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Citations since 2016
10 Research Items
I'm a PhD student in Psychology (Cognitive Science) at UBC, working with Dr. Rebecca Todd and Dr. Alan Kingstone. I'm currently interested in exploring the neural and behavioural substrates of human avoidance behaviours. I have also developed novel, user-friendly methods for closed-loop behavioural reinforcement as well as a toolbox for mesoscopic calcium imaging analysis, and worked on characterizing local glucocorticoid regulation in the mouse brain.
September 2019 - August 2020
- Research Assistant
- I have been a teaching assistant for courses in personality psychology (PSYC 305A) and introductory statistics (PSYC 218).
March 2019 - August 2020
- Research Assistant
- For my honours thesis, I carried out research on the differences in how people with anxiety or depression perform on an avoidance task, while trying to translate an animal-based avoidance task to humans.
March 2018 - present
- Laboratory Assistant, Kinsmen Laboratory of Neurological Research
- With the Murphy Lab, I am working to develop a real-time movement tracking and feedback system for mice as well as a system for automated segmentation of mouse brain images by brain region.
We must often decide how much effort to exert or withhold to avoid undesirable outcomes or obtain rewards. In depression and anxiety, levels of avoidance tend to be excessive and reward-seeking is reduced. Yet outstanding questions remain about the links between motivated action/inhibition and anxiety and depression symptoms, and whether they diffe...
Learning which stimuli in our environment co-occur with painful or pleasurable events is critical for survival. Previous research has established the basic neural and behavioural mechanisms of aversive and appetitive conditioning; however, it is unclear what precisely is learned. Here we examined what aspects of the unconditioned stimulus (US), sen...
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registratio...
Glucocorticoids (GCs) are secreted by the adrenal glands and locally produced by lymphoid organs. Adrenal GC secretion at baseline and in response to stressors is greatly reduced during the stress hyporesponsive period (SHRP) in neonatal mice (postnatal day (PND) 2-12). It is unknown whether lymphoid GC production increases in response to stressors...
Corticosterone is produced by the adrenal glands and also produced locally by other organs, such as the brain. Local levels of corticosterone in specific brain regions during development are not known. Here, we microdissected brain tissue and developed a novel liquid chromatography tandem mass spectrometry method (LC‐MS/MS) to measure a panel of 7...
Operant conditioning-based closed-loop neuromodulation for regulation of brain activity has shown promise in producing positive behavioral changes associated with functional and structural connectivity. An important question is whether and how augmented sensory feedback can be designed to improve the learning of motor skills. We hypothesize that au...
Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (M = 0.95, SD = 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut - a robust mov...
Quantitative analysis of large scale brain images relies on accurate alignment and segmentation of regions of interest. Matching a reference atlas to the brain data are very labor- and time-intensive manual tasks and prevent high-throughput analysis. Furthermore, human error may occur when clicking the anatomical landmarks in different subjects. Ma...
Markerless and accurate tracking of mouse movement is of interest to many biomedical, pharmaceutical, and behavioral science applications. The additional capability of tracking body parts in real-time with minimal latency opens up the possibility of manipulating motor feedback, allowing detailed explorations of the neural basis for behavioral contr...
Our goal is to examine how machine learning can help in understanding the linkage between brain activity and specific movements in mice. Classifier-based feature selection enables us to investigate whether movement categories are associated with distinct spatiotemporal patterns of cortical calcium signals.