ZZ Zifang Zhao Neural engineering and bioelectronics

Profile

Research Profile

Portrait of Zifang Zhao

Zifang "Frank" Zhao

Research profile

Zifang Zhao is a researcher in neural engineering and bioelectronics working on implantable and wearable systems for closed-loop neuroscience. His training spans neuroscience, electrical engineering, embedded systems, and bioelectronic materials across Peking University, NYU Langone Medical Center, Columbia University, and Cornell University.

Peking University Training in neuroscience and electrical engineering, including early work on pain-related electrophysiology and neural data analysis.
NYU Langone Medical Center Closed-loop network manipulation and high-resolution human ECoG analysis in systems neuroscience settings.
Columbia University and Cornell University Implantable neuroelectronics, flexible biointerfaces, ionic communication, and wireless multimodal neural recording.
Research scope
  • Implantable and wearable neural interfaces
  • Closed-loop sensing and stimulation
  • Organic, iontronic, and epidermal bioelectronics
  • Embedded systems and neural data methods
Research themes Curriculum vitae

Direction

Collaboration and Research Environment

Current collaboration interests

  • Joint work in neural engineering, systems neuroscience, and bioelectronics.
  • Projects that connect device platforms to mechanistic neuroscience questions.
  • Collaborations in embedded systems, biointerfaces, and neural data methods.

Working and mentoring style

  • Projects are framed around a concrete neuroscience or measurement problem.
  • Device design, analysis, and experiments are developed together.
  • Mentoring emphasizes rigor, technical depth, and interdisciplinary fluency.

Research direction

I build neural technologies as experimental tools for answering circuit-level questions, especially when standard wired or short-term interfaces are limiting.

Collaboration style

I value collaborations that combine deep technical development with clear biological hypotheses and careful validation.