Organizers

Sara Hooker

Sara is the head of Cohere For AI, a non-profit research lab that seeks to solve complex ML problems while creating more points of entry into machine learning research. Previously, she was a Research Scientist at Google Brain doing DL research on reliable explanations of model predictions for black-box models.

Rosanne Liu

Rosanne is the Co-founder and Executive Director of ML Collective, a non-profit organization providing research training and support for all. She is also a researcher at Google Brain. Previously, she was a founding member of Uber AI. She obtained her Ph.D. in CS at Northwestern University, and published research at several top venues.

Pablo Samuel Castro

Pablo is Ecuadorian, and moved to Montreal after high school to study at McGill. He obtained his masters and Ph.D. at McGill, focusing on Reinforcement Learning. He is a staff research Software Developer in Google Research (Brain team) in Montreal. His research focuses on fundamental RL ML, and Creativity.

Fatemehsadat Mireshghallah

Fatemehsadat is a Ph.D. Candidate at the CSE department of UC San Diego. Her research interests are Trustworthy Machine Learning and Natural Language Processing. She is a recipient of the National Center for Women & IT (NCWIT) Collegiate award. She is a co-chair of Widening NLP (WiNLP), and D&I chair at NAACL 2022.

Sunipa Dev

Sunipa (she/her) is a Research Scientist at Google Research. Her work is primarily at the intersection of language and society with the goal to make AI technologies more inclusive, and socio-culturally aware. Previously, she was an NSF Computing Innovation Fellow at UCLA, before which she completed her Ph.D. at the University of Utah.

Benjamin Rosman

Benjamin is a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand, South Africa, where he runs the Robotics, Autonomous Intelligence, and Learning (RAIL) Laboratory. He is the Director of the National E-Science Postgraduate Teaching and Training Platform (NEPTTP).

João G.M Araújo

João (he/him) is a Brazilian undergraduate in CS at the University of São Paulo. He is an ML Engineer at Cohere, an Canadian NLP startup. He has organized lecture series on Theoretical Deep Learning, as well as, panel discussions on different deep learning topics, all of which are composed by eminent researchers in the field.

Savannah Thais

Savannah is an Associate Research Scholar at the Princeton Institute for CSE, focusing on geometric deep learning for physical and social systems. She is a former member of the WiML Board of Directors, the Machine Learning Knowledge Convener of the CMS Experiment at CERN, and the founder of Comunity Insight and Impact.

Tejumade Afonja

Tejumade is a doctoral researcher at Cispa Helmholtz Center for Information Security. She co-founded AISaturdays (in Lagos, Nigeria), an AI community that teaches free ML classes. She worked with Vibrant Emotional Health through CMU's Data Science for Social Good fellowship program, to help people experiencing mental health crisis get timely help.

Tyler Zhu

Tyler is a master's student at UC Berkeley focused on computer vision. He also attended there for his undergrad. While at Berkeley, he was heavily involved in Machine Learning @ Berkeley, a student organization dedicated to fostering an undergraduate ML community through research collaborations, industry partnerships, and mentorship.

Sunny Sanyal

Sunny is a third-year Ph.D. student at the University of Texas at Austin. This summer he worked as an applied science in Alexa AI at Amazon Science, Sunnyvale. He is broadly interested in knowledge distillation, vision and language for small devices. Previously, he worked in wireless 5G and received several research awards for his work.

Swapneel Mehta

Swapneel is a Ph.D. candidate at New York University's Center for Data Science researching online disinformation using tools from causality and probabilistic machine learning. He was an ML Engineering intern with Twitter Inc., worked on recommender systems with Adobe Research, and ML for particle physics at CERN.