Tiernan Kennedy
Ph.D. in Computer Science & Engineering, University of Washington
B.S. in Chemistry · Minor in Mathematics · Certificate in Interdisciplinary Biomedicine, UMass Amherst
I integrate theory, computation, and molecular engineering to convert new molecular understanding into robust technologies—and engineered systems into new means of discovery.
My work connects mechanistic discovery, computational abstraction, and experimental engineering. I focus on systems where mechanisms are only partially observable, useful representations are not obvious in advance, objectives conflict, and experiments are expensive. I ask what can be learned from limited measurements, which interventions can distinguish competing explanations, and how models, experimental design, optimization, and automation can make each cycle of research more informative than the last.
My doctoral research with Chris Thachuk at the University of Washington focused on robust molecular programming and DNA nanotechnology. Rather than treating robustness as a single performance metric, I studied distinct sources of failure and developed computational and experimental multi-objective optimization strategies spanning circuit architecture, sequence design, and chemically expanded genetic alphabets. This work examined how composable design principles can balance performance and desigtn tractability to produce molecular computing systems that are robust by design.
The same perspective motivates my work on untangling observability and learnability in autonomous learning of reaction network structures from experimental data, mechanism-forward discovery and synthetic validation of functional RNAs, and closed-loop experimental automation. Across these areas, I use engineering not only to construct new systems, but to elucidate core sets of design principles that underly emergent complexity and provide mechanistic targets for discovery—and discovery not only to explain existing systems, but to uncover new engineering capabilities.
My training in chemistry, biomedicine, and computer science allows me to connect molecular mechanism, biochemical experimentation, algorithmic modeling, optimization, and research infrastructure. My goal is not simply to explore larger spaces more quickly, but to develop scientific processes in which understanding and technological capability accumulate together.