Postdoctoral Fellow, MARA; Modeling, Abstraction and Reasoning Agents

About the Fellowship

This Basis Postdoctoral Fellowship is a collaborative initiative between the Basis Research Institute and Cornell University's Ellis Lab. As a fellow, you will be a key contributor to our ambitious MARA (Modeling, Abstraction, and Reasoning Agent) project, which aims to develop foundational AI technologies that enable systems to actively discover abstract models of the world and reason with them to achieve goals.

Basis is a nonprofit applied AIresearch organization with two mutually reinforcing goals.

The first is 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 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.

Kevin Ellis is an Assistant Professor in the Computer Science department at Cornell University. His research focuses on artificial intelligence, program synthesis, and the intersection of AI and cognitive science. The Ellis Lab explores how to build AI systems that learn and reason like humans, particularly in areas such as programming by example, world modeling, neural-symbolic integration, and few-shot learning. The group combines techniques from machine learning, program synthesis, probabilistic programming, and cognitive science to develop AI systems that can learn complex tasks from limited data and generalize across domains.

Our research aims to develop new foundations and technologies for modeling, abstraction, and reasoning in AI systems, focusing on the MARA project. MARA's general goal is to build systems that actively discover abstract models of the world and reason with these models to carry out goals. Building these systems will demand advances in knowledge representation, abstraction, reasoning, active learning, and a first-principles rethinking of what it means to model the world.

The immediate mission of MARA is to solve the Abstract Reasoning Corpus (ARC) in a way that generalizes to other domains
, with the broader mission of building systems capable of learning in an open, growing portfolio of domains using human-comparable amounts of data and interaction.

Fellows will have the opportunity to contribute to this ambitious project, working closely with a team of researchers at Basis and Cornell University. The research environment is both structured and adaptable, providing multiple avenues for scholarly contribution. As a fellow, your expertise can shape various aspects of the project, allowing for a balance of focused research, academic exploration, and software development.

• Researchers holding a PhD in computer science, artificial intelligence, machine learning, cognitive science, or related fields.
• Strong background in areas such as program synthesis, probabilistic programming, machine learning, AI reasoning systems, and cognitive modeling.
• Experience in developing AI systems that combine neural and symbolic methods is highly valued.
• Interest in foundational AI research and its applications to modeling, abstraction, and reasoning.
• Individuals with a demonstrated track record in scientific research, evidenced through publications, technical reports, or impactful software projects.

• Conduct independent and collaborative research focused on the MARA project.
• Develop new methods and algorithms for modeling, abstraction, and reasoning in AI systems.
• Apply these methods to concrete challenges such as the Abstract Reasoning Corpus (ARC) and other domains.
• Disseminate research findings through academic publications and presentations at leading conferences.
• Actively engage in knowledge transfer within Basis…