Category: Events

The Cambridge ELLIS Unit Seminar Series holds talks by leading researchers in the area of machine learning and AI. Our first  speaker of 2023 will be Dr. Manuel Gomez Rodriguez. Details of his talk can be found below. Title: “Improving Decision Making with Machine Learning, Provably” Abstract: Automated decision support systems promise to help human experts […]

Read more

We are proud to support this year’s NeurIPS@Cambridge meet-up event on 8 December 2022. This is an offline and Cambridge-local meetup of the Neural Information Processing Systems (NeurIPS) conference. Our goal is to bring students, researchers, and engineers from the greater Cambridge area (UK) together for an opportunity to meet and discuss machine learning research […]

Read more

The Cambridge ELLIS Unit Seminar Series holds talks by leading researchers in the area of machine learning and AI. Our next speaker will be Dr. Maria Perez-Ortiz. Details of her talk can be found below. Title: “Towards Planet-centered Artificial Intelligence” Abstract: Could technological advances impact our chances of ensuring a sustainable future? The intersection of technology-sustainability […]

Read more

The Cambridge ELLIS Machine Learning Summer School is a distinguished course offered to graduate students, researchers and professionals, featuring engaging experts in their respective field and/or world-recognized professionals speaking about advanced machine learning concepts. More details about the summer school and how to apply can be found here. The application deadline is on May 22, 2022.

Read more

The Cambridge ELLIS Unit has started a Seminar Series that will include talks by leading researchers in the area of machine learning and AI. Our first speaker of the year will be Dr. Cheng Zhang. Details of her talk can be found below.  Title: “Deep End-to-end causal inference” Abstract: Causal inference is essential for data-driven decision-making across domains such as business engagement, medical treatment or policy-making.  […]

Read more