Dr. Taylor Chomiak
Positions
Staff Scientist and Adj. Assistant Professor
Cumming School of Medicine, Department of Clinical Neurosciences
Child Health & Wellness Researcher
Alberta Children's Hospital Research Institute
Contact information
Background
Biography
My research is focused on developing Al data engines, algorithms, and advanced statistical models that can be scaled-up to larger patient populations and implemented across urban and rural communities as a cost-effective model for diagnostic and therapeutic care. A large portion of my research program has included developing simple clinical tests, machine-learning tools, and wearable technologies as part of a smartphone sensor technology platform to support a full suite of automated clinical assessments and therapeutic interventions in clinical populations. Some of this research is highlighted below.
Research
Areas of Research
The posterior attention network is an important area of the brain that is involved in the processing of sensory stimuli, memory, and attention control. It has also been implicated in several well-known neurological conditions, such as autism spectrum disorder (ASD) and Parkinson's disease (PD). My basic science research focuses on fundamental mechanisms of cortical maturation and plasticity, both in terms of non-pathological states and in psychiatric illnesses such as ASD. This includes understanding how the timing of neural development and the unmasking of neural connectivity can affect behavioural development.
My clinical research has focused on a newly developed sensorimotor cueing technology platform and wearable-sensor system. This technological platform computationally links motor action with reward feedback for self-motivating stimulation that can serve as a biofeedback-based behavioural shaping strategy. The goal is to further develop the use of this technology to promote positive clinical outcomes in neurological conditions such as ASD and PD.
Participation in university strategic initiatives
Projects
New computational tool uses less processing power over shorter period of time to provide more accurate results
By combining conventional cognitive and motor assessments with mobile and computing technology and nonlinear feature space mapping, a new predictive model has been developed that could lead to tailored treatment to improve quality of life for people living with Parkinson's.
Forecasting through Recurrent Topology (FReT) offers a new approach to decode and forecast time-evolving dependencies in a time-series that reduces computational complexity and cost.
This new computational framework represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.
Dormant neurons that slowly activate as the brain matures, may help us understand normal and abnormal neurodevelopment.
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