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Connor T. Jerzak PRO
cjerzak
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"Nullius in verba"
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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/
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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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I Built a RAG System That Listens to Live BBC News and Answers Questions About "What Happened 10 Minutes 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:
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5 Lectures and keynotes defining AI right now
If you want to understand the multifaceted AI landscape in 2025 and see where the field is heading – start with (or revisit) these legendary talks. They can help you capture what’s happening in AI from multiple angles:
1. Andrej Karpathy: Software Is Changing (Again) → https://www.youtube.com/watch?v=LCEmiRjPEtQ
Unveils Software 3.0 – a paradigm where LLMs are the new computers, programmed with prompts instead of code. The key: developers must now master coding, training, and prompting as AI becomes the heart of software building
2. Richard Sutton, The OaK Architecture: A Vision of SuperIntelligence from Experience → https://www.youtube.com/watch?v=gEbbGyNkR2U
Unveils the OaK (Options and Knowledge) architecture – a model-based RL framework for continual intelligence, where every component learns, meta-learns & builds hierarchical abstractions
3. GTC March 2025 Keynote with NVIDIA CEO Jensen Huang → https://www.youtube.com/watch?v=_waPvOwL9Z8
Dives into the accelerated computing and the importance of Physical AI. From the Blackwell GPU architecture & AI factories to breakthroughs in agentic AI & robotics, Jensen Huang explains how NVIDIA aims to power every layer of the AI ecosystem
4. Yann LeCun "Mathematical Obstacles on the Way to Human-Level AI" → https://www.youtube.com/watch?v=ETZfkkv6V7
Yann LeCun always argues we need a new path to machines that reason about the world – not LLMs or RL. So this lecture is about self-supervised systems with world models, planning, memory and energy-based learning
5. Andrew Ng: State of AI Agents → https://www.youtube.com/watch?v=4pYzYmSdSH4
Highlights one of the most pressing topics of 2025 – agents, explaining why most effective AI agents rely on simple, linear workflows built from modular “Lego-brick” tasks + what predicts AI startup success in the new agent era
Subscribe to the Turing Post: https://www.turingpost.com/subscribe –your shortcut to deep, clear AI analysis
If you want to understand the multifaceted AI landscape in 2025 and see where the field is heading – start with (or revisit) these legendary talks. They can help you capture what’s happening in AI from multiple angles:
1. Andrej Karpathy: Software Is Changing (Again) → https://www.youtube.com/watch?v=LCEmiRjPEtQ
Unveils Software 3.0 – a paradigm where LLMs are the new computers, programmed with prompts instead of code. The key: developers must now master coding, training, and prompting as AI becomes the heart of software building
2. Richard Sutton, The OaK Architecture: A Vision of SuperIntelligence from Experience → https://www.youtube.com/watch?v=gEbbGyNkR2U
Unveils the OaK (Options and Knowledge) architecture – a model-based RL framework for continual intelligence, where every component learns, meta-learns & builds hierarchical abstractions
3. GTC March 2025 Keynote with NVIDIA CEO Jensen Huang → https://www.youtube.com/watch?v=_waPvOwL9Z8
Dives into the accelerated computing and the importance of Physical AI. From the Blackwell GPU architecture & AI factories to breakthroughs in agentic AI & robotics, Jensen Huang explains how NVIDIA aims to power every layer of the AI ecosystem
4. Yann LeCun "Mathematical Obstacles on the Way to Human-Level AI" → https://www.youtube.com/watch?v=ETZfkkv6V7
Yann LeCun always argues we need a new path to machines that reason about the world – not LLMs or RL. So this lecture is about self-supervised systems with world models, planning, memory and energy-based learning
5. Andrew Ng: State of AI Agents → https://www.youtube.com/watch?v=4pYzYmSdSH4
Highlights one of the most pressing topics of 2025 – agents, explaining why most effective AI agents rely on simple, linear workflows built from modular “Lego-brick” tasks + what predicts AI startup success in the new agent era
Subscribe to the Turing Post: https://www.turingpost.com/subscribe –your shortcut to deep, clear AI analysis
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