The Song Lab is led by Dr. Shang Song, an assistant professor whose early-career work has been recognized by the prestigious NIH Director's New Innovator Award (DP2), an American Heart Association Career Development Award, Rising Stars honors from MIT, Johns Hopkins, and Columbia, a Ralph E. Powe Junior Faculty Award, and an Arizona Biomedical Research Centre New Investigator Award — a record that reflects both external confidence in her research agenda and active, diversified funding. Her work spans neural engineering, organ-on-chip systems, conductive biomaterials, and cancer microenvironment modeling, published in venues including Nature Biotechnology, Nature Communications, and Biomaterials.

What makes this research program exciting is a genuine technical throughline: the lab's command of MEMS microfabrication and conducting biomaterials allows it to operate at a resolution — nanoscale membrane selectivity, precisely tuned electrical microenvironments, morphing electronics that adapt to growing tissue — that most biological or clinical labs simply cannot access. That specificity matters. The BBB-on-chip work addresses a real and longstanding bottleneck: the failure rate of CNS drug candidates in clinical trials remains among the highest of any indication, in large part because animal models do not faithfully recapitulate human BBB permeability. Microfluidic platforms that can discriminate at the molecular scale represent a meaningful step toward changing that. The peripheral nerve work is similarly non-incremental — autograft remains the clinical gold standard with well-documented limitations, and the lab's approach of combining conductive polymer nerve guides with wireless electromagnetic modulation of transplanted stem cells addresses the biological problem from both the structural and the signaling side simultaneously, as shown in Biomaterials (2021) and Nature Communications (2022).

For students, training in this environment means developing fluency in precision microfabrication, bioelectronics, stem cell models, and in vitro tissue platforms — tools that are increasingly central to where the field is heading. As AI-driven drug discovery matures, it creates urgent demand for physiologically accurate human tissue models that can generate reliable data at scale; the organ-on-chip and biomaterial platforms being built here are precisely the kind of experimental infrastructure that will feed that pipeline. Students work directly with Dr. Song in a small lab where research directions are shaped collaboratively, not handed down.