The ISHI Student Ambassadors remind us that the future of forensic science is being shaped right now by students who are unafraid to tackle complex questions. Each spotlight in this series shares their research, their journeys, and the impact they hope to make on the field.
Meet Maria Flores, who is diving into one of the most exciting frontiers in forensic DNA: epigenetics. Her work focuses on developing non-linear models for age estimation, moving beyond traditional approaches that often struggle to capture the complexity of DNA methylation patterns. By improving prediction accuracy—especially in younger age groups—Maria is helping to advance methods that could strengthen human identification in meaningful ways.
For Maria, though, this research is about more than numbers and models. It’s about ensuring forensic tools are equitable, rigorously validated, and beneficial across all communities. Her persistence through coding challenges and dataset hurdles reflects the resilience it takes to innovate in emerging areas of science, and her vision is clear: forensic methods that are both cutting-edge and fair.
Tell us the story of your research. What sparked the question, and what have you discovered so far?
During my lab rotations, I joined Dr. Matteo Pellegrini’s group, where I was introduced to research focused on DNA methylation. I quickly realized this was a strong fit, as DNA methylation was an emerging topic in forensic science and Dr.Pellegrini’s expertise in methods development offered the best environment to explore it. At the time, I knew DNA methylation was being applied to age estimation models in forensics, but I realized a key limitation: most existing approaches relied on linear regression. This felt counterintuitive since age-associated methylation markers, such as ELOVL2, show non-linear patterns over the lifespan. This observation led me to ask: how can we better leverage DNA methylation to improve age estimation models beyond what has already been published? My research has focused on developing and validating a non-linear model for age estimation that captures these complex methylation patterns. So far, our results suggest that non-linear approaches can improve prediction accuracy, particularly in younger age groups where traditional models often struggle.
What drew you to this specific topic—and why does it matter to you personally or professionally?
What initially drew me to this topic was how newly emerging DNA methylation research is within the field of forensic science. As a scientist, I find it exciting to take on areas that are still developing, where there is an opportunity to learn, innovate, and contribute to shaping the field. For me, forensic research carries both personal and professional significance. I deeply value using science as a tool to advance our communities, and I believe forensic applications have a profound societal impact. Scientific tools have not always been applied equitably across all groups, making it especially important to me to approach this work in a way that ensures it is beneficial for all communities.
What’s one method or part of your research process that you found unexpectedly challenging or exciting?
I think the most challenging part of this work was identifying and accessing publicly available/published datasets. Also, developing a method is inherently challenging in general. Computation can be discouraging at times, especially when a bug or small mistake sends you back through your code to trace the source. While computation can be frustrating, I did find it exciting that after all that frustration our approach outperformed the results reported in the original study from which one of the datasets was derived.
Was there a moment during your research where something clicked—or didn’t go as expected? How did you adapt?
There wasn’t a single dramatic moment where everything suddenly clicked or went wrong, but there were many smaller challenges that tested my persistence. For me, one of the biggest hurdles was the computation because some moments included tracking down a stubborn coding error or revisiting my analysis to double-check where something had gone off. I developed patience, leaned on my problem-solving skills, and was willing to take a step back when I felt stuck. I remembered that research progress happens in increments and resilience was just as important as the final result.
What real-world problem do you hope your research helps to solve, and who do you hope it impacts most?
The identification of unknown individuals and the narrowing of suspect pools in criminal investigations are real-world challenges that I hope my research can help address. To me it is essential that forensic methods are validated rigorously and tested across the broadest possible range of individuals. My goal is to continue developing methods and models that are reliable and applicable for all groups
If someone only remembers one thing from your poster, what do you hope it is?
While everyone at the conference has invested significant effort into both their research and the way it is presented, my hope is that people walk away from my poster remembering the central insight of my research. For me, that means not just the findings themselves, but the way the work is communicated. It is important to me that I can explain my work in a way that is accessible and easy to follow.
Looking ahead, what’s the next question you’re itching to explore?
Looking ahead, I want to continue pursuing research questions that ensure forensic tools are fair and considerate of all groups. One direction I am excited about is expanding age estimation studies to larger, more diverse datasets, so that we can better test and validate models. I am also eager to explore new applications of DNA methylation beyond age estimation and investigate how epigenetic markers might provide additional insights that strengthen forensic science as a whole.