Zilu LiangJunior Associate Professor, Ph.D. in Electrical Engineering and Information Systems
- Areas of Research
- Ubiquitous Computing, Wearable Computing, Human Computer Interaction, Data Science, Health Informatics, Machine Learning, Natural Language Processing, Serious Game, Eye-tracking, fNIRS Brain Imaging, VR/XR/Oculus
Zilu Liang received her MSc and PhD in Electrical Engineering and Information Systems from the University of Tokyo in 2011 and 2015, respectively. She pursued exchange studies at the University of Oxford in 2010 and at the Imperial College London (with Prof. John Polak) in 2012. In 2015 she was selected as a recipient of the Australian Government Endeavor Research Fellowship which sponsored her postdoctoral research at the University of Melbourne (with Prof. James Bailey, Prof. Lars Kulik and Dr. Bernd Ploderer). During 2016-2019 she was an assistant professor at the Graduate School of Engineering, The University of Tokyo. She is a member of a number of research associations and she has received several awards for her research achievements.
Zilu is an avant-garde supporter of the Quantified Self movement . She combines a wide variety of sensing, computing, and data mining techniques to tackle the challenges surrounding data collection, data analysis, and human-computer interaction in Quantified Self, with a strong focus on health and wellbeing. Her research projects have been sponsored by a JSPS Grant-in-Aid for Research Activity Startup (2016-2018), an AIST Grant for Early- Career Researcher (2016-2018), an AMED Interstellar Initiative Grant (2018-2019), and a JSPS Grant-in-Aid for Early-Career Scientists (2019-2021). She has close collaborations with the Queensland University of Technology in Australia and the Trinity College Dublin in Ireland, and is looking forward to involving highly-motivated students in several exciting on-going research projects (more details on personal website).
Growing up in a family of teachers, Zilu has a natural passion for teaching. She received faculty development training on teaching and mentorship in the University of Melbourne. Her teaching style builds on validated educational theories. She seeks to further explore the mechanisms that underlie interpersonal differences in effective learning and teaching based on brain science and data mining. Since 2018, she has been serving as a program committee member and a reviewer for top notch international conferences on engineering and computing education, including the ACM Global Computing Education Conference (CompEd), the ACM Technical Symposium on Computing Science Education (SIGCSE) and the IEEE Conference on Engineering, Technology and Education (TALE). She is a member of the Japanese Society for Engineering Education.
Zilu treasures physical strength and is addicted to exercise. When she is not doing research or teaching, you can find her doing Taekwondo, hot yoga, or weight training.
Beyond the 10,000-Step Challenge: Using Wearables to Create a Better Self
Wearable devices already constitute a major development in consumer technologies, with everything from watches, wristbands, glasses and even rings being turned into a smart device. One key component of these devices is the ability to measure – be it your heart rate, the amount of sleep you got last night, or how many steps you took on a given day. KUAS’ Dr. Zilu Liang, whose research focuses on combining wearable technology with deep learning, has her sights set on something even bigger: she believes the future of wearables will be go beyond simply measuring.
As part of the “Quantified Self” movement, people have become more and more interested in improving their lifestyle based on measuring themselves. Usually, people start doing this with a goal in mind. One example would be trying to understand one’s sleep patterns better in an attempt to get a better night’s sleep. What current technology can do is provide quantifiable understanding, e.g. average hours of sleep on a weekday. However, there remains great potential in how to analyze and utilize this data.
First, another benefit of the spread of wearables is the ability to measure over a long period of time, as these devices are usually used daily. This can be then combined with deep-learning algorithms, making it possible to go beyond simply measuring and into the provision of analysis and solutions. The goal is to provide data-based, personalized ways to improve one’s lifestyle. Going by the previous example of sleep monitoring, the corresponding app could then give you based on your trends and behavior, potentially eclipsing the more generalized advice offered by professionals. However, Dr. Liang wants to go even further.
She believes in creating a future where we can provide analysis on things now considered abstract, such as stress or depression. By using the power of wearables and combining the various measurable they provide, this could potentially allow us to come up with a quantifiable definition of such terms, and thus help us understand what metrics, trends and precursors to look for, allowing for forecasting and early prevention.