About Me

I am a PhD student under Cambridge-Tuebingen PhD Felloship. I am working with Bernhard Schölkopf at the Max Planck Insitute for Intelligent Systems in Tübingen and Ferenc Huszar at University of Cambridge.

My research interest lies in the intersection of causal inference and machine learning. Particularly, I am interested in Causal Representation Learning. Deep neural network methods have proved to be very efficient at learning statistical association within data. However, it is becoming clear that they fail to learn causally valid representations, which is essential for robust inference and reasoning. Therefore I am interested in developing new techniques for training neural networks such that they learn about the invariant causal representation behind the data.

Previously, I was a Msc in Machine Learning student at University College London (UCL) and studied Mathematics (Bsc + MMath) at University of Cambridge. I also used to work as a Quantitative Strategist at Goldman Sachs International.

News

• July 2022: Great to see fellow researchers in first Cambridge ELLIS Machine Learning Summer School.
• July 2022: Happy to be back to statslab Cambridge to give a talk on “Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data” at Causal Reading Group hosted by Qingyuan Zhao.
• June 2022: Excited to attend an invited in-person ELLIS Theory Program Workshop at beautiful Arenzano
• May 2022: Great to share “Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data” at OxCSML Seminar Talk

Publications

Please see my publication list here or visit my Google Scholar.

Contact

Email: syg26@cantab.ac.uk
Twitter: syguoML