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@@ -117,15 +117,15 @@ The **AI & Global Development Lab** fuses modern machine‑learning with Ear
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  ## Recent Team Tutorials
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- - [**Isaac Corley**](https://isaac.earth/) presents a guest lecture: [*Best Practices for Cloud-Native GeoAI*](https://aidevlab.org/cloud-geo-ai/)
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  - [**Adel Daoud** ](https://adeldaoud.com/)presents: [*A First Course in Planetary Causal Inference: Confounding*](https://planetarycausalinference.org/transcript-pci-tutorial-2025/) (@IC2S2 2025)
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  - [**Adel Daoud** ](https://adeldaoud.com/)presents: [*Planetary Causal Inference*: Overview](https://planetarycausalinference.org/transcript-pci-seminar-yale/) (@Yale)
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  - [**Adel Daoud** ](https://adeldaoud.com/)presents: [*Seminar on AI & Global Development*](https://aidevlab.org/transcript-ai-global-dev-lab-harvard-astrostatistics/) (@Harvard Astrostatistics)
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  - [**Connor Jerzak**](https://connorjerzak.com/) presents: [*Seeing Like a Satellite While Learning Across Scales: Remote Audits + Multi-Scale Optimization for Heterogeneity*](https://planetarycausalinference.org/transcript-seeing-like-a-satellite/) (@Columbia)
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  - [**Satiyabooshan Murugaboopathy**](https://www.linkedin.com/in/msatiya/?originalSubdomain=de) presents: *[Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?](https://aidevlab.org/transcript-platonic/)*
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- - [**Markus Pettersson**](https://www.linkedin.com/in/pettersson-markus/) presents: *[Debiasing ML Predictions for Causal Inference Without Additional Ground Truth Data](https://www.youtube.com/watch?v=jE4glnPmOuY)*
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  - [**Kazuki Sakamoto**](https://www.linkedin.com/in/kazukisakamoto/) presents: *[A Scoping Review of Earth Observation and Machine Learning for Causal Inference](https://connorjerzak.com/transcript-a-scoping-review-pci/)*
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- - [**Fucheng Warren Zhu**](https://www.warrenzhu.com/) presents: [*Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using EO and Computer Vision*](https://connorjerzak.com/transcript-encoding-multi-level-dynamics-in-effect-heterogeneity-estimation/)
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  ## Repositories
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  ## Recent Team Tutorials
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+ - [**Isaac Corley**](https://isaac.earth/) presents a guest lecture: [*Best Practices for Cloud-Native GeoAI*](https://aidevlab.org/cloud-geo-ai/) (@Gov 355M)
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  - [**Adel Daoud** ](https://adeldaoud.com/)presents: [*A First Course in Planetary Causal Inference: Confounding*](https://planetarycausalinference.org/transcript-pci-tutorial-2025/) (@IC2S2 2025)
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  - [**Adel Daoud** ](https://adeldaoud.com/)presents: [*Planetary Causal Inference*: Overview](https://planetarycausalinference.org/transcript-pci-seminar-yale/) (@Yale)
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  - [**Adel Daoud** ](https://adeldaoud.com/)presents: [*Seminar on AI & Global Development*](https://aidevlab.org/transcript-ai-global-dev-lab-harvard-astrostatistics/) (@Harvard Astrostatistics)
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  - [**Connor Jerzak**](https://connorjerzak.com/) presents: [*Seeing Like a Satellite While Learning Across Scales: Remote Audits + Multi-Scale Optimization for Heterogeneity*](https://planetarycausalinference.org/transcript-seeing-like-a-satellite/) (@Columbia)
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  - [**Satiyabooshan Murugaboopathy**](https://www.linkedin.com/in/msatiya/?originalSubdomain=de) presents: *[Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?](https://aidevlab.org/transcript-platonic/)*
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+ - [**Markus Pettersson**](https://www.linkedin.com/in/pettersson-markus/) presents: *[Debiasing ML Predictions for Causal Inference Without Additional Ground Truth Data](https://www.youtube.com/watch?v=jE4glnPmOuY)* (@AAAI)
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  - [**Kazuki Sakamoto**](https://www.linkedin.com/in/kazukisakamoto/) presents: *[A Scoping Review of Earth Observation and Machine Learning for Causal Inference](https://connorjerzak.com/transcript-a-scoping-review-pci/)*
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+ - [**Fucheng Warren Zhu**](https://www.warrenzhu.com/) presents: [*Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using EO and Computer Vision*](https://connorjerzak.com/transcript-encoding-multi-level-dynamics-in-effect-heterogeneity-estimation/) (@CLeaR)
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  ## Repositories
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