Our research aims to “develop machine learning algorithms for human-like general artificial intelligence” that can help us solve the hard problems of humanity. We seek to achieve this by creating learning algorithms fundamental to perception, cognition, and action. We hope that this computational approach will also shed light on the inner workings of the human mind.
What We Work On
We're not chasing benchmarks. Our research targets the foundations of general intelligence:
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Next-generation architectures — beyond transformers-as-default
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Reasoning & abstraction — structured, compositional, transferable
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Neural & Neurosymbolic World models & long-horizon planning
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Causal discovery & reasoning
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Embodied AI & robot learning
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Agent AI & Scientist AI
What You Will Do
You'll own a research question. You'll get close mentorship from day one, participate in weekly seminars, present papers, and engage in real research discussions. Strong projects are developed toward workshop or conference submissions.
Requirements
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Minimum 8 weeks, full-time
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Must be physically present in the lab during the internship
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Basic background in machine learning and deep learning
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Experience with PyTorch preferred
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Candidates who can continue into the following semester are strongly preferred
What You Get
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Competitive stipend
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GPU resources & research infrastructure
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Direct mentorship from faculty and senior researchers
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A research environment that takes ideas seriously
Selection Criteria
We care more about how you think than what's on your CV.
We look for candidates who:
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Genuine curiosity about research problems (not just engineering interest)
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Ability to read, question, and engage with papers
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Strong problem-solving instincts
Bonus
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Prior research experience
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Project portfolio
How to Apply
1.
CV / Resume
2.
Academic transcript
3.
(Optional) GitHub, publications, or project portfolio