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Open Phil AI Fellowship — 2021 Class

Organization Name 
Award Date 
Grant Amount 
To provide scholarship support to five machine learning researchers over five years.

Grant investigator: Daniel Dewey

This page was reviewed but not written by the grant investigator.

Open Philanthropy recommended a total of approximately $1,300,000 over five years in PhD fellowship support to five promising machine learning researchers that together represent the 2021 class of the Open Phil AI Fellowship.1 These fellows were selected from 397 applicants for their academic excellence, technical knowledge, careful reasoning, and interest in making the long-term, large-scale impacts of AI a central focus of their research. This falls within our focus area of potential risks from advanced artificial intelligence.

We believe that progress in artificial intelligence may eventually lead to changes in human civilization that are as large as the agricultural or industrial revolutions; while we think it’s most likely that this would lead to significant improvements in human well-being, we also see significant risks. Open Phil AI Fellows have a broad mandate to think through which kinds of research are likely to be most valuable, to share ideas and form a community with like-minded students and professors, and ultimately to act in the way that they think is most likely to improve outcomes from progress in AI.

The intent of the Open Phil AI Fellowship is both to support a small group of promising researchers and to foster a community with a culture of trust, debate, excitement, and intellectual excellence. We plan to host gatherings once or twice per year where fellows can get to know one another, learn about each other’s work, and connect with other researchers who share their interests.

The 2021 Class of Open Phil AI Fellows

Collin Burns

Collin is an incoming PhD student in Computer Science at UC Berkeley. He is broadly interested in doing foundational work to make AI systems more trustworthy, aligned with human values, and helpful for human decision making. He is especially excited about using language to control and interpret machine learning models more effectively. Collin received his B.A. in Computer Science from Columbia University. For more information, see his website.

Jared Quincy Davis

Jared Quincy Davis is a PhD Student in Computer Science at Stanford University. His research asks what progress is necessary for the most compelling advances of machine learning (e.g. those that powered AlphaGo) to be applied more broadly and extensively in real-world, non-stationary, multi-particle domains with nth order dynamics. Jared is motivated by the potential of such AI advances to accelerate the rate of progress in, and adoption of, technology. Thus far, his work has focused on addressing the specific computational complexity, memory, and optimization challenges that arise when learning high resolution representations of the dynamics within complex systems. Jared’s fundamental research has thus far been applied to great effect in problems spanning structural biology, industrial systems control, robotic planning and navigation, natural language processing, and beyond. To learn more about his research, visit his scholar page.

Meena Jagadeesan

Meena Jagadeesan is a first-year PhD student at UC Berkeley, advised by Moritz Hardt, Michael I. Jordan, and Jacob Steinhardt. She aims to develop theoretical foundations for machine learning systems that account for economic and societal effects, especially in strategic or dynamic environments. Her work currently focuses on reasoning about the incentives created by decision-making systems and on ensuring fairness in multi-stage systems. Meena completed her Bachelor’s and Master’s degrees at Harvard University in 2020, where she studied computer science, mathematics, and statistics. For more information, visit her website.

Jesse Mu

Jesse is a PhD student in Computer Science at Stanford University, advised by Noah Goodman and affiliated with the Stanford NLP Group and Stanford AI Lab. He is interested in using language and communication to improve the interpretability and generalization of machine learning models, especially in multimodal or embodied settings. Previously, Jesse received an MPhil in Advanced Computer Science from the University of Cambridge as a Churchill scholar, and a BS in Computer Science from Boston College. For more information, see his website.

Tan Zhi-Xuan

Xuan (Sh-YEN) is a PhD student at MIT co-advised by Vikash Mansinghka and Joshua Tenenbaum. Their research sits at the intersection of AI, philosophy, and cognitive science, asking questions like: How can we specify and perform inference over rich yet structured generative models of human motivation and bounded reasoning, in order to accurately infer human goals and values? To answer these questions, Xuan’s work includes the development of probabilistic programming infrastructure, so as to enable fast and flexible Bayesian inference over complex models of agents and their environments. Prior to MIT, Xuan worked with Desmond Ong at the National University of Singapore on deep generative models, and Brian Scassellati at Yale on human-robot interaction. They graduated from Yale with a B.S. in Electrical Engineering & Computer Science.

  • 1. This is an estimate because of uncertainty around future year tuition costs and currency exchange rates. This number may be updated as costs are finalized.