The present state of science, its organizational principles, cultural norms, prevailing paradigms of thought, and many other characteristics have often been the result of incidental, unusual, and surprising historical events. The same can be said of scientific prejudices and of those forces that have at times prevented important trains of thought from developing. As we confront the consequences of unprecedented growth of the scientific enterprise, it is essential that we continually re-examine the basic culture and infrastructure of the scientific establishment.
Metascience encompasses a broad range of topics ranging from traditional epistemology, to science policy, to data driven investigation of the research corpus. Although the term itself has been in use for at least several decades, its connotation in recent years has been shaped by efforts to address the reproducibility crisis. I am interested in a wide range of metascientific issues including innovations and reforms to the journal system, scientific funding, graduate education, large-scale collaborative endeavors (“open science”), and re-examining the historical and philosophical foundations of basic scientific methodology.
Computational Modeling, Data Science, and Software Engineering in 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 and drug design. 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.
Philosophy of Mind and Artificial Intelligence
If biophysical models are a bottom-up approach to understanding the brain and mind, then introspection might be thought of as a top-down approach. Although the past century has seen multiple world-changing revolutions in the neurosciences, our inner lives have largely remained a mystery to established scientific practices. I am interested in all aspects of introspective science / contemplative neuroscience, including fundamental philosophical investigation into consciousness and theories of mind, neuroscientific research into meditation, historical and anthropological research into Buddhist thought and other worldwide contemplative traditions, and historical and modern perspectives on psychoanalysis and its relationship to neurological disorders.
Finally, I have long-standing interests in artificial intelligence, and in particular, in the rich interdisciplinary topics at the intersection of machine learning, biophysics, and comparative neurology. I am interested in the long-term consequences of advances in artificial intelligence for the biomedical sciences, and conversely, the applications of advances in biophysical simulations of model organisms to the design of AI systems. I am also actively involved in the emerging disciplines of AI safety and AI ethics, and the applications of comparative neuroanatomy, affective neuroscience, and contemplative neuroscience for the design of AI systems.