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My passion is answering difficult questions by understanding and predicting the behavior of complex systems — both human-engineered and those found in the natural world. How are circadian oscillations effected by dramatic changes in global temperature? How can we leverage erg rowing data dynamics to help remotely train athletes? How might daylight saving time effect health outcomes across the U.S.? What is the neurochemical basis for emotions, depression, and consciousness? How can we work to create the sustainable agriculture of the future? These questions aren’t just intellectually stimulating; but also have the capacity to inform major decisions such as which R&D endeavors merit additional funding or which disease state mechanisms are ripe for therapeutic intervention.

My expertise blends biological insight with the computational and mathematical skills of a data scientist to seek answers. I build quantitative methods subject to physical and biological constraints, then use them to analyze real data and predict real outcomes. I’ve modeled both steady state and dynamic processes across a spectrum of physical scales; created user friendly web-applications for visualizing/exploring climate models and circadian data; and collaborated with a broad range of scientists/engineers to design controlled experiments in the lab setting. My efforts have informed major decisions in allocations of funding to optimize experimental design with fixed budgetary constraints, developed statistical frameworks aimed at enhancing rigor and reproducibility in scientific research, and advanced basic understanding of fundamental biological processes.

Moving forward, I look to continue developing my skills and deploying them to actualize my values — seeking answers to questions that address problems related to the climate crisis, human health, and social/environmental justice.

Check out my past and present projects at my GitHub page (github.com/nesscoder).