Multifunctional Integrated Nanoelectronics for the Brain


Reverse-engineering the brain demands complex approaches, which require dovetailed cross-disciplinary efforts and convergence research.  We believe nanoelectronics can be tailored to uniquely complement many other fields and practices of studying the brain through adding multifunctionality towards achieving convergence while keeping their electronic advantage to integrate and scale across spatial and temporal domains.  We refer to this multifunctional integrated nanoelectronics for the brain as neuroelectronics+.  In this talk, I will introduce our neuroelectronics+ concept.  I will also discuss several of my group’s recent examples along this vision embodied in the forms of microscopical, therapeutic, and connectomical neuroelectronics+, all enabled by new concepts in materials science, electrical engineering, and advanced manufacturing.  In addition to fundamental merit in engineering innovations, we envision the development and translation of neuroelectronics+, and more broadly, bioelectronics+ will transform both biology and medicine.

  Date and Time




  • Date: 09 Dec 2021
  • Time: 12:00 PM to 01:00 PM
  • All times are America/New_York
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  • Boston, Massachusetts
  • United States

  • Starts 22 November 2021 12:00 AM
  • Ends 09 December 2021 12:30 PM
  • All times are America/New_York
  • No Admission Charge


Dr. Hui Fang


Multifunctional Integrated Nanoelectronics for the Brain


Hui Fang received his B.S. degree in 2009 from Tsinghua University and his Ph.D. degree in 2014 from the University of California, Berkeley, both in Materials Science and Engineering.  He was then a postdoctoral fellow at the University of Illinois, Urbana-Champaign from 2014 to 2016. After starting his independent career at Northeastern University in 2016, he joined Dartmouth College in 2021 as an Associate Professor in the Thayer School of Engineering.  Fang’s research interests encompass the fields of neuroelectronics, electronic materials, and electroactive organisms.  His research has been recognized by multiple awards, including an NSF CAREER Award (2019), an NIH R01 Award (2020), and an NIH U01 Award (2021), and has been cited over 7600 times.