Unbabel/wmt22-cometkiwi-da
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reacted to cjerzak's post with 🔥 3 months ago
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Ethos: In our team at UT Austin, we train students to become full-stack researchers—and increasingly, designers of the systems that do research. Our students learn to carry projects end-to-end: from idea generation and theory to data creation, analysis, and iterative refinement across diverse subfields. Using modern AI (including agentic workflows) and scalable computation, students build reproducible pipelines that can ingest and update planetary-scale data—like satellite imagery and other high-dimensional sources. But the goal isn’t tool use for its own sake: students learn to set the objectives, constraints, and evaluation standards that guide these systems through large spaces of hypotheses, while grounding results in causal inference and careful measurement. The outcome is scholarship that can rigorously test policy counterfactuals and translate evidence into durable, responsible improvements in societal well-being.
We welcome students at every stage to engage with projects—from motivated high-schoolers to undergraduates, graduate students, and those from highly non-traditional backgrounds.
Join us! https://connorjerzak.com/students/
We welcome students at every stage to engage with projects—from motivated high-schoolers to undergraduates, graduate students, and those from highly non-traditional backgrounds.
Join us! https://connorjerzak.com/students/
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reacted to cjerzak's post with 🧠👀 7 months ago
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>>> We're writing a new book, <Planetary Causal Inference>, on how to model counterfactuals at planetary scale by combining satellite imagery + other global data with local studies and RCTs. Forthcoming in 2026+.
>>> Book info: https://planetarycausalinference.org/book-launch
>>> All datasets used in the book will be openly available on our lab’s Hugging Face hub:
theaidevlab
>>> Book info: https://planetarycausalinference.org/book-launch
>>> All datasets used in the book will be openly available on our lab’s Hugging Face hub:
upvoted a paper 12 months ago
reacted to cjerzak's post with 🚀👀 about 1 year ago
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Scaling laws for multi‐scale dynamics: stacking resolutions uncovers hidden structure and drives predictable performance gains. Multi‐scale works because it blends coarse and fine contexts into a unified representation.
Dive into the math and experiments here: https://planetarycausalinference.org/scaling-laws-for-multi-scale/
Dive into the math and experiments here: https://planetarycausalinference.org/scaling-laws-for-multi-scale/