Welcome to my personal homepage
I am a Senior scientist at Amazon, based in Cambridge, UK.
Before joining Amazon I co-founded Inferentia together with Neil Lawrence, Zhenwen Dai and Javier Gonzalez. Our start-up was eventually acqui-hired by Amazon. I also worked as a research associate in machine learning and social robotics at Sheffield, and before that I pursued my PhD degree with topic Deep Gaussian Processes and Variational Propagation of Uncertainty.
My research areas of interest are: representation learning, transfer learning, uncertainty quantification. I am working with machine learning models from the Bayesian probabilistic or the deep learning domain, but I also investigate methods combining these areas.
I have worked in a variety of application areas, such as: multi-modal information fusion, decision making, information retrieval, AI-assisted data science.
Contact
Email: lastname {at} amazon {dot} com
Twitter: @adamianou
Google Scholar
News
- Jul. 2020: Excited to teach Deep Learning at Data Science Africa 2020!
- 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.