Wearable computer×Reinforcement learning

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.

7 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.

• Di WuAuburn University
  1/3
• U. Rajendra AcharyaPolitecnico di Torino
  2/1
• Mingyi RaoFudan University
  1/1
• Cecilia LaschiUniversity of California San Diego
  1/1
• Abbas KhosraviSingapore Polytechnic
  1/1
• Geyong MinUniversity of Passau
  1/1
• Md. Jalil PiranUniversity of Exeter
  1/1

• Stanford UniversityUS
  90/70
• University of California, BerkeleyUS
  67/74
• Carnegie Mellon UniversityUS
  58/59
• Massachusetts Institute of TechnologyUS
  89/25
• Tsinghua UniversityCN
  76/28
• Nanyang Technological UniversitySG
  68/30

• Optimizing Federated Learning on Non-IID Data with Reinforcement Learning2020 · 1,000 citations · DOI ↗
  2
• Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles2020 · 748 citations · DOI ↗
  2
• Integrating blockchain for data sharing and collaboration in mobile healthcare applications2017 · 700 citations · DOI ↗
  2
• Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence2019 · 576 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.