Pressé Lab

My research journey in the Pressé lab began at the bench, working hands-on with bacterial and C. elegans cultures, before I transitioned into Bayesian statistical modeling. This trajectory has given me a unique perspective—one that deeply considers the biological realities of experimental systems while leveraging computational approaches to extract meaning from data. By bridging these two worlds, I have developed a framework for thinking that allows me to engineer solutions to both computational and biological challenges.

Technical and Analytical Skills

Starting with experimental microbiology and worm culture, I learned how to design and execute biological experiments with precision. I worked with bacterial strains, prepared growth media, and maintained C. elegans and bacterial cultures, all while troubleshooting variability in biological conditions. These early experiences shaped my appreciation for the underlying biophysics of bacterial growth and population kinetics—how how host-to-host variability makes large effects on overall population trajectories, how stochastic fluctuations can drive population dynamics, and how external factors introduce noise into biological systems.

As I moved into Bayesian inference and simulation-based stochastic modeling, I began to see how these biological realities could be formally represented in mathematical frameworks. Bayesian methods allow us to encode prior knowledge about biological processes and systematically update our understanding based on new data. This work has given me opportunities to critically examine the assumptions embedded in theoretical models—what parameters actually represent in a physical system, how they should be interpreted, and where experimental validation is needed.

One of the most valuable lessons from this research has been the ability to design relevant control experiments based on statistical and biological reasoning. When developing models for bacterial growth, I had to ask: Are we capturing the true sources of variability, or are there hidden factors in the experimental design? These questions pushed me to think about model assumptions and their connection to physical reality—not just in theory, but in practice. Designing controls to isolate key parameters became an essential step in refining my models and ensuring that computational outputs reflected biological truth.

Multidisciplinary and Integrative Thinking

Because I have worked extensively on both the experimental and computational sides of this research, I have learned to navigate the interplay between theoretical assumptions and empirical validation. Bayesian inference provides a structured way to seek distributions over parameters, but those parameters must be meaningful in the context of biological constraints. For example, modeling microbial population kinetics requires carefully considering how growth rates fluctuate under different environmental conditions, which in turn influences how we select priors and interpret posterior distributions.

This experience has solidified my belief that the best scientists do not simply apply statistical methods in isolation or run experiments without considering theoretical underpinnings. Instead, they work iteratively—adjusting models in response to experimental findings, refining experiments based on model predictions, and constantly re-evaluating assumptions. This mindset is what allows me to engineer my way around problems, whether in computational modeling or biological experimentation.

Mentorship, Lab Work, and Experimentation

Beyond my own research, I have worked to train experimentalists in the lab, teaching them techniques in bacterial culturing, worm maintenance, and data collection while also introducing them to the statistical frameworks that help make sense of their results. This experience has reinforced my ability to communicate across disciplines, translating complex statistical concepts into practical insights for biological researchers and vice versa.

Additionally, I have contributed to developing and refining lab protocols to improve the reliability of experimental workflows. My collaboration with my labmate Stan has been especially valuable—we have worked together to design new platforms for microbial monitoring and troubleshoot issues in experimental cycles. These experiences have taught me the importance of iterative problem-solving in a research setting: nothing works perfectly on the first attempt, and the ability to adapt, refine, and optimize is essential.

Value and Impact on My Academic and Professional Goals

This research has been pivotal in shaping my approach to science. By starting with experimental work and later integrating computational modeling, I have developed a holistic understanding of the research process. I now recognize that theoretical models are only as good as the biological data they are built upon and that experimental results are most powerful when analyzed through a rigorous mathematical lens.

This work aligns with my Grand Challenges theme by contributing to scientific understanding and biomedical research through improved statistical modeling techniques. More importantly, it has shown me that being a complete scientist means being able to think both experimentally and computationally, designing models that are grounded in reality and experiments that test meaningful hypotheses. I look forward to continuing work that integrates hands-on biological research with computational problem-solving, ensuring that theoretical insights are always paired with experimental validation.