Congratulations to UT Computer Science Ph.D. student Yeonju Ro on being named a 2026 MLCommons ML and Systems Rising Star! Co-advised by Aditya Akella and @atlaswang.bsky.social, Yeonju is also a contributor to UT’s @ldosexpedition.bsky.social and Infra AI Center.

One-day workshop at Chalmers on 5 June: Principled Statistics in the Age of AI (PSAI26). Speakers: Xiao-Li Meng (keynote, Harvard), Joshua Bon, Cory McCartan, Fredrik Johansson, Ashkan Panahi. Free, registration by 29 May → jameshbailie.github.io/PSAI26/

Postdoc opening at Chalmers / AI and Global Development Lab: Data Science & Statistics. Estimating poverty from satellite imagery of African communities; causal inference and prediction-powered inference for policy evaluation. Deadline 27 May 2026. www.chalmers.se/en/about-cha...

PhD opening at Chalmers / AI and Global Development Lab: Earth Observation, Data Science, and AI for poverty estimation. Deep learning on Sentinel-2 imagery, benchmarked against Pléiades and Landsat, with AI-explainability. Deadline 13 June 2026. www.chalmers.se/en/about-cha...

Omatharv Bharat Vaidya, Connor T. Jerzak, Nhat Ho, Chandrajit Bajaj: Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms https://arxiv.org/abs/2605.08423 https://arxiv.org/pdf/2605.08423 https://arxiv.org/html/2605.08423

We’re looking for a managing director for the AI.UP center at the University of Potsdam. AI.UP develops and consults on AI across disciplines and supports interdisciplinary collaboration and grant initiatives. *Permanent position* Deadline: June 28, 2026 Details: causalinferencelab.com/jobs

Samuel G. Balter, Ethan Jerzak, Connor T. Jerzak: Multiplication in Multimodal LLMs: Computation with Text, Image, and Audio Inputs https://arxiv.org/abs/2604.18203 https://arxiv.org/pdf/2604.18203 https://arxiv.org/html/2604.18203

Thanks, Ingo! Graham Goff and I have put a lot of work into this package. {DAGassist} can estimate a wide range of models and classify covariate roles after you make your DAG either with {ggdag} or {daggitty}. We hope that the package can make DAG-consistent estimation a little easier for everyone!

UT Austin PolMeth Speaker Series: Grad Student Lightning Talks this week! ⚡️ 🗓️ Thurs, Apr 16 @ 12:30pm 📍 Batt 5.108 (Lunch provided!) 🗣️ Rachel Jeon: Activism & Treaty Design 🗣️ Sophia Lander: Tech Innovation in PPPs 🗣️ Allison Verrilli: Policing Federalism & Local Safety

We're hiring a Research Support Associate at MIT Political Science. Work with me, In Song Kim, Rich Nielsen, and Naoki Egami on methods and political economy. Great opportunity for those considering a PhD in the social sciences. One-year position with possible extension.

Excited to welcome Gengchen Mai to the UT Austin Methods Speaker Series this Thursday, April 2! Talk: “Spatial Representation Learning: What, How, and Why” on latest GeoAI & scientific applications. 🕛 12:00-1:30pm 📍 BAT 5.108 (5th floor Batts Hall), UT Austin campus Join us!

New grad course at UT Austin this coming Fall (2026); look for this in the catalog -- "Gov 385L: Causal ML & EO for Social Sci"

Cool workshop upcoming this May -- join if you can. "We take rationality seriously, but should we? Why is a rational agent better in some respect than an irrational one? Is rationality just instrumentally useful, or does it have intrinsic value?" ejerzak.github.io

Join us on Thursday, 29 January, at *13:30 CET for the International Roundtable on Computational Social Science with Bedoor AlShebli (NYU Abu Dhabi) 🔹 The Science of Science... and its Human Side 🔹 More info: liu.se/en/event/int...

FastRerandomize unites batched candidate generation, key-only storage & design-respecting inference, while using the SoTA in accelerated computing (T/GPU). That design delivers order‑of‑mag speedups & enables tighter covariate balance in high-dim experiments⇢more precise causal estimates, lower cost

The remains of Gresham castle photographed with a regular camera and how it looks with a LiDar drone camera.

Calling those looking to get feedback on their quant work📢: Join us at @TexasAM for TexMeth2026 this Feb. Great opportunity to connect with the regional community and showcase your research. The sun will still be strong for those coming from the north!🤝📊☀️ Apply: docs.google.com/forms/d/e/1F...

Application deadline for UT Austin Gov - PhD admissions is 12/10 this cycle. More information below. I am hearing that the Department will not be shrinking its cohort this admission cycle!

The 8th European Conference on Social Networks (EUSN 2026) will take place on 11-15 August 2026 in Norrköping, Sweden, hosted by @iasliu.bsky.social ⌛ Deadline for workshop and session proposals: 1 December 2025 More information: liu.se/en/event/eus...

Looking forward to giving a presentation on the planetary causal inference paradigm at Department of Methodology, LSE, today. Join us if you are around. www.lse.ac.uk/methodology/...

Sign up for this week's Seminar Series with @adeldaoud.bsky.social 💡 21 Nov, 2 - 3pm 🌎Planetary Causal Inference: combining computer vision and earth observation to analyse disparities in health and living conditions among neighbourhoods in Africa, 1990 to date www.eventbrite.com/e/department...

If you are in the vicinity of Oxford University, join me today at 4 pm for my talk at Nuffield College. Title: "Planetary Causal Inference: combining computer vision and earth observation to..." Link to event details: [Sociology Seminar at Nuffield College](www.nuffield.ox.ac.uk/news-events/...)

Markus B. Pettersson, Adel Daoud: Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping https://arxiv.org/abs/2511.01408 https://arxiv.org/pdf/2511.01408 https://arxiv.org/html/2511.01408

"Counterfactuals. At planetary scale." The Planetary Causal Inference (PCI) book launches soon: planetarycausalinference.org/book-launch/

Excited to see our PhD student Andrés Cruz (@arcruz0) presenting his work "Sensitivity Bounds for Uncertainty Propagation" at LAPolMeth IX in Viña del Mar, Chile. Catch it during the poster session on Nov 22 (12:45-14:00, Building C). Don't miss this work on uncertainty in quant social science!

Debiasing ML predictions for causal inference—no new labels needed. We propose Tweedie’s correction to fix shrinkage enabling “one map, many trials.” arxiv.org/abs/2508.01341 #CausalInference #MachineLearning #EarthObservation #PovertyMapping

🥳 Join us in celebrating @alongcamejones.bsky.social and his legacy with the Policy Agendas Project. Excited to see what @seanmtheriault.bsky.social and Derek Epp do next! 🤟

ML wealth maps from space 🛰️ are great, but suffer from "shrinkage" bias, which waters down policy impact results (causal inference). We developed correction methods that fix this bias *without* new data. arxiv.org/abs/2508.01341 #CausalInference #DataForGood #AI #PovertyMapping #EarthObservation

Are you interested in using earth observation data and deep learning for estimating poverty in Africa, poverty specifically? Then apply to this research engineer position at AI and Global Development Lab. More info about our research at www.aidevlab.org . Deadline is 7th Nov. lnkd.in/ePX7VT-K

New undergrad course (UT Austin, this spring): Gov 355M - AI & Programming Foundations for Computational Social Science. We'll treat curiosity as our raw material and AI+computation as our foundry - learning how the frontiers of technology can accelerate discovery in the social sciences + beyond.

A little less than 2 weeks to submit an abstract proposal for PolMeth - Europe. Join us in Dublin this coming May to share your new political science research using cutting edge methods!!

Check out the data paper (open access) and the publicly available data: We hope that many people will use it in their research and for other purposes.

When on-the-ground data are scarce or outdated, satellites can keep the evidence flowing. Our guide shows how to turn data from satellites into useful causal claims, improving targeting + evaluation, avoiding common issues related to resolution mismatch / leakage / spatial dependence.

Connor T. Jerzak, Adel Daoud Remote Auditing: Design-based Tests of Randomization, Selection, and Missingness with Broadly Accessible Satellite Imagery https://arxiv.org/abs/2510.00128

Adel Daoud, Cindy Conlin, Connor T. Jerzak: Chinese vs. World Bank Development Projects: Insights from Earth Observation and Computer Vision on Wealth Gains in Africa, 2002-2013 https://arxiv.org/abs/2509.25648 https://arxiv.org/pdf/2509.25648 https://arxiv.org/html/2509.25648

LSE's Department of Methodogy is searching for an Assistant Professor in Computational Social Science. Applicants from across the social sciences are very welcome! Please reach out if you would like any more details.

Fall schedule for the Political Methodology Seminar Series at the UT Austin Department of Government, AY25-26 is out! Event calendar links here: connorjerzak.com/methods-seri...

With a new Multi-Scale Representation Concatenation method, researchers enhance satellite imagery optimization for causal inference in anti-poverty programs by revealing treatment effect heterogeneity across scales, potentially amplifying impacts without extra costs. https://arxiv.org/abs/2411.02134

Attention #polisky! Apply for @utaustin.bsky.social LBJ School of Public Affairs with a focus on American Political Institutions. I'm on the committee, so please reach out with questions. See below: apply.interfolio.com/170943

Markus Pettersson, Connor T. Jerzak, Adel Daoud: Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven P... https://arxiv.org/abs/2508.01341 https://arxiv.org/pdf/2508.01341 https://arxiv.org/html/2508.01341

Markus Pettersson, Connor T. Jerzak, Adel Daoud Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis https://arxiv.org/abs/2508.01341

Satiyabooshan Murugaboopathy, Connor T. Jerzak, Adel Daoud: Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty? https://arxiv.org/abs/2508.01109 https://arxiv.org/pdf/2508.01109 https://arxiv.org/html/2508.01109

Excited to teach two grad courses @UTAustin Spring '26! 📊 Gov 385La: Making Big Data – Learn web scraping, crowd-sourcing, & data curation to build high-quality datasets for social sci impact. 🌍 Gov 385Lb: Causal ML & EO for Social Sci – Deploy satellite data & ML for causal inf at planetary scale

Connor T. Jerzak, Stephen A. Jessee: Attenuation Bias with Latent Predictors https://arxiv.org/abs/2507.22218 https://arxiv.org/pdf/2507.22218 https://arxiv.org/html/2507.22218

Join a cool group at UC Berkeley, March 7-8, 2026, for "Clouds, Streams, and Ground (Truths)"—exploring methods to study algorithmic music ecosystems. Details: www.algorithmicmusicmethods.com

A First Course in Planetary Causal Inference: Confounding. Adel presents. www.youtube.com/watch?v=oD7D...

Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis and Machine Learning https://arxiv.org/abs/2504.19043

🚨 New preprint! Excited to share our work on extracting and evaluating the potentially many feature descriptions of language models 👉 arxiv.org/abs/2506.15538