I am a Ph.D. candidate with the Computational and Biological Learning group at the University of Cambridge, supervised by Dr José Miguel Hernández-Lobato and advised by Dr Richard Turner. My research focuses on developing probabilistic models (typically parameterized by deep neural networks) and associated scalable approximate inference procedures.
I am particularly interested in developing deep probabilistic models that capture structure existing in specific data modalities, and exhibit nice behaviours such as sample and parameter efficiency, generalization, and calibrated uncertainties. Much of my recent work has focused on meta-learning, which enables fast few-shot generalization. Another theme in my research is modelling symmetries in the data, such as by incorporating translation or permutation equivariance.
PhD in Probabilistic Machine Learning, Present
University of Cambridge
MPhil in Machine Learning, 2017
University of Cambridge
MSc. in Applied Statistics and Engineering, 2016
Ben-Gurion University of the Negev
BSc. in Engineering, 2014
Ben-Gurion University of the Negev
We extend ConvCNPs to Construct and meta-learn translation equivariant maps from the space of data sets to predictive stochastic processes.
We extend deep sets to functional embeddings and Neural Processes to include translation equivariant members (Oral Presentaiton).
Powerful meta-learning system based on the neural process framework (Spotlight)
We introduce ML-PIP, a general probabilistic framework for meta-learning