Leadership & mentoring

Building teams for hard measurements

I lead experimental, computational, and theoretical programs by defining a shared quantitative question, assembling complementary capabilities, and keeping measurement, model, and inference connected from the outset.

Cross-scaleresearch from molecules to cells to tissues
Multidisciplinaryphysical scientists and biomedical engineers
2018–presentgroup leadership at NIST

Scientific leadership

Biophysical & Biomedical Measurement at NIST

I direct NIST’s primary group for physical measurements in biology. The group brings together physical scientists, biomedical engineers, associate researchers, and theorists to develop devices, measurement methods, and quantitative frameworks across biomolecules, cells, and tissues. Programs include cytometry, single-molecule sensing, electronic biophysical measurements, organ-on-chip systems, and quantitative response to drugs and other perturbations.

My role is to set multi-year scientific strategy, develop new programs, manage resources and staffing, connect experimental projects to quantitative theory, and create collaborations that allow capabilities distributed across institutions to function as an integrated program.

Program building

Creating structures that enable collaboration

I spearheaded a clearer framework for defining NIST’s role in funded collaborations with academic and clinical partners. This has helped make responsibilities and contributions to shared experimental programs more concrete from the outset.

I now participate as a Co-Investigator on a Wake Forest-led NIH U01 in human biodosimetry, contributing NIST measurement and modeling capabilities within the broader team. More generally, I develop research roadmaps that connect near-term measurements and publications to longer-term capabilities in high-throughput measurement, hierarchical biological systems, and experimental biomolecular dynamics.

Leadership principles

Organizing research

Start with the quantity

Define what must be measured or inferred before selecting an instrument, dataset, model, or algorithm.

Align teams and data

Experimentalists, theorists, and data scientists work best when signals, assumptions, metadata, and uncertainty are shared objects rather than handoffs.

Build methods that last

A successful project should create not only a result, but also a method, framework, dataset, or collaboration structure that enables the next problem.

Mentoring

Selected mentoring

My mentoring spans biomolecular simulation, nanoscale transport, spectroscopy, quantum information, and scientific method development. This list highlights research themes rather than attempting to reproduce an institutional staff directory.

Swapnil BaralBiomolecular representations, nucleic-acid ensembles, and clustering methods
Justin ElenewskiBiomolecular energy transport and quantum/classical methods
Subin SahuIon and thermal transport through nanopores and biomolecular systems
Christoph RohmannNanomaterials, adsorption, catalysis, and computational convergence
Daniel GrussScalable simulation of nonequilibrium quantum transport
Satvik ManjiganiNanopore data quality and transferable inference; mentored from high-school through undergraduate research
Mackenzie KincaidQuantum information, foundations, and decoherence
Andy SveskoQuantum information, thermodynamics, and the quantum-to-classical transition

Teaching

From physical intuition to working methods

As an Assistant Professor of Physics at Oregon State University, I designed and taught graduate courses and maintained an average teaching evaluation of 5.4/6.0. My teaching emphasizes the movement between physical intuition, mathematical structure, computational implementation, and experimental consequence.

Students should leave a course able not only to reproduce a derivation, but to recognize the assumptions that make it useful, test its limits, and translate it into a calculation or measurement.