Data Science and Software Engineering in Computational Biomedicine
From high-throughput gene sequencing, to novel biological sensors, to new multi-modal imaging technologies, growth in the biomedical sciences is so expansive that we are inundated with new advances coming from many fronts. The ultimate consequence of introducing computational modeling, data science, and software engineering into the biomedical sciences will be no less than a fundamental reconceptualization in the classification, diagnosis, and treatment of disease.
I am interested in all aspects of the intersection of these fields, with a focus on realistic biophysical simulations of model organisms such as Caenorhabditis elegans and applications to the study of disease processes, drug design, and artificial intelligence. Related topics that I have pursued include the broad set of organizational and cultural issues confronting the biomedical sciences as a result of widespread automation and computational methods.
Artificial Intelligence, AI Safety, and AI Governance
I have long-standing interests in understanding the history, growth trajectories, and long-term consequences of artificial intelligence. I am interested in the applications of machine learning and artificial intelligence to the biomedical sciences, and conversely, the relevance of advances in computational biomedicine to the design of AI systems.
I am also actively involved in the emerging disciplines of AI safety, AI ethics, and AI governance. I have active collaborations pursuing a wide variety of topics, including models of AI goal structures informed by neuroscience and cognitive psychology, robust software engineering practices for systems ranging from computer algebra to biophysical simulations, the implications of AI for the dynamics of wealth and power, political aspects of AI-induced unemployment and inequality, and avenues for international cooperation in the research and governance of advanced AI systems.