efficient Deep Learning Application Development
3. Mobile/Edge Computing: Distributed Learning, Federated Learning

Preferred abilities but not required: Python, C Programming, Android/Linux Development.

Please look at the Research and Machine Learning Hub pages for our latest work and potential projects.