We're exploring directed evolution of bacteria to produce various products, including biomaterials and therapeutics, using computational methods in a lab setting.

We're using drones to analyze the built environment, focusing on processing lines rather than points, which is traditionally more challenging in computer vision.

Good engineers are drawn to challenging technical problems, and we've successfully tackled many of these challenges.

In my postdoc, I shifted towards inverse imaging problems, where the observed data is not a direct image but a transform, such as in CT scans.

Through engineering, we can create clever constructions that lead to interesting behaviors in fluid dynamics, similar to how glider guns and gates operate in the Game of Life.

At Arc Institute, we are focused on applying high-throughput scalable technologies to understand multi-system interactions, particularly at the neuro-immune interface.

There's significant potential for breakthroughs by merging different subject areas, and I've always aimed to be multidisciplinary in my own work, integrating neuroscience, machine learning, and computer science.

In collaborations with hospitals, we focus on developing algorithms that maximize the quality of high-resolution images from limited data, particularly in medical imaging.

Exploring how physicists explain complex concepts can provide valuable insights for effective communication in any field.