Biomedical Engineering PhD Student at Washington University in St. Louis
Previously Postgraduate Research Associate at Yale University
B.Sc. in Biomedical Engineering from University of Ghana
I am a Biomedical Engineering PhD student in the Chen Ultrasound Lab at Washington University in St. Louis. I am interested in brain state-dependent closed-loop focused ultrasound neuromodulation, and more broadly in our ability to both read from and write to the brain: decoding what ongoing neural activity tells us about the state of a circuit, and using that to shape when and where we intervene.
Before starting my PhD, I was a postgraduate research associate in the AZA Lab at Yale University with AZA Allsop, MD, PhD, where I built a neural data analysis tool for clustering neural responses into functional ensembles. I also studied how music and mindfulness affect psychiatric and psychological conditions across a range of settings, combining EEG, heart rate, surveys, and interviews.
I obtained a B.Sc in Biomedical Engineering at the University of Ghana, where I was fortunate to be advised by Prof. Samuel K Kwofie.
I am committed to mentorship, and am an organizer of the NeuroAI workshops at the Deep Learning Indaba.
Selected projects spanning neural ensemble decoding, mental health, and translational neuromodulation.
Closing the loop between reading from the brain and writing to it: decoding ongoing neural activity and the brain states it reflects, then using focused ultrasound to write back non-invasively at the moment the circuit is most receptive.
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An unbiased method to partition diverse neuronal responses into functional ensembles, revealing interpretable population dynamics during innate social behavior.
Read preprint
How music mindfulness acutely enhances autonomic activity and psychological state in adults experiencing anxiety and depression.
Read paper
Machine learning for biomedical problems, including anti-inflammatory compound screening with AICpred and clinical imaging models for radiology support.
Read paperMost recent publications on Google Scholar.
Functional Encoding Units represent interpretable attractor-like dynamics in neural ensembles
NeuroAI practical tutorial
An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior
Machine Learning Models Predict Small Molecules as Potential Biotherapeutics for COVID-19-induced Cytokine Storm
Machine Learning Models Predict Small Molecules as Potential Biotherapeutics for COVID-19-induced Cytokine Storm
Instructor · YCCI Exposures Program
Workshop Facilitator · Health Professional Recruitment Exposures Program
Co-organizer · NeuroAI Workshops at Deep Learning Indaba
Member · Yale Postgraduate Associate Professional Development Committee
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