Welcome to my personal website. I'm currently helping conduct posttraining and human data research at Thinking Machines Lab. Prior to this, I worked in research at OpenAI for a little more than 2 years. My research interests at OpenAI focused on the symbiosis of human and machine intelligence. I am deeply interested both in the ways in which these new tools can be used to enhance human capabilities and understanding and in the ways in which human intelligence can be used to improve that of the machines. The interplay between human and machine intelligence is a fascinating topic, and I'm excited to continue working on it and am very pleased that I've been able to conduct this research under the tutelage of people who genuinely care about ensuring that the epochal changes brought about by this technology are for the betterment of our species writ large.
Before my move to industry I was certain I'd keep my career within academia. I completed my Ph.D. in Applied Mathematics at the University of Waterloo (May, 2022) in the Math Medicine Lab under the guidance of Mohammad Kohandel. My academic journey also includes an M.Sc. from McMaster University under Gail Wolkowicz and a B.Sc. (Hons) in Math and Computer Science from Redeemer University with a thesis supervised by Kevin Vander Meulen. Throughout my career, I've been fortunate to receive recognition for both my teaching and research contributions. I love teaching and am very grateful for the opportunities that I've had to help students discover their own aptitudes and interests.
My current research interests leverage my Computer Science background more than my Applied Mathematics foundation—though I continue to be fascinated by the remarkable parallels between these disciplines. My focus lies in Artificial General Intelligence research, encompassing safety, alignment, and the implications of self-improving AI systems. The capabilities of these large networks both inspire and humble me, and I feel a profound responsibility to contribute, however modestly, to guiding the development of this transformative era in computing.
During my graduate studies, I concentrated on computational biology and mathematical modeling of biological systems and processes. My Ph.D. thesis specifically explored the application of machine learning/AI techniques in conjunction with differential equations and stochastic models to investigate cancer stem cell evolution and treatment response in human cancers. The intricate complexity of biological systems continues to fascinate me, and I aim to illuminate their behavior through stochastic machine learning approaches paired with conventional models. After all, as illustrated by this XKCD comic, nature rarely arranges itself for straightforward description, even if patterns emerge in the aggregate.
This website houses information about my background, research interests, teaching resources, and current activities. Please don't hesitate to reach out if you have any questions or would like to connect!