AAAI 2022 Queer in AI Social and Mentorship Events

Organized by Raj Korpan and Danica Sutherland

About

Queer in AI’s presence at AAAI 2022 aims to create a safe and inclusive casual networking and socializing space for LGBTQIA+ individuals and allies involved with AI. We aim to create a community space where attendees can connect with each other, bond over shared experiences, and learn from each individual’s unique insights into AI, queerness, and beyond!

Structure & How to Join

We are organizing two events at AAAI:


A social event, to bring the community together. A speaker will reflect on the diversity and inclusion of the AI community, followed by an informal networking session. The event will be free and open to all. The sign-up form can be found here.


We will also host a mentorship session for undergraduate and junior grad students, where speakers will talk about their journey since undergrad, a Q&A session, and break-out groups to pair students with a more senior member of the community. To attend, please register in advance of the conference. The registration form can be found here.


Dates and times are TBD

Code of Conduct

Please read Queer in AI code of conduct which will be strictly followed at all times. Recording (screen recording or screenshots) is prohibited. All participants are expected to maintain the confidentiality of other participants.

Queer in AI adheres to Queer in AI Anti-harassment policy. Any participant who experiences harassment or hostile behavior may contact Queer in AI Safety Team. Please be assured that if you approach us, your concerns will be kept in strict confidence, and we will consult with you on any actions taken.

Organizers

For any concerns or questions, please reach out to either of us using the contact info given on our websites. Your concerns will be kept confidential.

Raj Korpan (he/him) is an Assistant Professor of Computer science at Iona College. His research is in explainable artificial intelligence with a focus on autonomous robot navigation using hierarchical planning and cognitive models. He has a Ph.D. in computer science from the Graduate Center of the City University of New York.

Danica J. Sutherland (she/they) is an Assistant Professor at the University of British Columbia. Her research interests include learning and testing on sets and distributions, learning "deep kernels," and representation learning and statistical learning theory more broadly. She completed her Ph.D. in computer science at Carnegie Mellon University.

Contact Us

Email: queerinai [at] gmail [dot] com