Welcome to my personal homepage
I am a senior scientist at Spotify based in UK, working in machine learning and information retrieval.
Before joining Spotify I was at Amazon, working on products in core ML, supply chain optimization and Alexa shopping. Prior to that I had 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, working with prof. Neil Lawrence.
My research areas of interest are: transfer learning, deep representation learning (in particular graph neural networks) and uncertainty quantification. Lately I'm focusing a lot on information retrieval applications, such as recommender system tasks. In the past I worked a lot with Bayesian probabilistic models and their extensions to the deep learning paradigm, as well as applications to decision making.
Contact
Email: andreasd [a] spotify [dot] com
Twitter: @adamianou
Google Scholar
News
- Sep. 2022: At Stanford University to talk about GNNs at the Stanford Graph learning workshop..
- April 2022: At the Univ. of Sapienza Rome, I'm offering a 2-day course on "the role of Data in Industrial ML Applications"..
- May 2021: Our extensive paper on Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis is now on JMLR.
- Feb. 2021: Looking forward to the Cambridge Science accelerator winter school, where I'll be speaking about the role of uncertainty in machine learning
- Jan. 2021: Two new papers: "Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data" (ICLR) and "Fast Adaptation with Linearized Neural Networks" (AISTATS)!
Particular thanks to first authors W. Maddox and F. Tonolini.