Engineering in Medicine and Biology San Francisco


Dear members of the Engineering in Medicine & Biology Society

We formed a local chapter in San Francisco and our officers are:


ChairXiaorong Zhang, PhD, Assistant Professor in Intelligent Computing & Embedded Systems, San Francisco State University

Vice-Chair: Charles Koch, MBA, JD, Associate in Patent Litigation, Pepper Hamilton

TreasurerAlex Matov, PhD, SMIEEE, Founder in Scientific Computing & Early Detection of Disease, DataSet Analysis

Secretary: Alexandre Coimbra, PhD, Principal Scientist in Clinical Imaging, Genentech


Below you will find information regarding our meetings:

TalkIntelligent Ear-Level Devices for Hearing Enhancement and Health and Wellness Monitoring (IEEE SPS Distinguished Industry Speaker Talk)

With resurgence of AI and machine learning, sensor miniaturization and increased wireless connectivity, ear-level devices are going through a major revolution transforming themselves from hearing devices into hearing enhancement and health and wellness monitoring devices. In this talk, we will present examples of such transformation in the areas of hearing enhancement, health and wellness monitoring and user experience. In the process, we will highlight how AI and machine learning, miniaturized sensors and wireless connectivity are enabling and accelerating the transformation. In addition, we will discuss practical challenges for the transformation today. Finally, we will provide an outlook on future directions and opportunities.

Date: Thursday, May 9th, 2019

Time: 6:30pm – 8:30pm (registration, food, networking 6:30pm-7:00pm, technical presentation followed by Q&A 7:00pm-8:30pm)

LocationAMD, 2485 Augustine Dr, Santa Clara, CA 95054

Speaker: Tao Zhang, Ph.D., Director of Signal Processing Research Department, Starkey Hearing Technologies

Tao Zhang received his B.S. degree in physics from Nanjing University, Nanjing, China in 1986, M.S. degree in electrical engineering from Peking University, Beijing, China in 1989, and Ph.D. degree in speech and hearing science from the Ohio-State University, Columbus, OH, USA in 1995. He joined the Advanced Research Department at Starkey Laboratories, Inc. as a Sr. Research Scientist in 2001, managed the DSP department from 2004 to 2008 and the Signal Processing Research Department from 2008 to 2014. Since 2014, he has been Director of the Signal Processing Research department at Starkey Hearing Technologies, a global leader in providing innovative hearing technologies. He has received many prestigious awards including Inventor of the Year Award, the Mount Rainier Best Research Team Award, the Most Valuable Idea Award, the Outstanding Technical Leadership Award and the Engineering Service Award at Starkey.

He is a senior member of IEEE and the Signal Processing Society and the Engineering in Medicine and Biology Society. He serves on the IEEE AASP Technical Committee, the industrial relationship committee and the IEEE ComSoc North America Region Board. He is an IEEE SPS Distinguished Industry Speaker and the Chair of IEEE Twin-cities Signal Processing and Communication Chapter.

His current research interests include audio, acoustic, speech signal processing and machine learning; multimodal signal processing and machine learning for hearing enhancement, health and wellness monitoring; psychoacoustics, room and ear canal acoustics; ultra-low power real-time embedded system design and device-phone-cloud ecosystem design. He has authored and coauthored 120+ presentations and publications, received 20+ approved patents and had additional 30+ patents pending.

Register for this presentation here:


TalkProtecting & Profiting from Your Intellectual Property

Date: Tuesday, June 26th, 2018

Time: 11:45am – 1:45pm (lunch catering 11:45am-12:30pm, technical presentation 12:30-1:45pm)

Location: Spaces Works Levi’s Plaza, 1160 Battery Street, Suite 100, San Francisco, CA 94111

Speaker: George Likourezos, MS, JD of Carter, DeLuca, Farrell & Schmidt

George Likourezos, Esq. is an IEEE member for over 25 years, and a partner at the intellectual property law firm of Carter, DeLuca, Farrell & Schmidt.  He graduated from Polytechnic University (now NYU Tandon School of Engineering) with a Bachelor of Science in Electrical Engineering and a Master of Science in Operations Management.  He has a Juris Doctor degree from Touro Jacob D. Fuchsberg Law Center, and is registered to practice with the U.S. Patent and Trademark Office. He has over 20 years’ experience representing startups, university, research institutions, and corporate clients, including many Fortune 100 companies, in intellectual property matters in fields including: AI, bioelectronics, cybersecurity, digital health, electrical engineering, electronics, electromechanical devices, life sciences, materials, medical devices, telecommunications, and computer-implemented inventions.  His legal experience includes patent procurement and licensing, prosecuting patent applications, conducting due diligence investigations, rendering freedom to operate legal opinions, and representing clients in patent litigation proceedings.

Carter, DeLuca, Farrell & Schmidt (CDFS) is a leader in intellectual property law, Carter, DeLuca provides timely and cost-effective services to clients from around the world. CDFS provides their clients with guidance, clarity and direction in protecting innovations in a wide range of technologies.

Register for this presentation here:

TalkDeep Learning Image Recognition of Alzheimer’s Disease

Date: Friday, May 4th, 2018

Time6:15pm – 8:15pm (social hour 6:15-7pm, technical presentation 7-8:15pm)

Location: The Bar Association of San Francisco, 301 Battery Street, 3rd Floor, San Francisco, CA 94111

Speaker: Saman Sarraf, MS, SMIEEE of Konica Minolta Laboratory USA

Abstract: Recently, the application of deep learning in recognizing dementia has been of interest to researchers. To extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer’s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer’s and healthy brain data has been challenging in older adults due to highly similar patterns of brain atrophy and image intensities. Deep learning architectures such as convolutional neural networks are designed to extract hierarchical features from data and perform classification through deep layers. The application of deep learning to recognize Alzheimer’s disease from normal healthy brains demonstrated a very high performance of classification using MRI and functional MRI data. This presentation outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer’s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture.

Register for this presentation here:

Join our growing online community: linkedin/group/EMBS SF

Other recent IEEE events in San Francisco:

Meeting of the Executive Committee was held on September 13, 2018

Meeting of the Communications Society was held on August 15, 2018

Meeting of the Executive Committee was held on August 9, 2018

Meeting of the Communications Society was held on July 18, 2018

Meeting of the Executive Committee was held on July 12, 2018

Meeting of the Executive Committee was held on June 14, 2018

Meeting of the Executive Committee was held on May 10, 2018

Meeting of the Executive Committee was held on March 29, 2018

Meeting of the Communications Society was held on January 31, 2018 sf.chapters.comsoc