“From the very start, you get this incubator feeling.”
At Meharry SACS, we don’t just train computational scientists — we cultivate discoverers. Our doctoral students arrive with questions and leave as pioneers, equipped not only with technical expertise, but also with the vision and values to transform healthcare through responsible AI and data science.
The student you’re about to hear from embodies what makes our program unique. From day one, Jaylin Dyson, Ph.D. student and research assistant in data science, has been immersed in publication-ready research, surrounded by faculty who see potential in everycurious question, and empowered to turn classroom ideas into real-world solutions.
Dyson’s journey illustrates what becomes possible when rigorous training meets an environment that celebrates both innovation and equity. This is what computational medicine looks like at Meharry SACS.
How does Meharry enable doctoral students to conduct high-level computational research early in their programs?
It’s encouraged from day one to participate in existing high-level computational research or to start exploring your own. Each course taken here often culminates in a final project that is publication-ready work. Everyone here is research-minded, so many conversations will lead to a research idea, and many interactions with a faculty member lead to a new research direction. So, from the very start, you get this incubator feeling. For example, I’ve already been able to take ideas from class, turn them into a structured project, and get feedback that pushes it toward something you could actually submit or build on.
You’re interested in launching an AI/NLP company in healthcare. How has Meharry encouraged entrepreneurial thinking?
I think they encourage my entrepreneurial thinking by giving me the know-how on building things that are outside of the box. I think many people go about their daily lives with some good ideas in passing, but what Meharry has done for me is that it has taught me to sit down with that idea, prototype it, and make it. And oftentimes creating these things out of curiosity leads to the ideas of monetizing it. For example, instead of leaving an idea as just a thought, I’ll sketch the workflow, identify what data it needs, and build a first working version to see if it solves a real problem.
AI can reinforce bias. How has Meharry’s focus on equity influenced your approach to
“Here at Meharry, we take an oath...do no harm with the technology we create.”
I believe that because we are surrounded by people who have traditionally been marginalized or who have firsthand experience navigating bias in our societal systems and day-to-day life, we tend to take this perspective a little more seriously than those who have not experienced these things. Oftentimes, when we are reviewing literature for a new AI tool, we notice a gap where bias has barely been reviewed and is therefore ingrained in the AI system. Here at Meharry, we take an oath, and it is well known throughout our department to do no harm with the technology we create. This oath was created in a similar spirit to the Hippocratic oath or other well-known oaths in health care. So we are not only taught to build but also taught to think about what bias can exist within our system, whether it be racial, biological or a system flaw. From the inception of building, we view bias as an intrinsic part, not just something we fix at the end. For example, I try to think early about who is missing from the data, what assumptions the model is learning, and whether performance holds across different subgroups before I trust the outputs.
How have you grown as a researcher since starting at Meharry?
"[Research] often reminds me of one of my favorite TV shows, Star Trek, in which every episode features a place that has never been explored by humans before...This is largely due to my growth here at Meharry."
I believe that because we are surrounded by people who have traditionally been marginalized or who have firsthand experience navigating bias in our societal systems and day-to-day life, we tend to take this perspective a little more seriously than those who have not experienced these things. Oftentimes, when we are reviewing literature for a new AI tool, we notice a gap where bias has barely been reviewed and is therefore ingrained in the AI system. Here at Meharry, we take an oath, and it is well known throughout our department to do no harm with the technology we create. This oath was created in a similar spirit to the Hippocratic oath or other well-known oaths in health care. So we are not only taught to build but also taught to think about what bias can exist within our system, whether it be racial, biological or a system flaw. From the inception of building, we view bias as an intrinsic part, not just something we fix at the end. For example, I try to think early about who is missing from the data, what assumptions the model is learning, and whether performance holds across different subgroups before I trust the outputs.
How have you grown as a researcher since starting at Meharry?
I’ve become truly enchanted by the process of research. At first, I thought nothing under the sun could be discovered by me, and I often wondered what could be brought up that hasn’t already been. However, as I delved deeper, I realized there are far more unknown facts, undiscovered nuances, and knowledge gaps than there are people available to address them. In fact, there aren’t enough PhDs in the world to address all these gaps. I take great satisfaction in developing solutions to these research gaps and in being among the first to explore or uncover them. It often reminds me of one of my favorite TV shows, Star Trek, in which every episode features a place that has never been explored by humans before. That is how I view research, and this is largely due to my growth here at Meharry.
What advice would you give to students who want to work at the intersection of AI and medicine?
What led you from software development into computational medicine, and how did Meharry SACS support that shift? I think the number one thing I would tell them is to stay curious at all times, be observant, and read as much as possible about the newest technology and non-technology-related topics. To work in this space requires the ability to connect the dots, not to know everything, but to know when they connect and how to act when they do. Some of the best ideas I’ve come up with have come from observing topics I have no idea about, and in those observations, I find connections to things that can be resolved easily with computational tools. What led me from software development into computational medicine was realizing that the same building mindset can directly impact patient outcomes and discovery when it is applied to medical problems and biomedical data. Meharry supported that shift by putting me in research-minded environments early, where coursework and faculty interactions naturally push you toward real computational research questions and projects. You’ve called data science the next frontier in medical discovery.
What experiences at Meharry shaped that view?
I believe just the sheer volume of data nowadays makes it nearly impossible for humans to contribute anything beyond creating a computational tool to extract what is going on. I remember learning math in middle school, and our teacher told us that we could do math that they couldn’t do when they were younger, simply because we had a calculator that could compute in seconds, whereas it would have taken them hours to do by hand. And because of that calculator, we could do more math and theoretically create and discover more. I believe we are at this pivotal moment again, but this time not with calculators but with computational power that exists, allowing us to analyze data that would take the entire duration of human existence to sort through. My experience at Meharry has shown me this. For example, we can take rare diseases and millions of other diseases and find connections between them to repurpose drugs to target the rare disease based on what we know they do in other diseases. And computationally, we can do that rapidly, accelerating lab discovery. This was highlighted during the COVID-19 pandemic. But beyond the lab in healthcare, we can use the same concepts to increase patient outcomes and satisfaction by developing models and providing medical data to discover new and efficient ways to administer healthcare.
Your Research Day project applied GeneEA to analyze BRCA1 protein interactions. In simple terms, what problem were you solving?
So, what we did here was pull in a list of genes that interact with BRCA1, which is known to be highly associated with breast cancer. Using a biological database, we looked up what those genes do—things like pathways and other internal functions. We then used computational tools to compare what showed up in our BRCA1 interactor list against what you would expect by chance given a defined background gene set, which is basically an enrichment check. Based on this output, if something is overrepresented, meaning it appears over and over again compared to what would be expected, then it suggests that pathway or function may be meaningfully connected to BRCA1’s biology and gives us testable hypotheses. The outputs of these results can give us a host of input into how breast cancer and this gene in particular functions, things that may be associated with it, and even therapeutic discoveries or hypotheses that can be tested in a lab and connected to existing knowledge.
Your Next Chapter Starts Here
Dyson’s story is just one of many unfolding at Meharry SACS. In 2025 alone, our students were published in 17 journals, delivered 21 conference presentations, and showcased 23 poster presentations—120 contributions to advancing knowledge and transforming healthcare. But numbers only tell part of the story. What truly defines Meharry SACS is the culture our students describe: the incubator feeling from day one, the faculty who turn conversations into collaborations, the oath to do no harm with the technology we create, and the community that holds us accountable to building AI systems that serve everyone — especially those who have been marginalized by traditional healthcare systems.
If you’re ready to be more than a programmer or data analyst — if you want to be a discoverer, an innovator, and a force for equity in computational medicine — Meharry SACS is where your journey begins. Here, your curiosity becomes discovery. Your ideas become solutions. And your work becomes the future of healthcare.
Ready to explore your own uncharted territory? Join us.
