Community Spotlight #8: Michelle Eliason
October 27, 2025
My name is Michelle Eliason, and I’m a PhD Candidate in Rehabilitation Science at the University at Buffalo, where I study the neural and cognitive mechanisms of aging and recovery following acquired brain injury. I’m also the founder of Buffalo Occupational Therapy (est. 2018), a private practice focused on cognitive–motor rehabilitation for adults with neurological complexity.
Tell us about your background.
I’ve been a practicing occupational therapist since 2016, specializing in cognitive rehabilitation, executive function, and dual-tasking methodologies. My program director, Dr. Cristian Cuadra, encouraged me to explore Dr. Sook-Lei Liew’s work when I shared my growing interest in cognitive neuroscience and data science as tools for advancing rehabilitation research. He also recommended the ReproRehab Fellowship as a next step in my development. When I was accepted and attended the kickoff, I was surrounded by like-minded, data-driven colleagues who share a vision for rigorous, reproducible rehabilitation science. I immediately knew I was exactly where I needed to be.
What sparked your interest in learning data science?
When I began analyzing FreeSurfer cortical metrics and functional connectivity data for my dissertation, I realized how powerful data science can be in strengthening occupational therapy research through rigor and reproducibility. Learning R has allowed me to move beyond static results and start building transparent, systematic pipelines that make clinical research both accessible and scalable. Data science, for me, is not just a technical skill. It’s the bridge between neuroimaging and clinical relevance, empowering our profession to communicate objectively and credibly within the broader scientific community.
What are you most excited about ReproRehab?
ReproRehab represents everything I believe the future of occupational therapy research within rehabilitation science should be—rigorous, scalable, open, reproducible, and collaborative. I’m especially excited to learn from peers across disciplines who are redefining how we collect, analyze, and share rehabilitation data. Listening to the presentations during the kickoff was incredibly inspiring! It confirmed that rigor and creativity can coexist. I want to be an advocate for this within my profession.
Is there anything you’re especially looking forward to doing with your newfound programming and data science skills?
I’m eager to apply the R skills, data science concepts, and organizational techniques I’m learning in Pod 3 (woot woot!) to both my current manuscripts and my dissertation research. My goal is to contribute to aging and rehabilitation science through transparent, open-source, reproducible workflows that others in graduate and clinical research can adapt, learn from, and build upon. I am hopeful that supporting this mission will aid in accelerating the science we need around tDCS, cortical morphometry, and functional connectivity. I also hope to mentor rehabilitation clinicians who want to integrate data literacy and reproducible research practices into their professional behaviors, strengthening the scientific foundation of our field.
Anything you are passionate about outside of ReproRehab?
Outside of research and clinical work, I’m passionate about professional advocacy and mentorship. I enjoy creating video montages of family and friends, hiking with my family, and having deep conversations about anything.
Anything else you would like to share? (website/github/podcast)
