About me

I'm currently an NSF Postdoctoral Fellow at Brown University studying the dynamic interaction between spring and muscle; but, my interest in elastic energy began in 2005 when I discovered target archery. The goal of a target archer is simple: consistently hit a target the size of a silver dollar from the other side of the room. This takes patience, fine motor control, and most importantly, an understanding of the interaction between the body that generates force and bow that transforms that force into stored elastic energy.

The central questions that drove my archery training in 2005 are, in essence, the same questions I have today about biological elastic mechanisms. How can I configure my body position to increase my accuracy (What properties of a muscle-spring system reduce error sensitivity)? Does my long-term accuracy depend on how quickly I load my bow (do muscle-spring dynamics affect mechanical behavior)? Among my most successful teammates, are there trends in techniques or choices in equipment (What general principles govern muscle-spring mechanics across multiple systems)?

My interest in archery naturally transformed into that of elastic mechanisms in biology. And so I traded my bow and arrows for training in biomechanics and evolutionary theory. I was also fortunate enough to receive the Department of Energy Computational Science Graduate Fellowship during this time. For these reasons, I've developed a diverse skill set that includes quantitative morphology, materials testing, numerical analysis, and computer simulation.

I've used my skills to investigate elastic systems across multiple organisms (mantis shrimp, bullfrogs, grasshoppers, whales, rats), and I've generated knowledge regarding the dynamics of energy storage in biological elastic mechanisms. I currently combine experimental and systems-level modeling approaches to test hypotheses about the properties that emerge from the interaction of spring and muscle.

Computation and biology

In my experience, the need for computational training in biology couldn't be more clear. There's the extremely practical reason: biology is becoming increasingly quantitative, and the ability to leverage computational power to analyze experimental data is a necessary skill for the next generation of researchers. But, I also believe that computation can provide novel tools to probe previously unaddressed hypotheses. Computer simulation not only allows large numbers of in silico experiments to be performed in a short period of time but can also permit experimental manipulations that are more difficult to perform on specimens and systems in the real world.

The difficulty of training computational biologists does not lie in one particular field. The biological concepts, the programming, and the applied math are all relatively straightforward on their own. The major roadblock lies in the misconception that one must first master all three topics before useful models can be made.

Throughout my academic career, I've designed workshops and working-groups to break down this misconception. I've developed SOURCE (studying, originating, and understanding R code examples), a study group focused on statistical problems and computer programming, in which I focused heavily on transforming biological problems into computational problems and demonstrating their appropriate solutions. While working with SOURCE, I've created introductory R classes, produced a computer-intensive biomechanics lab manual, and been involved in multiple consultations and collaborations. I'm currently working on a series of video tutorials on scientific computing for biologists that can be accessed from anywhere on the internet.