The deadline to apply for a funded PhD or MPhil with October 2021 start has now passed. If you would like to work with me in the future (from 2022 on), please reach out in an email - the best time to do this is from around September the year before so there's plenty of time before the application deadline to talk about interesting research proposal topics. Here is a guide I wrote for prospective students. Applications. I currently don't have a good way to host interns and visiting students in Cambridge, though if you're interested in practical work on algorithmic transparency, I may take interns at Twitter.
I'm Ferenc Huszár, Senior Lecturer in Machine Learning at the University of Cambridge. I recently joined the Department of Computer Science and Technology, a nice and cozy department, where we're building a new machine learning group with Neil Lawrence and Carl Henrik Ek and others. I'm interested in principled deep learning techniques: optimization, generalization, representation, transfer, meta-learning, and so on.
I did my PhD in Bayesian machine learning with Carl Rasmussen, Máté Lengyel and Zoubin Ghahramani over at the Engineering Department in Cambridge. I worked on topics of approximate inference [1, 2], active learning [3, 4], and applications of these to sciences [5, 6].
Following my PhD I worked in various jobs in the London tech/startup sector. My highlight as a researcher is joining Magic Pony Technology, a startup where we developed deep learning-based image superresolution [7, 8] and compression  techniques. After Twitter's acquisition Magic Pony, I have worked on a range of ML topics, like recommender systems [10, 11] and fair machine learning.
I started this blog some time ago but now it kind of has its own life. inFERENCe got started when, in 2015, I returned to machine learning research after a 3-year stint as a data scientist. I basically slept through the deep learning revolution. In those three years, many things happened, so I had to play catch up.
Initially, these blog posts helped me understand the body of literature I have missed: generative adversarial networks, variational autoencoders, representation learning, etc. Nowadays, I continue reading and writing about current papers, trying to reinterpret them and find connections to things I know, usually ending up with some kind of KL divergence.
If you're new here and want to get a taste, you may want to start with these crowd favourites:
- introduction to causal inference and do-calculus and follow-up posts.
- dilated convolutions and Kronecker products
- Gaussian Distributions as Soap Bubbles
- Everything that works works because it's Bayesian
- GANs are broken in more than one way
You can send me an email to email@example.com, follow me on twitter @fhuszar, or look at my Google scholar profile.