Bio 
I received my undergraduate education at Harvey Mudd College in sunny southern California where I studied computer science and physics. I have always been interested in computational approaches to solving problems, and I became interested in physics in college. I wanted to understand why the world is the way it is as well as well as the methods by which we figure out new knowledge.
After I graduated, I decided I wanted to work on problems with more practical applications – longer battery life for my ipod, efficient solar cells, etc. So, I joined the research group of Richard Hennig in the Materials Science and Engineering department here at Cornell. I am currently in my fourth year with the group. We focus solely on computational and theoretical work, but almost all of our investigations are motivated by the desire to improve useful real-world technologies.
Outside of school, I play the guitar and train in Brazilian jiu-jitsu.
Research
I am interested in developing and using novel computational and theoretical techniques to design new materials for technological applications. In order to gain the understanding to design better materials, we are often interested in investigating the physics underlying known materials with unusual or poorly-understood properties.
For example, zinc oxide (ZnO) is a semiconducting metal oxide with potential applications to photovoltaic and other electronic devices. As the name suggests, photovoltaic materials are useful for converting light into electricity – that is, for solar cells. However, as grown in the lab, zinc-oxide unexpectedly exhibits some odd properties which are undesirable for electronic applications and are thought to be due to unknown defects in the material. If we understood the nature of these defects, we would be better prepared to design processing conditions to avoid them.
Ab-initio electronic structure methods are computational techniques that allow us to study materials with great precision by taking into account the behavior of all the electrons in every atom in the system. One of these methods, density functional theory (DFT), has been used to predict which defects are likely to be stable and thus might be responsible for these undesirable properties. Unfortunately, DFT is known to describe these properties rather poorly. We are therefore applying more accurate
methods such as Quantum Monte Carlo (QMC) to describe the system.
These methods are used to solve problems that are very computationally expensive by virtue of the need to consider many, many different possible configurations of a physical system. Instead of looking at all the possibilities, we just pick a few in an intelligent yet random way.
This use of randomness is the reason for the name Monte Carlo, a reference to the famous casino.
In addition to the study of particular known systems, I am interested in the theoretical prediction and design of new materials. To this end, we have developed a genetic search algorithm to find stable new structures.
These algorithms apply the principles that lead animal species to become well adapted to their environments in order to find novel materials that are likely to exist in nature. We are currently working on applying this method to a search for new high-temperature superconducting materials as well as systems with promise for next-generation, high-performance Li-ion batteries.