Jia Liu (Assistant Professor in Bioengineering at Harvard School of Engineering and Applied Sciences)

Understanding brain function through large-scale brain-computer interfaces (BCIs) is essential for deciphering neural dynamics, treating neurological disorders, and developing advanced neuroprosthetics. A major challenge in the field is to achieve simultaneous, large-scale, stable recording of neural activity, with single-cell resolution, millisecond precision, and cell-type specificity across three-dimensional (3D) brain tissue, throughout development, learning, and aging. In this talk, I will introduce a suite of soft and flexible bioelectronic technologies engineered to address this challenge in brain-computer interfaces. First, I will present tissue-like bioelectronics, capable of tracking the activity of individual neurons in behaving animals across their entire adult life. Then, I will discuss the electrochemical limitations of soft materials and our strategies to overcome them, establishing a scalable platform for large-scale, stable, and long-term brain mapping with a pathway toward human clinical translation. Next, I will discuss the creation of “cyborg organisms” by integrating stretchable, mesh-like electrode arrays into 2D sheets of stem/progenitor cells that undergo 2D-to-3D morphogenesis to form brain organoids or embryonic brains, enabling continuous 3D electrophysiological recording during development. I will then highlight how the brain’s dynamic nature—and the challenge of capturing neural changes over time—can be addressed by the stable recording enabled by flexible BCIs to decode neural intrinsic signal drift. These platforms support long-term, adaptive neural decoding and facilitate integration with neuromorphic algorithms for real-time interpretation of intrinsic neural dynamics. Building on this, I will introduce DriftNet, a deep neural network framework inspired by neural dynamics. DriftNet mitigates catastrophic forgetting, outperforming conventional and state-of-the-art lifelong learning models, and equips large language models with cost-effective, lifelong learning capabilities. Finally, I will present our latest efforts integrating 3D single-cell spatial transcriptomics, electrophysiology, and agentic AI to map brain activity with cell-type specificity. I will conclude by outlining a future vision in which soft and flexible electronics, spatial omics, and AI agents converge to construct a comprehensive brain cell functional atlas, transforming next-generation BCI applications.

Professor Liu received his Ph.D. in Chemistry from Harvard University in 2014 and completed postdoctoral training at Stanford University in 2018. He joined Harvard School of Engineering and Applied Sciences as an Assistant Professor in 2019. At Harvard, his lab develops tissue-integrated intelligent bioelectronic systems that merge flexible and soft electronics with living tissues and combine multimodal in situ characterization with deep learning and agentic AI to decode and control biological processes. His research spans tissue-like bioelectronics and cyborg organoids to AI-driven multimodal analysis and tissue functional control for understanding neural dynamics, organ function, and disease mechanisms. Professor Liu has pioneered new paradigms in bioelectronics, establishing foundations for soft electronic materials, nanoarchitectures for tissue-like electronics, and AI-integrated bioelectronic systems. His work was recognized as a milestone in bioelectronics by Science (2013, 2017) and was selected among Top 10 World-Changing Ideas and Most Notable Chemistry Research (2015). He has received numerous honors, including MIT Technology Review’s “Innovators Under 35” (Global List), the AFOSR Young Investigator Program (YIP) Award, the NIH/NIDDK Catalyst Award (from the NIH Director’s Pioneer Award Program), the William F. Milton Award, and the Aramont Award for Emerging Science Research Fellowship. Professor Liu is also a co-founder and scientific advisor of several deep-tech startups translating his lab’s innovations into practice, including Axoft, Elastro, MorphMind (AIScientist), and NanoRythmics.