Going all the way back to its roots in alchemy, chemistry is a science of transformation – and it has always involved interactions at the disciplinary boundaries of a wide range of fields, including natural science, technology, mathematics, philosophy, psychology, spirituality, and medicine. My own research practice as very much entrenched in this trans-disciplinary tradition, and I intentionally strive to bring together an eclectic mix of chemists, computer scientists, artists, and philosophers, all of whom are working to make progress on questions which arise at the interface of these fields. Broadly speaking, my interests involve the development and application of virtual reality, machine learning, and high-performance computing technologies to re-imagine abstract concepts within the field of nano-scale transformation and change.
More specifically, my current research interests include:
- Applying state-of-the-art computational technologies like high-performance computing, machine learning, and virtual reality to scientific simulation and visualization
- Exploring how new forms of immersive digital technology mediate interactions between people, and its potential for enabling us to rethink our relationships to ourselves, others, and the natural world
- Understanding the statistical mechanics of high-dimensional search landscapes in a range of areas, including quantum mechanics, atmospheric chemistry, and biochemistry. Natural systems evolve as complicated coupled networks, and we develop algorithmic machinery (e.g., using Markov-type master equation approaches) aimed at characterizing these networks.
- Understanding the micro-physics of energy transfer under both equilibrium and also non-equilibrium conditions: How do microscopic systems acquire energy from their surroundings? And how do they utilize the energy they acquire?
- Developing strategies to accelerate rare events and path sampling using both automated algorithms and human-guided strategies. For example, I am working to develop algorithmic tools which could eventually be used in domains like pharmacological discovery.