The Oxford Climate Society is currently recruiting for a new committee to lead the society from Trinity 2024 to Hilary 2025 (inclusive).
We are an educational society aimed at empowering students of all subjects to learn about climate change, and ultimately take action at university and in their careers beyond. We welcome both undergraduate and postgraduate students of all levels of experience.
At Oxford University Economics Summer Schools (OUESS), we understand the importance of expanding the academic horizons for you, especially when certain areas may not be covered in the current coursework. We are excited to extend an exclusive invitation to individuals who may not have access to this material within their own courses.
XTX Markets, a leading algorithmic trading company, is excited to announce the launch of XTY Labs, a new machine learning division to be headed by newly appointed Research Director, Dr. Atlas Wang.
XTY Labs is set to become a hub for elite machine learning researchers and will be home to the new ‘XTY Labs AI Residency Program’, offering researchers short-term AI residency contracts ranging from 6-12 months.
Have periods affected your study? Do you teach students who have periods?
The PERIODically podcast is a series of frank conversations among undergraduate and postgraduate chemistry students at Oxford about their experience of how having a period has affected their studies.
Although it focusses on the Chemistry degree in particular, much of what’s discussed is relevant to teaching and studying other subjects including Maths. Recommended listening.
Please join us for refreshments outside L3 from 1530.
Predicting real-world phenomena often requires an understanding of their causal relations, not just their statistical associations. I will begin this talk with a brief introduction to the field of causal inference in the classical case of structural causal models over directed acyclic graphs, and causal discovery for static variables. Introducing the temporal dimension results in several interesting complications which are not well handled by the classical framework. The main component of a constraint-based causal discovery procedure is a statistical hypothesis test of conditional independence (CI). We develop such a test for stochastic processes, by leveraging recent advances in signature kernels. Then, we develop constraint-based causal discovery algorithms for acyclic stochastic dynamical systems (allowing for loops) that leverage temporal information to recover the entire directed graph. Assuming faithfulness and a CI oracle, our algorithm is sound and complete. We demonstrate strictly superior performance of our proposed CI test compared to existing approaches on path-space when tested on synthetic data generated from SDEs, and discuss preliminary applications to finance. This talk is based on joint work with Georg Manten, Cecilia Casolo, Søren Wengel Mogensen, Cristopher Salvi and Niki Kilbertus: https://arxiv.org/abs/2402.18477 .
