[Jobs] Joint postdoc in LLM-Based Reasoning at Harvard and Basis Research Institute
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Karen Schroeder
Mar 8, 2024, 1:59:02 PM3/8/24
to Women in Machine Learning
Hello all,
We've opened a new Postdoctoral Fellowship on LLM-Based Reasoning and Planning, together with Nada Amin’s group at Harvard. This Fellowship is a collaborative initiative between Basis Research Institute and Harvard University’s Amin Lab. It also includes collaborators at the Hugh Kaul Precision Medicine Institute. As a fellow, you will be a key contributor to our focused efforts to research and develop fundamental methods in AI to help practitioners, including doctors and civic policymakers, make informed decisions based on vast amounts of data and information.
About Basis
Basis (https://www.basis.ai/) is a nonprofit applied AI 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.
About Nada Amin’s Group
Nada Amin’s research group explores new ways of programming that are easier, safer, faster. For safer, we look at systems and formalisms based on types and verification. For faster, we look at meta-programming techniques, including generative programming and reflection, to collapse levels of interpretations and move between different views of the same program in a way that helps optimizations, understanding, and modifications. For easier, our goal is to enable more people to manipulate computer programs (static) and processes (dynamic) robustly. To this end, we look at combining Machine Learning and Programming Languages to enable the creation of neuro-symbolic systems that can move back and forth between learnable (neural) and interpretable (symbolic) representations of a system.
Research Focus
Our research aims to develop new foundations and technologies for reasoning and planning that leverage large language models (LLMs) and integrate them with complementary approaches, including program synthesis, and causal and probabilistic programming. The goal is to create integrated systems that harness the deep domain knowledge and emergent capabilities of LLMs, alongside the precision, efficiency and correctness of formal reasoning frameworks. This could deliver the best of both worlds: the intuitive, context-rich insights of LLMs and the logical, structured analysis provided by formal methods. Key application areas include developing integrated agents that:
The research environment is both structured and adaptable, with multiple avenues for scholarly contribution. As a fellow, your expertise can shape various aspects of the project. It will deepen your foundations in multiple domains, including large language models, reasoning, planning, and precision medicine or civic policymaking while also allowing for a balance of focused research, academic exploration, and software development.
Learn more and apply here. Open until filled.
Basis (https://www.basis.ai/) is a nonprofit applied AI 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.
About Nada Amin’s Group
Nada Amin’s research group explores new ways of programming that are easier, safer, faster. For safer, we look at systems and formalisms based on types and verification. For faster, we look at meta-programming techniques, including generative programming and reflection, to collapse levels of interpretations and move between different views of the same program in a way that helps optimizations, understanding, and modifications. For easier, our goal is to enable more people to manipulate computer programs (static) and processes (dynamic) robustly. To this end, we look at combining Machine Learning and Programming Languages to enable the creation of neuro-symbolic systems that can move back and forth between learnable (neural) and interpretable (symbolic) representations of a system.
Research Focus
Our research aims to develop new foundations and technologies for reasoning and planning that leverage large language models (LLMs) and integrate them with complementary approaches, including program synthesis, and causal and probabilistic programming. The goal is to create integrated systems that harness the deep domain knowledge and emergent capabilities of LLMs, alongside the precision, efficiency and correctness of formal reasoning frameworks. This could deliver the best of both worlds: the intuitive, context-rich insights of LLMs and the logical, structured analysis provided by formal methods. Key application areas include developing integrated agents that:
- Automate data science: augmenting and automating functionalities in the data science pipeline, such as data curation, cleaning, format translation, causal and statistical analysis, and model verification.
- Reliably execute long-term plans using heterogeneous external tools: improving automated planning and decision-making in complex open environments.
The research environment is both structured and adaptable, with multiple avenues for scholarly contribution. As a fellow, your expertise can shape various aspects of the project. It will deepen your foundations in multiple domains, including large language models, reasoning, planning, and precision medicine or civic policymaking while also allowing for a balance of focused research, academic exploration, and software development.
Learn more and apply here. Open until filled.
Best,
Karen Schroeder
Research Operations
Basis Research Institute
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