Basis is a nonprofit applied research organization with two mutually reinforcing goals.
Our Mission
Basis is a 501(c)(3) nonprofit applied research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society’s ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we’re building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
Who We Are
Our Team
Between our core members, advisors, and collaborators, Basis is made up of:
Zenna Tavares
Zenna Tavares is a co-founder and director of Basis. His research aims to understand reasoning, especially how we come to derive knowledge from observing and interacting with the world. He completed his Ph.D. at MIT, developing the foundations for causal probabilistic programming. He enjoys working between fields, drawing from programming languages, probabilistic machine learning, cognitive science, design, and art.
Eli Bingham
Eli Bingham is a co-founder and director of Basis, a machine learning fellow in the Data Sciences Platform at the Broad Institute of MIT and Harvard, a co-creator and core developer of the Pyro probabilistic programming language, and formerly a senior research scientist at Uber AI Labs. He focuses on research at the intersection of probabilistic machine learning, programming languages, and biology and on bringing that research to practice by delivering high-quality open-source software to many thousands of users.
Emily Mackevicius
Emily Mackevicius is a co-founder and director of Basis, where she leads the Collaborative Intelligent Systems group. She did her postdoctoral work studying memory-expert birds in the Aronov lab and the Center for Theoretical Neuroscience at Columbia, and her PhD work studying how birds learn to sing in the Fee lab at MIT. Her theoretical work is strongly grounded in experimental practice, currently high-resolution behavioral recordings of groups of animals foraging in environments ranging from NYC to Arctic Alaska.
Alyse Portera
Alyse Portera is business operations manager at Basis. She has over 20 years of business, human resources and operational management experience in the life sciences, government and non-profit sectors. Alyse was the Regional Director of Research Operations at the Howard Hughes Medical Institute and prior to that she was the Deputy Commissioner for Administration at the Westchester County Health Department in New York where she oversaw all finance functions, human resources, facilities management, information technology, emergency preparedness and corporate administrative functions.
Andy Zane
Andy Zane is a visiting researcher at Basis and a PhD candidate in UMass Amherst's College of Information and Computer Sciences. His broad research interests include Bayesian modeling, causality, and decision theory, while recent efforts have explored ways optimal experimental design can mitigate false accusations in police lineups. Previously, he led development of business intelligence and industrial automation tools in the natural resource industries, and still consults in this sector.
Anna Hidalgo
Anna Hidalgo is an operations consultant (part time) at Basis. She is helping the organization to grow its capacity and general operations. She holds a Ph.D. in Sociology (Columbia University), with subfield specialities in cultural sociology, gender, sexuality, and race.
Archana Warrier
Archana Warrier is a research trainee at Basis, advised by Zenna Tavares, and an incoming ELLIS PhD student at TU Darmstadt, advised by Angela Yu in the Computational Modelling of Intelligent Systems lab. She is interested in building agents that learn and efficiently update world models of their environment and of other agents' goals, beliefs, and abilities.
Dan Waxman
Dan Waxman is a postdoctoral fellow at Basis, working with Matt Levine at Basis and Youssef Marzouk at MIT. His work focuses on Bayesian machine learning and statistics, causal inference, and dynamical systems.
Dat Nguyen
Dat Nguyen is a joint postdoctoral fellow at Basis and Nada Amin's lab at Harvard SEAS. He works on probabilistic programming and verification, building neuro-symbolic systems that pair LLMs with SMT solvers and proof assistants for proof automation and scientific discovery.
Dmitry Batenkov
Dima Batenkov is a research scientist at Basis. He was previously an assistant professor of applied mathematics at Tel-Aviv University and a postdoctoral fellow at MIT.
Emily Bunnapradist
Emily Bunnapradist is a research resident at Basis focusing on world modeling and agentic systems. Her work involves leveraging theories of intelligence to build more capable and trustworthy AI systems.
Fritz Obermeyer
Fritz Obermeyer is a research scientist (part-time) at Basis. He works on Pyro and specializes in Bayesian machine learning. He earned a PhD in Programming Language Theory and has worked on machine learning platforms at Salesforce, Google, and Uber. Currently, he is researching applications of large language models to decision making.
Jack Feser
Jack Feser is a research scientist at Basis. He was previously an assistant professor of computer science at Hamilton College and completed his Ph.D. at MIT with Armando Solar-Lezama. His research interests are in programming languages and automated programming, particularly as applied to relational databases.
Jean Yoo
Jean Yoo is a research trainee at Basis. Her research interests broadly lie in visual computing and AI.
Karen Schroeder
Karen Schroeder is research operations manager at Basis. She was previously a postdoctoral research scientist in brain-computer interfaces and computational/systems neuroscience at Columbia’s Zuckerman Institute, and holds a Ph.D. in neural engineering from the University of Michigan. Her research interests include neuroAI, neurotechnology, and meta-science for AI research.
Kiran Gopinathan
Kiran Gopinathan is a research scientist at Basis focused on formal verification, program synthesis, type systems, language design, and proof engineering. Her work spans proof repair, verified compilation, and formal coordination languages for trustworthy AI systems. She pioneered the field of proof repair for imperative programs (Sisyphus, PLDI 2023 Distinguished Paper) and produced the first formally verified proof of the probabilistic properties of the Bloom Filter.
Martin Jankowiak
Martin Jankowiak is a research scientist (part-time) at Basis, a machine learning fellow in the Data Sciences Platform at the Broad Institute of MIT and Harvard and a co-creator and core developer of Pyro and NumPyro. He was previously a senior research scientist at Uber AI Labs and has a PhD in theoretical physics from Stanford. He works on a wide range of topics across probabilistic machine learning, high-dimensional statistics and applications in computational biology.
Matthew Levine
Matthew Levine develops methods at the intersection of machine learning, dynamical systems, and uncertainty quantification, with a focus on improving prediction and inference in biological and physical systems. His work blends mechanistic modeling, probabilistic inference, and modern machine learning to build principled tools that hold up in real, noisy settings.
Michelangelo Naim
Michi is a research scientist at Basis, working at the intersection of AI, physics, and math. His work focuses on computational theories of intelligence, including reasoning, learning, and decision-making, alongside open-source software.
Nick Jourjine
Nick Jourjine is a postdoctoral research scientist at Basis. He is interested in bridging bioacoustics and social neuroscience, with a focus on tools for automated behavior tracking in the wild. Previously, he was a postdoc with Hopi Hoekstra at Harvard University, where he studied the evolution of neural mechanisms that support vocal communication in deer mice, and Anna Lindholm at the University of Zürich, where he studied seasonal social dynamics in wild house mice.
Rafal Urbaniak
Rafal Urbaniak is a research scientist at Basis, specializing in Bayesian methods and causal probabilistic programming. His interests include causal explanation and abstraction, criminal evidence evaluation, bias in natural language processing, and online aggression. At Basis, he develops advanced tools for causal explanation to inform policy-making decisions and identify collaboration strategies in animal behavior. Rafal holds a Ph.D. in Logic and Philosophy of Mathematics from the University of Calgary and has held positions at the Research Foundation Flanders, Trinity College Dublin, the University of Bristol, and the University of Gdansk.
Ralph Peterson
Ralph Peterson is a postdoctoral fellow at Basis, where he develops multi-agent models to study animal behavior. He is currently conducting fieldwork on collective behavior in NYC’s urban rat populations. Ralph earned his PhD in Neural Science from NYU, where he investigated vocal communication and auditory processing in social rodents using computational neuroethological approaches. Prior to his PhD, he developed quantitative methods for analyzing animal behavior at Harvard Medical School.
Ravi Deedwania
Ravi Deedwania is an operations consultant at Basis. He has previously worked as a product manager at venture-backed startups and a research assistant at the White House Council of Economic Advisors. He has an S.B. in economics from MIT and a JD from Yale Law School.
Sreela Kodali
Sreela Kodali is a research scientist at Basis focusing on novel embedded systems and robotics platforms. She previously completed her PhD in Electrical Engineering at Stanford University where she collaborated with the NIH and led interdisciplinary research in human perception, medical devices, and wearable design. Her professional interests include environmental and health applications, reconfigurable hardware, and robotic systems.
Tim Cooijmans
Tim Cooijmans is a research scientist at Basis. His research focuses on the dynamics and stability of gradient-based learning, particularly in challenging systems such as recurrent and attentive neural networks and multi-agent reinforcement learning. He holds a PhD from Mila. Tim has previously worked on radiation simulation tooling at CERN, on music generation at Google Brain's Magenta and on approximate automatic differentiation at DeepMind.
Yair Shenfeld
Yair Shenfeld is a research scientist (part-time) at Basis. He is an assistant professor of applied mathematics at Brown University. Currently he works on generative modeling and optimal transport.
Yichao Liang
Yichao Liang is a research intern at Basis and a PhD student at the Computational and Biological Learning Lab (CBL) at Cambridge, supervised by Adrian Weller. His research focuses on the complementary goals of understanding and developing generalist agents, drawing on diverse traditions in AI research, including formal languages, probabilistic modeling, deep learning, and planning.
Yixiu Zhao
Yixiu Zhao is a research engineer at Basis. He holds an applied physics Ph.D. from Stanford University, where his research focused on probabilistic inference and generative models. Yixiu is broadly interested in how humans and AIs understand the world by building abstractions from observation and interaction.
Yiyun Liu
Yiyun Liu is a research scientist at Basis working on type theory and formal verification. His PhD work with Stephanie Weirich at the University of Pennsylvania designed dependent type systems and mechanized their correctness properties in the Rocq proof assistant, with publications at POPL, ICFP, OOPSLA, and CSF.
Want to work with us? Learn more about opportunities to join our research collaborations, and career opportunities, here.
Advisors
Armando Solar-Lezama ↗︎
Solar-Lezama is a professor at MIT EECS and an associate director and the COO of MIT CSAIL, where he leads the Computer-Aided Programming Group.
Joshua Tenenbaum ↗︎
Tenenbaum is professor of Computational Cognitive Science in MIT’s Department of Brain and Cognitive Sciences, Scientific Director with the MIT Quest for Intelligence, an investigator at the Center for Brains, Minds and Machines and MIT CSAIL, and a MacArthur Fellow.
Kevin Ellis ↗︎
Ellis is an assistant professor of computer science at Cornell, where his group works on artificial intelligence, deep learning and program synthesis.
Anthony Philippakis ↗︎
Philippakis is chief data officer of the Broad Institute of MIT and Harvard, co-director of the Eric and Wendy Schmidt Center for AI and Biology, a venture partner at GV, and a cardiologist at Brigham and Women’s Hospital.
Rui Costa ↗︎
Costa is a professor of neuroscience and outgoing CEO of the Zuckerman Institute at Columbia, the incoming CEO of the Allen Institute, and an elected member of the European Molecular Biology Organization and the National Academy of Medicine.
Contact Us
If you are interested in supporting our mission to create a new space for AI research with financial or other resources, or collaborating with us on either core technology or any of our current challenge projects, please reach us at contact@basis.ai.
If you’d like to stay up to date on Basis news, software releases, or challenge projects please join our mailing list.
FAQ
Where is Basis located?
Basis has offices in New York City and Cambridge, Massachusetts.
When was Basis founded?
Basis was founded in 2022 by Zenna Tavares, Emily Mackevicius, and Eli Bingham.
Who is on Basis’s advisory board?
Basis is advised by Armando Solar-Lezama, Joshua Tenenbaum, Kevin Ellis, Anthony Philippakis, and Rui Costa.
Is Basis a for profit business?
No. Basis is a 501(c)(3) nonprofit research institute that builds open-source software.
What is Basis’s mission?
Basis has two goals: (1) advance basic scientific research in artificial intelligence and (2) help solve society’s intractable problems. To accomplish these goals, we seek to discover mathematical principles of reasoning, learning, and decision making, and to implement these principles in open-source software.
How is work at Basis organized?
Work at Basis is organized as a cycle between core technology and applied challenge projects. We (i) develop advanced technologies in probabilistic modeling and causal inference, (ii) leverage these advances in applied projects such as AI-driven robot design, dynamical modeling for biological systems, and participatory city modeling, and (iii) use the new questions that emerge from our applied work to guide further methodological research and technology development.
How is Basis funded?
Basis is supported through a combination of research grants, private philanthropy, and contract work with partners. We welcome inquiries from organizations and individuals interested in supporting our work. To discuss potential funding opportunities, please contact us at contact@basis.ai.
What does partnership with Basis look like?
Basis consults with municipal governments, participates in academic collaborations, and contracts with for-profit companies. We welcome partnerships that help advance our research and extend the reach of our work. If you are interested in collaborating with us, please contact us at contact@basis.ai.
I’m interested in employment opportunities at Basis. Who should I contact?
You can find all of our open employment opportunities on our careers page. We also offer postdoctoral fellowships.
How can I stay up-to-date on what Basis is doing?
You can sign up for the Basis mailing list. You can also learn more about our recent work on our blog.