In this episode, I’m talking with Emre Kiciman, the senior principal research manager leading the AI for Industry research team at Microsoft Research Redmond. After completing a PhD in systems and networking in 2005, Emre began his career with Microsoft Research in the same area, studying reliability in large-scale internet services. Exposure to social data inspired him to refocus his research pursuits: his recent work in causal analysis—including DoWhy, a Python library for causal inference—is helping to connect the whats […]

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. NEW RESEARCH Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn maximal ancestral  

LI LYNA ZHANG: Thank you for having me. HUIZINGA: So let’s start with a brief overview of your paper. Tell us about the issue your research addresses and why it matters. ZHANG: OK, so this paper is about how to effectively extend the context window of large language models beyond 2 million tokens. Why this is important? Because enabling longer input contexts can improve LLM capabilities. Right now, some LLMs can only handle a limited context window of 4K tokens, […]