Sensing
Short description: Developing nanoscale tools to measure electrical, chemical, and mechanical signals from living systems with high spatial and temporal resolution.
Long description: Cells communicate through fast and dynamic electrical, chemical, and mechanical signals that are still difficult to measure at the right scale. Existing tools often lack the spatial precision, temporal resolution, or long-term compatibility needed to study these processes in complex biological systems. We develop nanoscale bioelectronic platforms that enable sensitive, real-time measurements in cells, tissues, and engineered biological models. These technologies help reveal how cells communicate, respond to stress, and change in health and disease.
Relevant papers: https://onlinelibrary.wiley.com/doi/full/10.1002/smsc.70249
https://www.biorxiv.org/content/10.64898/2026.02.13.705576v1.abstract
https://www.nature.com/articles/s41565-025-02032-w
https://www.nature.com/articles/s41378-024-00817-y
Nanoelectronics for Real-Time, Parallel Intracellular Sensing
Nanoelectrode output
Input signal
Delivery
Short description: Engineering nanostructured platforms for precise intracellular and nuclear access, transport, and control.
Long description: Delivering molecules to the correct intracellular compartment remains a major barrier in biology and medicine. This challenge is especially important for cargos that must reach specific sites such as the nucleus, where gene regulation and expression occur. We engineer nanostructured interfaces and delivery technologies that enable controlled access to cellular barriers and compartments. Our goal is to create precise and scalable strategies for intracellular delivery, cell engineering, and therapeutic intervention.
Relevant papers:
https://advanced.onlinelibrary.wiley.com/doi/full/10.1002/adfm.202410035
https://pubs.acs.org/doi/full/10.1021/acsnano.4c03743
Curvature-Induced Nuclear Poration for Targeted Delivery
Intelligence
Short description: Using AI, quantitative analysis, and physics-informed models to decode complex biological data and guide discovery.
Long description: The growing complexity of biological data has made interpretation a major bottleneck for discovery. High-dimensional electrical, chemical, and imaging datasets often contain important patterns that are difficult to detect using conventional analysis alone. We develop computational approaches that combine machine learning, quantitative analysis, and physics-informed modeling to transform complex measurements into interpretable biological information. These methods help us uncover mechanisms, classify cell states, and design smarter sensing and delivery technologies.
Relevant papers:
https://onlinelibrary.wiley.com/doi/full/10.1002/smll.202504402
https://www.nature.com/articles/s41467-024-55571-6
