I’m starting my 5th year in the PhD program in Biomedical Engineering at Boston University. I’m broadly interested in using my quantitative skills to investigate human attention, learning, neuropsychiatric disorders, and hard-to-quantify behaviors in neuroscience and psychoacoustics. I study these interests in the BU Auditory Neuroscience Lab under the mentorship of Dr. Barbara Shinn-Cunningham, Ph.D. Below are high-level summaries of my work.

Current work for non-experts

My current project is a study of various forms of selective attention in young adults with Attention Deficit Hyperactivity Disorder (I’ll explain below!). In our world of innumerable distractions, my goal is to better understand ADHD as a brain disorder, understand the influence of stimulant medications on the brain’s activity, and identify a relationship between our inattentiveness and our brain’s sensory (visual and auditory) systems. These basic science aims are important for neuroscience and pharmacology alike, as we aim to understand how our cognitive abilities influence our behaviors. On a clinical note, a pipe dream is to design better (more quantitative) criteria for diagnosis of ADHD using neuroimaging.

So how will I do this? By analyzing people’s performance on selective attention tasks. Auditory selective attention is our ability to pay attention to specific sounds within a complex mixture of sounds – for instance, a single conversation at a loud cocktail party. Visual selective attention is similar: it’s how we can use our eye movements or even peripheral vision (without moving our eyes) to single out a certain sight — for instance, finding a yellow dress in a closet of blacks, greys, and blues. To study these abilities, I designed a set of simple computer tasks and have invited young adults with and without ADHD to perform them in our lab. Simultaneously, I record electroencephalography (EEG), a measure of brain activity in the form of electrical activity that is picked up on the scalp. From the EEG data, I find neural correlates (measures from the brain that correlate to a brain process) of selective attention and then then assess how they look different from ADHD and neuro-typical (non-ADHD) people.


More specifically…

Using 64-channel EEG, I aim to identify neural correlates of selective attention from several measures including but not limited to:

  • the modulation of event-related potential (ERP) N1 components
  • alpha band oscillatory activity (power modulations)
  • theta and beta band power ratios (seen in literature to be elevated in ADHD during resting state EEG)
  • task performance

I plan to use source localization to identify ROIs with altered activity and then use machine learning techniques to classify subjects as ADHD or neurotypical. I also aim to develop a descriptive model of the interactions between pre-frontal, parietal, and occipital channels and sources. This work is underway. If you have more questions, please contact me directly.

Previous posters:

Australia Science of Learning 2017
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Quantitative Biology and Physiology Fellowship Symposium, 2016
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Society for Neuroscience, 2015
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Biomedical Engineering Society (BMES), 2012
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I like being able to laugh at myself, so here’s an incomplete poster from my first research position. I was 16 or 17 years old, and my partner finished it up and we can’t find the final version. So cut me a break… 🙂
IMSA Student Inquiry and Research Symposium, 2009
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Coming soon:
Society for Neuroscience 2017