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Broadening Research Collaborations
Sara Hooker · Rosanne Liu · Pablo Samuel Castro · FatemehSadat Mireshghallah · Sunipa Dev · Benjamin Rosman · João Madeira Araújo · Savannah Thais · Sara Hooker · Sunny Sanyal · Tejumade Afonja · Swapneel Mehta · Tyler Zhu

Sat Dec 03 06:45 AM -- 03:00 PM (PST) @ Room 394-395
Event URL: https://sites.google.com/view/broadening-collaboration-in-ml/ »

This workshop aims to discuss the challenges and opportunities of expanding research collaborations in light of the changing landscape of where, how, and by whom research is produced. Progress toward democratizing AI research has been centered around making knowledge (e.g. class materials), established ideas (e.g. papers), and technologies (e.g. code, compute) more accessible. However, open, online resources are only part of the equation. Growth as a researcher requires not only learning by consuming information individually, but hands-on practice whiteboarding, coding, plotting, debugging, and writing collaboratively, with either mentors or peers. Of course, making "collaborators" more universally accessible is fundamentally more difficult than, say, ensuring all can access arXiv papers because scaling people and research groups is much harder than scaling websites. Can we nevertheless make access to collaboration itself more open?

Author Information

Sara Hooker (Cohere For AI)
Rosanne Liu (ML Collective, Google Brain)
Pablo Samuel Castro (Google)
FatemehSadat Mireshghallah (University of California San Diego)
Sunipa Dev (Google Research)

Computing Innovation Fellow 2020, Research Assistant at University of Utah, Postdoctoral Fellow at UCLA starting Jan 2020. Research interests are Responsible and Interpretable AI, NLP and Algorithmic Fairness.

Benjamin Rosman (University of the Witwatersrand)
João Madeira Araújo (University of São Paulo)
Savannah Thais (Columbia University)
Sara Hooker (Cohere For AI)

I lead Cohere For AI, a non-profit research lab that seeks to solve complex machine learning problems. We support fundamental research that explores the unknown, and are focused on creating more points of entry into machine learning research. Prior to Cohere, I was a research scientist Google Brain doing work on training models that go beyond test-set accuracy to fulfill multiple desired criteria -- interpretable, compact, fair and robust. I enjoy working on research problems where progress translates to reliable and accessible machine learning in the real-world.

Sunny Sanyal (The University of Texas at Austin)
Sunny Sanyal

I am a PhD student in the Department of Electrical and Computer Engineering at the University of Texas at Austin. Although I am broadly interested in computer vision tasks, my primary research interests lie in the area of Vision and Language pretraining, Knowledge Distillation and Self-Supervised Learning. Last summer (2022) I interned at Amazon's Alexa team and worked on a new feature for the amazon echo devices. I graduated with an M.Eng. degree in Information and Communication Engineering from Chongqing University of Posts and Telecommunications, Chongqing, China in 2019, and received a B.Tech degree in Electronics and Communication Engineering from the Maulana Abul Kalam Azad University of Technology (formerly known as West Bengal University of Technology), Kolkata, India. During my undergrad, I gloriously failed to scale up my startup, Tronix India, and later worked in an Indian multinational IT firm, TechMahindra. I received the Chinese Government Scholarship (nominated by MHRD, India) for my master's studies. I also received an Honorary mention award in IEEE ComSoc student competition 2018, Excellent master's thesis Award 2019, and Outstanding international student award 2019. Also, I have served as a reviewer, TPC, PC, and publicity co-chair for some top journal(s) and conferences.

Tejumade Afonja (Saarland University)

Tejumade Afonja is a Graduate Student at Saarland University studying Computer Science. Previously, she worked as an AI Software Engineer at InstaDeep Nigeria. She holds a B.Tech in Mechanical Engineering from Ladoke Akintola University of Technology (2015). She’s currently a remote research intern at Vector Institute where she is conducting research in the areas of privacy, security, and machine learning. Tejumade is the co-founder of AI Saturdays Lagos, an AI community in Lagos, Nigeria focused on conducting research and teaching machine learning related subjects to Nigerian youths. Tejumade is one of the 2020 Google EMEA Women Techmakers Scholar. Tejumade was a co-organizer for ML4D 2019 NeurIPS workshop and she is serving as the lead organizer this year. She is affiliated with several other workshops like BIA, WIML, ICLR, Deep Learning Indaba, AI4D, and DSA where she occasionally serves as a volunteer or mentor.

Swapneel Mehta (New York University)

I am a Ph.D. student at NYU Data Science working with the Center for Social Media and Politics and collaborating with folks at Oxford University. My research deals with controlling misinformation on social networks using tools from simulation-based inference and causality. I use probabilistic programs to simulate user behavior and information propagation on social networks.

Tyler Zhu (University of California, Berkeley / Machine Learning @ Berkeley)

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