Kim Nguyễn Ph.D.
(she/her)
Hi! I'm a Research Scientist at Cooper Health and Penn Medicine. I use multimodal methods including behavioral, health records, and neuroimaging to understand trends and patterns in clinical research topics and human health.
I completed my Ph.D. at Temple University and my Bachelors of Science at UT Austin. My Ph.D. aimed to decode various inputs to memory by focusing on development and maturation. I used behavioral and MRI methods (functional, structural, and diffusion) with mixed-methods analysis to resolve multi-factor human interactions.
With 8 years of research experience, I have developed proficient research design, technical problem-solving, and effective storytelling. I am looking forward to applying my expertise and expanding my toolkit into novel and exciting datasets!
git hub projects
research projects
Memory for real world navigation
Role: Lead researcher
Method: Behavioral factor analysis
Funding: NIH R01 + PhD diversity grant
Using dimensionality reduction factor analysis, I found that variables of memory for real world experiences in children and adults are intertwined across two memory factors: spatiotemporal structure and perceptual/factual/locale. This work is in prep for a peer-reviewed publication.
Preprint available here.
Neural coding of a real world experience
Role: Lead researcher
Method: fMRI univariate and multivariate analysis
Funding: NIH R01 + PhD diversity grant
Tracking neural BOLD level, I found that when children and adults have good memory of a rich tour experience, the brain codes this information for later incidental retrieval.
Children's relational language supports their spatial mapping
Role: Lead researcher
Method: Behavioral mixed-effects modeling
Funding: NIH R01
With mixed-effects linear modeling, I found that using prepositions to link environmental features during a map reading task predicted spatial mapping in a virtual paradigm. This relationship was only seen in children 6-12 years, but not adults.
Preprint available here.
Fornix integrity relates to memory behavior
Role: Researcher & mentor
Method: Behavioral and diffusion MRI
Funding: NIH R01 + PhD diversity grant
We used probabilistic tractography to reconstruct the fornix to measure tract volume and microstructure. Fornix tract volume and FAt were predictors of memory for real world encoding.
Figure: fornix reconstruction by Giovanna Arantes De Oliveira Campos
Behavioral nuances of virtual and real world navigation
Role: Researcher & mentor
Method: Behavioral
Funding: NSF EHR + PhD diversity grant
Using two virtual paradigms and one real world paradigm, we discovered that human behavior factors together based on the paradigm instead of other characteristics such as environmental build, task used, or modality.
Preprint available here.
Hippocampal CA1-2 volume positively correlated with navigation memory
Role: Researcher & mentor
Method: structural MRI
Funding: NIH R01 + PhD diversity grant
Automatic and manual QC segmentation of hippocampal subfield volume using a T2-weighted scan taking with children (8-13y) and adults. CA1-2 volume is correlated to better route efficiency and free recall of real world navigation.
relevant training
Machine Learning Statistical Foundations Professional Certificate by Wolfram Research on LinkedIn Learning
SQL Essential Training on LinkedIn Learning
Generative AI for Data Scientists Specialization, IBM on Coursera
Hippocampal Subfield Anatomy Workshop, Hippocampal Subfields Group in Albacete, Spain
Happy to chat! kimvnguyen246@gmail.com