NeurIPS 2020 Social on Open Collaboration in ML Research

Improving the AI research field to be more open, inviting, inclusive and fair requires never-ending efforts. Recent movements of open-source and open-access in ML research, including MOOCs, accessible code and software, papers, numerous blogs, youtube channels and podcasts dissecting research papers, as well as conferences removing barriers of entrance have all contributed to the great goal of democratizing AI research. Moving forward, the next thing to open up is collaboration and mentorship opportunities. We’ve already seen great examples set by both individuals actively outreaching, and organizations dedicated to produce research in an openly collaborative manner. How to move forward open collaboration even more in ML research? This social is organized to foster discussions around this topic.


Tuesday, 8 December 2020, 2pm - 4pm PT / 5pm - 7pm ET / 11pm - 1am EU / 6am - 8am Taipei

Link to Google Calendar


  1. Rosanne Liu, ML Collective
  2. Ashley Edwards, DeepMind
  3. Jason Yosinski, ML Collective

Confirmed panelists and mentors

  1. Eric Jang, Google
  2. Subutai Ahmad, Numenta
  3. Feryal Behbahani, DeepMind
  4. Sara Hooker, Google Brain
  5. Kyunghyun Cho, NYU
  6. Olga Russakovsky, Princeton & AI4ALL
  7. Jeremy Howard,
  8. Devin Guillory, UC Berkeley
  9. Laurent Dinh, Google Brain

Planned Activities

  1. Panel on “Making ML research more open”
    1. How has working with different, diverse people impacted your career?
    2. How has opening up your team, and/or active outreaching generated positive outcomes; what are the difficulties/challenges (e.g. IP/legal concerns)?
    3. What parts of ML research need to be further opened?
    4. How can we distribute opportunities more fairly?
  2. Open mentoring sessions: selected researchers will be holding (virtual) office hours; participants are free to swing by (virtual) offices for casual research discussions.
  3. Random paired-off collaborator dating using we will use this fun platform to randomly pair participants up for a fixed-time icebreaker chat, in the hope of planting seeds for future collaboration.