Artificial neural network×Microfluidics

The Frontier Brief for this collision is still being written. The evidence below already reflects the current model run.

Pre-company space. No named players have staked this collision yet — an emerging pairing where the field, not a firm, is the story so far.

20 researchers publish on both sides of this collision without the fields themselves having met. Every name below is counted from papers in the corpus — not inferred.

• Arnan MitchellBeijing University of Posts and Telecommunications
  2/1
• Xinran WangPhiladelphia University
  2/1
• George G. MalliarasUniversity of California, Santa Cruz
  2/1
• Feng GuoMassachusetts Institute of Technology
  1/2
• J. SenthilnathInstitute of Materials Research and Engineering
  1/1
• Tonio BuonassisiNanyang Technological University
  1/1
• Zackaria MahfoudInstitute of Materials Research and Engineering
  1/1
• Daniil BashNational University of Singapore
  1/1
• Tan HuangInstitute of High Performance Computing
  1/1
• Siyu TianNational University of Singapore
  1/1

• Massachusetts Institute of TechnologyUS
  88/61
• Chinese Academy of SciencesCN
  104/37
• Tsinghua UniversityCN
  79/42
• Stanford UniversityUS
  112/27
• Harvard UniversityUS
  34/79
• ETH ZurichCH
  66/30

• Two-step machine learning enables optimized nanoparticle synthesis2021 · 239 citations · DOI ↗
  1
• An integrated design and fabrication strategy for entirely soft, autonomous robots2016 · 2,127 citations · DOI ↗
  2
• Liposomes: Advancements and innovation in the manufacturing process2020 · 726 citations · DOI ↗
  2
• Wettability control on multiphase flow in patterned microfluidics2016 · 624 citations · DOI ↗
  2

Counted from the corpus. Institution counts use best-effort affiliation (every author on a paper is paired with every institution on it), so read them as presence, not headcount.