Implantable BCI Systems & Translational Neuroengineering
My work spans the architecture and translation of high-bandwidth implantable brain–computer interfaces—from neural acquisition and wireless telemetry to system reliability, preclinical validation, clinical deployment, and neural information processing.
My work examines the architecture of high-bandwidth implantable neural interfaces and the engineering constraints that determine their real-world performance.
How can implantable neural interfaces acquire and transmit increasingly large amounts of neural information while remaining low-power, reliable, and clinically practical?
This work extends from experimental neurotechnology to systems designed for reliable operation in preclinical and clinical environments.
How do we translate experimental neural interfaces into reliable systems that can operate in real preclinical and clinical environments?
My research examines how neural information should be represented, preserved, transmitted, and modeled across future brain–computer interfaces.
What neural information should future BCIs preserve, transmit, and model?
System-level constraints across neural acquisition, onboard processing, wireless telemetry, power, latency, and decoding.
Understanding the tradeoff between raw neural signals, spikes, LFPs, information preservation, bandwidth, and decoding performance.
Developing system-level metrics for evaluating long-term intracortical interface stability.
Paradromics
System-level engineering and translation of implantable BCI systems spanning neural communication platforms, verification and validation, preclinical studies, regulatory testing, and early clinical deployment.
Applied Materials
Engineering of complex semiconductor manufacturing systems, with a focus on high-reliability electrical hardware and system integration.
Massachusetts General Hospital
Hao Zhen works at the intersection of implantable neurotechnology, systems engineering, and clinical translation. His experience spans BCI system architecture, neural communication hardware, verification and validation, preclinical studies, regulatory engineering, and early clinical deployment, building on earlier work in complex semiconductor manufacturing systems.
His research focuses on high-bandwidth implantable neural interfaces, neural information transmission, chronic interface reliability, distributed BCI systems, and neural decoding. He holds an M.Eng. in Engineering Management from Cornell University and a B.S. in Electrical and Computer Engineering from Northeastern University.