Welcome to my personal homepage
I am a Senior machine learning scientist at Amazon, based in Cambridge, UK.
Prior to that, I worked as a research associate in machine learning and robotics at Sheffield, and before that I pursued my PhD degree with topic Deep Gaussian Processes and Variational Propagation of Uncertainty.
My area of interest is machine learning, and more specifically: Bayesian probabilistic modeling, transfer/meta-learning, uncertainty quantification. I am applying these techniques to areas such as representation learning, decision making and automation of machine learning pipelines.
Contact
Email: lastname {at} amazon {dot} com
Twitter: @adamianou
Google Scholar
News
- Jan. 2020: Our paper on Empirical Bayes Transductive Meta-Learning with Synthetic Gradients was accepted at ICLR!
- Nov. 2019: New pre-print on Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems with amazing colleagues from Amazon!
- Oct. 2019: Invited talk at Univ. Manchester's Advances in Data Science Seminar Series.
- Oct. 2019: Invited talk at the workshop on Uncertainty Propagation in Composite Models, Munich.
- Sep. 2019: Two papers accepted at NeurIPS workshops: "Empirical Bayes Meta-Learning with Synthetic Gradients" and "On Transfer Learning via Linearized Neural Networks". With my interns Shell Xu Hu and Wesley Maddox.
- Aug. 2019: Invited talk at the Alan Turing Institute on Deep and Multi-fidelity learning with Gaussian processes.
- Mar. 2019: Our paper "Variational Information Distillation for Knowledge Transfer" was accepted at CVPR.