Dr Bryn Jeffries
GradCertEd (Higher Education), M.IT, D.Phil (Laser Physics), M.Phys
Research Fellow, School of Computer Science
J12 - The School of Information Technologies
The University of Sydney
Telephone | +61 2 9351 4275 |
Website |
School of Computer Science Human-centred data management CRC Alertness, Safety and Productivity |
Biographical details
Dr Bryn Jeffries is a Physicist and Computer Scientist, with experience in industry and academia. He works in the School of Computer Science at the University of Sydney, principally in the field of data science in sleep health. Bryn is a member of the Centre for Translational Data Science, and a co-investigator for the CRC for Alertness, Safety and Productivity.
Research interests
Researchers often collect large amounts of data without yet knowing where the value within it lies. They often then need to review at least some of this data manually in order to reveal its value, which can be a slow and sometimes subjective process. Dr Bryn Jeffries' research looks at more efficient means of extracting new and useful information from research and other types of data.
"Data science - the field that I work in - offers so many opportunities to work with data to answer complicated problems in a wide range of domains.
"For example, I'm currently working with data from the CRC for Alertness, Safety and Productivity, which aims to improve alertness and therefore safety in the workplace and during commutes, in part by improving people's sleep. The data comprises recordings from people with sleep disorders such as insomnia and sleep apnoea.
"To diagnose a person's condition, that person typically has to make multiple overnight visits to a sleep clinic, where they are monitored so that researchers can understand their brain and breathing activity during sleep, among other things. This data then needs to be analysed in order to decide what treatment is required. As well as being very time consuming, this process can also be rather subjective.
"We wanted to be able to be able to diagnose people and determine the best form of treatment without their having to make so many trips to the clinic. So we're collecting data outside the clinic, such as with activity meters and heart-rate monitors, and using machine learning techniques to identify different types of abnormal breathing from recordings of people while they're sleeping.
"This automated approach can provide faster and more consistent assessment than the traditional approach.
"This is just one example of data science in action. I love it because I love problem solving, and in this research I get to help solve real problems that affect millions of people around the world.
"I've been working at the University of Sydney since 2010, and have been a researcher here since 2014. I've been very fortunate to benefit from the experience of many wonderful people here. There is a great community of researchers across the whole University who are very willing to explore new ideas."
Teaching and supervision
Units of Study
- COMP9120 - Database Management Systems
- INFO2120 - Database Systems 1
- INFO2820 - Database Systems 1 (Advanced)
- INFO3404 - Database Systems 2
- INFO3504 - Database Systems 2 (Advanced)
Selected past Capstone, SSP/TSP and Honours topics
- Feature Extraction from Audio-Visual Fatigue Studies, Patricia Lor, Honours 2015
- EEG Analysis Pipeline: Exploratory Cluster Analysis on Insomnia Disorder, Stephen McCloskey, Honours 2016
- Classifying Sleep Apnea: Tools and Techniques for classification in time-series data, Rafael Mazzoldi, Honours 2016
- Predicting Subjective Sleep Quality Using Wearable Device Data, Sonali Kalthur, Capstone 2016
- Detecting sleep in Insomniacs from wearable data, QiChang Feng, Capstone 2016
- Analysing Actigraphy and Cogstate Capture Data for the Alertness CRC Insomnia Study, Nicholas Bosch, SSP 2016
- Automated Processing of Light and Circadian Phase Data, Xenia Boynton, Capstone 2017
- Predicting real-time Melatonin Amplitude from Ambulatory Monitoring via Machine Learning, Mohamad Elagha, Capstone 2017
- Decomposition Of Recurrent Neural Networks For Prediction Of Circadian Phase, Siyi Chen, Honours 2018
- Deep learning techniques for astrometry, Michael Rizzuto, Honours 2018
- Machine Learning Classification on Biometric Shirt Data to Detect Hypopnea and Obstructive Sleep Apnea Events, Nahian-al Hasan, SSP 2018
- Predicting abnormal sleep with wearable devices, Naris Rangsiyawaranon, SSP 2018
Current research students
Project title | Research student |
---|---|
Data mining sleep data for sleep disorders classification and sleep/wake detection | Rim HAIDAR |
Sleep Disorder Analysis Using Machine Learning Techniques | Stephen MCCLOSKEY |
Current projects
- Crash prediction from MongoDB logs, Kelly Stewart, Honours 2019
- At-home monitoring of sleep apnea using a smart shirt, Nahian-al Hasan, SSP 2019
Associations
- Institute of Physics
Selected grants
2017
- Practical Databases: Managing and Analysing Data with SQL (Open Learning Environment - Undergraduate); Roehm U, Jeffries B; DVC Education/Small Educational Innovation Grant.
2014
- Enhancing feedback in online assessment of computer programming; Jeffries B, Koprinska I, Viglas A, Gramoli V; DVC Education/Large Educational Innovation Grant.
Selected publications
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