Ladani Lab: Modeling for Early Cancer Detection

In Summer 2022, I worked in the Ladani Lab at ASU, contributing to the development of a handheld ultrasound device for early-stage breast cancer detection (DCIS). My role focused on creating a phantom breast tissue and tumor model with realistic acoustic properties, bridging materials science and biomedical imaging. This experience strengthened my skills in polymer formulation, experimental design, and time-series data analysis, exposing me to the challenges of signal processing—specifically, distinguishing meaningful biological signals from noise.

At the time, I was mainly thinking in terms of how to make something work, but I quickly realized that true engineering is about more than function—it’s about precision, accessibility, and robustness in real-world conditions. Seeing the challenges of noise in ultrasound signals, I became fascinated by the limitations of current statistical modeling in medical imaging. This motivated me to pursue a mathematics double major, not just to apply built-in tools but to understand the deep theoretical underpinnings of signal processing and probabilistic inference. This realization shaped my later research directions, reinforcing my commitment to developing better statistical models for medical technologies—ones that aren’t just functional in controlled lab conditions but truly reliable for clinical decision-making.

Medical Microrobotics Lab: Magnetic Control for Targeted Therapies

From Fall 2022 to Fall 2023, I worked in Dr. Hakan Ceylan’s lab, designing magnetically actuated microrobots for gut microbiome sampling and immune cell delivery. My work spanned multiple research areas:

1. Developing a bellows-based, magnetic-coercivity-driven GI tract capsule endoscope for minimally invasive gut sampling.

2. Conducting experiments and authoring methodology for a paper on magnetically steered “Cellborgs” for targeted immunotherapy.

3. Writing a literature review on magnetic nanoparticles in biomedical microrobotics to aid researchers in expanding this field.

This experience was pivotal in shaping my professional goals as a biomedical engineer. I entered the lab with an interest in targeted therapies and noninvasive diagnostics, but by working in the context of Mayo Clinic, I gained a more holistic view of medical device development. I saw how cost, manufacturability, and physician workflows dictate whether an innovation will ever reach patients. This reinforced a core principle I now carry: engineering for medicine is not just about making something possible—it’s about making it practical and scalable.

Beyond the technical skills, this lab also shaped how I think about personalized medicine. I had always been interested in precision medicine, but through my work on gut microbiome sampling and immune cell therapies, I began to see how individualized biological environments demand customized interventions. This further refined my research focus: I want to develop engineering-driven frameworks that capture biological variability, rather than treating all patients as statistical averages.

Pressé Lab: Computational Modeling of Gut Microbiome Dynamics

In Dr. Steve Pressé’s lab, I develop experimental and computational tools to improve biological data analysis, with a focus on bacterial population kinetics modeling in infection and gut microbiome contexts. My work involves:

1. Designing novel experimental approaches to quantitatively characterize gut bacteria at an individual level within host-pathogen systems.

2. Advancing Bayesian inference and machine learning methods to model bacterial growth and interactions in the gut of C. elegans.

This lab brought together all the threads of my previous research experiences—statistical modeling, biomedical technology, and biological complexity—into one unifying focus. While my past projects involved building devices, I came to realize that even the best-designed tools are only as useful as the data they generate and the models that interpret them. In this lab, I’ve moved beyond just collecting data to developing better ways to analyze and extract meaning from it, using computational physics and machine learning to capture biological dynamics with greater accuracy.

This experience has fundamentally shifted my perspective on how we design medical solutions. Many biomedical models today are overly simplistic, ignoring key biological complexities. By improving how we model microbial dynamics at an individual level, I aim to uncover biologically significant variations between hosts, enhance our ability to predict bacterial ecology, and ultimately contribute to more personalized, data-driven approaches in healthcare. This lab has given me the computational tools I need to advance my long-term goal: developing precision medicine frameworks that account for biological variability, rather than working around it.

Bridging Research to My Future as a Biomedical Engineer

Each of these research experiences has built upon the last, shaping my trajectory as a biomedical engineer dedicated to precision medicine. In the Ladani Lab, I learned how to construct models of biological tissues and analyze signals. In the Medical Microrobotics Lab, I saw how biological variability and clinical constraints shape medical technology development. And in the Pressé Lab, I have refined my ability to develop computational methods that make sense of biological complexity.

At the heart of all my research is the same central question: How do we develop medical technologies that account for individual biological differences, rather than erasing them? Whether through statistical modeling, experimental design, or biomedical devices, I am driven by the challenge of engineering solutions that are not just functional but truly personalized, accessible, and impactful.