Jonathan is a researcher in the System Sciences Laboratory. His interests lie in the application of artificial intelligence and machine learning within mobile environments and he has worked on the design, development and evaluation of a number of intelligent mobile application projects. His research interests include affective and ubiquitous computing, sensor data analytics, intelligent mobile applications, quantified self and mobile health. In particular, he is interested in the use of mobile phone and wearable sensors for activity recognition, behavior understanding and mental health improvement.
Jonathan has developed machine learning models for inferring psychological state information (for states such as anxiety, stress and panic) from physiological data (such as heart rate, respiration rate, heart rate variability and temperature). He has also analyzed wearable device and mobile sensor data including GPS traces, accelerometer data, activity and sleep data for use within health and wellness mobile applications. Jonathan is particularly interested in building intelligent mobile applications that combine personal and wearable sensors in order to benefit people suffering with behavioral health conditions such as anxiety and depression.
Jonathan received his PhD from the University of Auckland in New Zealand. His PhD research focused on the use of artificial intelligence for strategy generation in computer games. In particular, his research involved creating and evaluating systems that learned via demonstration and user traces. He produced a collection of computer programs that were able to play the game of poker at an advanced level. His computer poker agents have won international competitions. Jonathan published a comprehensive overview of the work in this field in Artificial Intelligence journal and he won the best application paper award at the 19th International Conference on Case Based Reasoning.
in the news view all
Panic button: How wearable tech and VR are tackling the problem of panic attacks
3 December 2015 | Wareable
events view all
Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series
16 May 2016 | Cancun, CA, Mexico
A Wearable and Mobile Intervention Delivery System for Individuals with Panic Disorder
1 December 2015 | Linz, CA
- Affective computing, Ubiquitous Computing, Sensor Data Analytics, Intelligent Mobile Applications, AI on Mobile Devices, Mobile Health.
- Designed and developed mobile and wearable systems for improving mental health and well-being
- Developed machine learning models for inferring psychological state information from physiological data
- PhD in Computer Science from University of Auckland, New Zealand