Research internship (summer 2019)
I am looking for a strong PhD student to work with me, as an intern during summer 2019.
I'm happy to discuss with you this opportunity, if you have strong research experience and/or have published papers on one of the following topics:
- Generative model design (e.g. latent variable models, flow-based/auto-regressive models, energy-based models)
- Representation learning (for e.g. disentanglement, transfer/continual learning, few-shot learning)
- Inference/training algorithms (e.g. VI, MCMC, adversarial methods, integral probability metrics)
If you are interested, send me an email at Firstname.Lastname [at] company [dot] com. Please include your CV and a short paragraph stating your research interest/experience.
I am very happy to collaborate with academia people (as long as it doesn't touch company data, and it doesn't result in IP conflicts).
I have worked on, and continue to work on, the following topics:
- Approximate inference, e.g. variational inference, message passing, SG-MCMC, function-space inference;
- Applications of uncertainty quantification, e.g. continual learning, adversarial attack & defence;
- Generative modelling, e.g. VAE/GAN/flow-based models/energy-based models/implicit models;
- Others, e.g. meta-learning, transfer learning, continual/life-long learning, disentangled representations, gradient estimation.
I am open to collaborate with people that have very different background. E.g. one of my on-going collaboration is on Bayesian methods applied to NLP tasks (and I'm still learning there).
If you are interested, send me an email at liyzhen2 [at] gmail [dot] com.