Professor Yeung Wai-ho Raymond, Choh-Ming Li Professor of Information Engineering, and Co-Director of the Institute of Network Coding, The Chinese University of Hong Kong (CUHK), has been named the recipient of the IEEE 2021 Richard W. Hamming Medal for his fundamental contributions to information theory and pioneering network coding and its applications. This is one of the highest honours in electrical and electronics engineering. It is the first time this award has been won by an Asian researcher since its establishment in 1988. It is also the first time that home grown research in this area has been recognised at this level.  

Professor Raymond Yeung is a world-renowned expert in information theory and a co-founder of the field of network coding. In the early 1990s, he was invited to participate in a project of NASA’s Jet Propulsion Laboratory for salvaging the malfunctioning Galileo spacecraft. The 25-bit synchronization marker he designed was used for transmitting back to Earth the images of Jupitar and its satellites taken by the spacecraft. Since 2010, he has been a Co-Director of the Institute of Network Coding at CUHK, the largest engineering research project ever funded in Hong Kong, with the total budget exceeding HKD100M.

Information inequalities are an indispensable tool for proving theorems in information theory. For a long time, Shannon-type inequalities were all the known information inequalities. In the mid-1990s, Professor Yeung contemplated the existence of non-Shannon-type inequalities and discovered with his collaborator the first such inequality which is now known as the Zhang-Yeung inequality. This groundbreaking work proves the incompleteness of Shannon-type inequalities. In addition to information theory, this finding has significant implications in different fields in information sciences, mathematics, and physics.

Professor Yeung also co-founded the field of network coding. In the late-1990s, he proposed the concept of network coding that revolutionised network communication. In the past, information was transmitted in a network very much like commodity flow, with the intermediate nodes relaying data packets passively. Simply speaking, with network coding, by applying coding to data packets inside the network, more information can be transmitted through the network. In practice, this means people can download data faster, watch video streaming with less delay, and communicate more securely on the Internet.

Professor Yeung has co-founded n-hop technologies, a startup company in Hong Kong Science Park that focuses on BATS, a network coding technology that solves the longstanding problem of packet loss in wireless multi-hop communication. Currently they are applying this technology to provide WiFi service at country parks that are not well covered by the cellular network. With this service, hikers can use mobile devices for navigation, accessing weather information, uploading pictures to social media, and even emergency calls. Professor Yeung has also co-founded CU Coding, another startup company that focuses on network coding data storage and physical-layer network coding.

Professor Yeung is the author of two textbooks on information theory and network coding that have been adopted by over 100 universities around the world. The paper he co-authored that founded the field of network coding has received close to 10,000 citations on Google Scholar. In 2014, he offered the first online course in information theory that attracted 25,000 students. Since then, the course has been offered regularly on Coursera and other platforms.

Professor Yeung has received numerous awards for his research contributions, including the 2018 ACM SIGMOBILE Test-of-Time Paper Award and the 2016 IEEE Eric E. Sumner Award. He is a Fellow of the Hong Kong Institution of Engineers, the Hong Kong Academy of Engineering Sciences, and IEEE. He holds 10 patents on BATS codes.

About IEEE Richard W. Hamming Medal

IEEE Medals are the highest awards presented by the IEEE. The IEEE Richard W. Hamming Medal, established in 1986, is named in honor of Dr. Richard W. Hamming, who had a central role in the development of computer and computing science, and whose many significant contributions in the area of information science include his error-correcting codes. It is awarded to an individual or team for exceptional contributions to information sciences, systems, and technology.

A multi-disciplinary team of researchers from CUHK has been granted up to 5.6 million Hong Kong dollars to assist in developing machine learning technologies for advancing digital biomarkers for Alzheimer’s Disease. This will contribute to the efficiency and accuracy of early identification using the state-of-the-art sensing and artificial intelligent (AI) technologies. The project is funded by the Diagnostics Accelerator program of Alzheimer’s Drug Discovery Foundation (ADDF) and a matching fund from SenseTime Technologies (SenseTime). It is led by Professor Guoliang XING from the Department of Information Engineering, Faculty of Engineering at CUHK, joined by local engineering and medical experts.

Alzheimer's Disease (AD) and related dementia is a growing health problem worldwide because of population aging. It affects 50 million people and there are more than 10 million new cases worldwide. It has been one of the most common chronic diseases for the aging population in Hong Kong, affecting one in every ten older adults. The number of confirmed cases is expected to rise from 115,000 in 2016 to around 240,000 cases in 2036.

Early identification of people at risk of developing AD and timely intervention to slow the onset and progression of AD are crucial, because disease modifying treatment for AD is not available at present. An accurate, easy-to-do test may enhance the effectiveness of screening and identification of AD and address the problem of denial of the illness. 

Recent years have witnessed significant progress in the Internet of Things (IoT) and smart sensing technologies, with many advanced sophisticated sensors making their way into personal and mobile devices. Many families today own more than a dozen  connected smart devices.  These devices can capture, on an unprecedented scale, the physiological, behavioral, lifestyle, and cognitive indicators, referred to as “digital biomarkers”, in natural living environments in a non-invasive manner.

Professor Bolei ZHOU from the Department of Information Engineering, CUHK, and a member of the research team, stated, “Machine learning technique allows us to conduct objective longitudinal monitoring and comparison of indicators, enabling the detection of minimal abnormalities in our nervous system, thus achieving early identification of and intervention in AD. However, the prevalence of sensors will lead to various issues such as privacy concerns and the “black box” nature of AI algorithms. These must be solved in order to interpret the digital markers and their links with disease pathophysiology.”

Comprising experts in AI and various medical specialties, the multi-disciplinary team aims to develop cutting-edge sensing and AI technologies to discover new digital biomarkers for early diagnosis of Alzheimer’s Disease.  The developed technologies will classify digital biomarkers for Activities of Daily Living (ADL), Behavioral and Psychological Symptoms of Dementia (BPSD), social interactions, motor function, and level of cognition. The team will also develop a real-time learning system which enables smart devices to collaboratively improve the accuracy of AI algorithms while keeping all the data on the device and hence preserving user privacy. In addition, the team will also propose interpretable machine learning algorithms to quantify the correlation of multi-modal digital biomarkers, and assist early detection, diagnosis and intervention. 

The new technologies from this project will lead to new approaches that can detect any immediate risks that require prompt attention or action. They will predict and identify individuals who have a higher chance of developing AD and dementia, introduce appropriate advice based on the specific needs of the individual, modify that according to the response of the prior suggestion or intervention, and provide feedback to the patients and caregivers so as to improve self-care management of the disease and stress adaptation. 

Professor Guoliang XING, the Principal Investigator (PI) of this project, stated, “This project will bring together top experts from engineering and medical fields to address one of the biggest challenges faced by our aging society. The sensing and AI technologies to be developed will empower doctors, caregivers and patients themselves to work together for early diagnosis of and intervention in AD. We believe this project will lead to important enabling technologies for the paradigm of “smart health”, the  vision of which is to transform today’s reactive hospital centred healthcare practice to proactive, individualised care and wellbeing.” 

Background of the project and research team

The project team includes Professor Guoliang XING as Principal Investigator (PI), and 5 Co-PIs: AI experts Professors Bolei ZHOU and Rosanna CHAN from the Department of Information Engineering, Professor Timothy KWOK from the Department of Medicine and Therapeutics, Dr. Allen LEE from the Department of Psychiatry, Faculty of Medicine at CUHK, and Professor Doris YU from the School of Nursing at the University of Hong Kong.

The major funding comes from the ADDF’s Diagnostics Accelerator, an initiative dedicated to developing reliable and affordable biomarker test for early diagnosis of Alzheimer’s disease. The ADDF was founded in 1998 and has awarded more than $150 million to fund over 626 programs for Alzheimer’s and related dementias in academic centers and biotechnology companies in 19 countries. To learn more, visit: www.alzdiscovery.org/ .