Updates from I2SC

Large Language Models (LLMs) are rapidly becoming part of research practices, and qualitative analysis is no exception. They enable researchers to broaden the scope of their work, analyze large volumes of textual and visual data, and save time on repetitive tasks. The question is no longer whether these advanced tools will be used, but how they can be integrated transparently and ethically into research workflows. Qualitative analysis is a complex practice that relies heavily on human interpretation, reflexivity, and judgment. From initial coding to theme development and the writing of analytical claims supported by evidence, the human element remains essential. Models for using LLMs in qualitative analysis vary widely. These range (1) from integrations with traditional analysis applications, which can provide a secure working environment but are limited by predefined features, to (2) dedicated qualitative AI applications, which offer speed but can be costly and may rely too heavily...

Nobody wakes up thinking, “Today I will be irrational.” And yet, most days work out that way. Ada has a presentation at 10 a.m. She's been rehearsing it for three days — in the shower, on the commute, at 2 a.m. while pretending to sleep. She knows it's cold. She could probably give it half-asleep. Ada is a lawyer at Simpson & Simpson. Her boss thinks she is exceptional. He says it in performance reviews, in passing, in the way he hands her out the interesting problems, and Ada always delivers. In the last year, she has been promoted twice. Slowly, almost invisibly, Ada begins behaving like the capable person her boss already believes she is. Although anxious about meeting her boss's expectations for the presentation, she is still ready to give it to her all. Psychologists call this the Pygmalion Effect—the quiet way other people's expectations shape how we perform. Ada doesn't know any of this. Unable to go back to sleep, she wakes up two hours before her a...

Image generated by ChatGPT This week we discussed reference letters for PhD applications and internships, focusing on how to approach potential letter writers and what materials to share. One key takeaway was that, as you progress in your career, it is normal and often helpful to share drafts or bullet points with your referees. The goal is to make the process easy for them and enable them to write a strong, informed letter. When to ask: Request letters well before deadlines so referees have time to reflect and write thoughtfully. Give them an easy way to decline if they cannot provide a strong letter. Ask when your work is still fresh in their mind and before busy academic periods. Avoid last-minute requests or assuming someone will say yes. Early, respectful requests increase the likelihood of a detailed and supportive letter. What to share: Do not assume they remember details or understand the role. Provide a complete packet—your CV, cover or research statements, job descriptio...

There's a particular kind of tragedy that doesn't come from bad luck or outside forces—it comes from the inside, from the very act of trying to escape. It's the tragedy of someone who sees the trap, runs from it, and finds themselves caught anyway. Or worse: someone who never even knew the trap existed, and whose every instinct to survive becomes the mechanism of their own undoing. This is the paradox of defiance. And once you see it, you may start to see it everywhere. Two African Stories, One Uncomfortable Truth Take Ola Rotimi’s The Gods Are Not to Blame , a Yoruba retelling of the Greek myth of Oedipus. A man named Odewale is fated at birth to kill his father and marry his mother. In the Yoruba context, this isn't just a "bad prediction"; it is a decree from the ancestors, a cosmic blueprint. His parents, terrified, try to have him killed as an infant. He survives, is raised elsewhere, and eventually, through pride, anger, and a series of tragic misun...

Artificial intelligence is increasingly framed as a climate solution. From optimizing power grids to improving climate models, AI is positioned as an accelerator of decarbonization.But emerging research suggests a more complex reality. Recent analysis in Nature Climate Action argues that AI’s climate impact cannot be evaluated through isolated use cases. Instead, it must be assessed across lifecycle energy demand, infrastructure externalities, governance systems, and information dynamics (Nature Climate Action, 2025).The central question is no longer “ Can AI help climate action?” It is: Under what conditions does AI produce a net climate benefit? Where AI Demonstrably Supports Climate Goals Climate Modeling and Forecasting Machine learning based surrogate models are increasingly augmenting physics-based simulations. Systems developed by Google DeepMind demonstrate that neural architectures can approximate atmospheric dynamics at lower computational cost than traditional numer...

Date : February 6, 2026 Abstract : Reliable subnational population estimates are essential for effective governance, service provision, and humanitarian response, yet many low- and middle-income countries lack recent or comprehensive census data. This talk explores emerging strategies for estimating population counts when traditional sources are unavailable. I will focus on the integration of diverse and incomplete data sources—including administrative records (e.g., health, education), satellite imagery, and geospatial covariates—combined through Bayesian modeling frameworks to generate high-resolution estimates. In the second part of the talk, I will extend these methods to the problem of nowcasting population distributions in crisis contexts, including forced displacement and conflict. Here, I will explore the utility of real-time or near real-time data streams—such as mobile phone metadata, satellite-derived indicators, and social media signals—for capturing rapid demographic shift...