We are pleased to announce the Cambridge-Tübingen PhD fellowships between the University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen.
Seven new papers from the group are to appear at the 2015 conference on Advances in Neural Information Processing Systems (NIPS 2015), to be held in December in Montreal, Canada.
The list of papers are:
- Statistical Model Criticism using Kernel Two Sample Tests
James Lloyd and Zoubin Ghahramani
- MCMC for Variationally Sparse Gaussian Processes
James Hensman, Alex Matthews, Maurizio Filippone and Zoubin Ghahramani
- Stochastic Expectation Propagation
Yingzhen Li, Jose Miguel Hernandez-Lobato and Richard Turner
- A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models
Nilesh Tripuraneni, Shane Gu, Hong Ge and Zoubin Ghahramani
- Neural Adaptive Sequential Monte Carlo
Shane Gu, Zoubin Ghahramani and Richard Turner
- Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
Amar Shah and Zoubin Ghahramani
- Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
Felipe Tobar, Thang Bui and Richard Turner
For the most up to date versions of the papers, visit the authors’ webpages, which may be found through our group members page.
Congratulations to Prof. Zoubin Ghahramani, who has been elected as a Fellow of the Royal Society. His nomination reads:
‘Zoubin Ghahramani is a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian nonparametric approaches to machine learning systems, and to the development of approximate variational inference algorithms for scalable learning. He is one of the pioneers of semi-supervised learning methods, active learning algorithms, and sparse Gaussian processes. His development of novel infinite dimensional nonparametric models, such as the infinite latent feature model, has been highly influential.’
See here for the full list of new Fellows.
PhD student Thang D. Bui wins prestigious Google Europe Doctoral Fellowship in Speech Technology. This year 15 fellowships were awarded to new doctoral students across Europe. From the ‘Research at Google’ website:
“In 2009, Google created the PhD Fellowship program to recognize and support outstanding graduate students doing exceptional work in Computer Science and related disciplines. The following year, we launched the program in Europe as the Google European Doctoral Fellowship program. [The recipients of this award] represent the next generation of researchers who will endeavor to solve some of the most interesting challenges in Computer Science. We offer our congratulations, and look forward to their future contributions to the research community with high expectation.”
More information in the link: http://googleresearch.blogspot.co.uk/2015/06/announcing-2015-google-european.html
Congratulations to Richard on winning the Cambridge University Students’ Union Student-Led Teaching Award. This is an award across the entirety of Cambridge with 265 nominations.
The nomination for Richard stated that he is “a brilliant lecturer … able to explain complex ideas in very clear language that students are able to relate to … Perhaps most important, more so than his lecturing style, is his passion for engineering and teaching. Listening to his lectures makes you feel as you yourself are on a voyage of discovery through the field.”
See more at: http://www.eng.cam.ac.uk/news/dr-richard-turner-honoured-teaching-award
Yarin and Mark were awarded a £10,000 prize each by Qualcomm for their innovation proposals titled “Scalable Probabilistic Representation Learning” (Yarin), and “Variational Inference and Kernel Bayes Rule” (Mark). Their award also includes an assignment of a Qualcomm researcher as mentor to facilitate close collaboration and interaction with Qualcomm Corporate Research & Development.
Prof Zoubin Ghahramani speaks on the BBC World Service’s The Forum programme. This 45 minute programme explores the topic of Deep Learning, interviewing Zoubin along with Professor Geoffrey Hinton (Toronto / Google) and Professor Trevor Darrell (Berkeley).
Zoubin also talks to BBC Radio 4 Inside Science about the recent Nature paper by Demis Hassabis and his team at Google DeepMind, in which they teach Deep Reinforcement Learning to play Atari games, and what this implies for AI.
Alex was awarded a £10,000 prize by G-Research for an outstanding PhD with applications to mathematical finance. His thesis, titled “Effective Implementation of Gaussian Process Regression for Machine Learning”, proposed a new framework for the implementation of Gaussian Processes which is currently used by some trading systems.
DEADLINE: 6 March 2015
We are seeking up to three highly creative and motivated researchers to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. Positions are available at the Senior Research Associate (SRA), Research Associate, and Research Assistant levels. All positions will involve research in direct collaboration with Professor Zoubin Ghahramani.
Research Area 1: Building an Automatic Statistician. This postdoc will be working on developing algorithms for the automated analysis and interpretation of statistical models. Candidates should have extensive experience in probabilistic modelling and scalable approximate inference. See: http://www.automaticstatistician.com/
Research Area 2: Probabilistic Programming and Bayesian Nonparametrics. This postdoc will be working with Zoubin Ghahramani in collaboration with MIT and should have research experience in these two areas, ideally to include research on an existing probabilistic programming language and MCMC methods.
Research Area 3: Inference in graphical models. This postdoc will be working on novel approaches for inference in graphical models, including message passing and approximations of partition functions. Experience in these two areas is required.
The successful applicants will have or be near completing a PhD in computer science, information engineering, statistics or a related area, and will have extensive research experience and a strong publication record in machine learning, including ideally papers in top machine learning conferences such as NIPS, UAI, ICML, and AISTATS. SRA candidates must hold a PhD, and have several years of postdoctoral experience and well as some experience supervising projects, staff and students.
All positions are for one year in the first instance, with expectation of renewal subject to good performance and funding.
You must apply though the application websites where further details are also available for the RA positions: http://www.jobs.cam.ac.uk/job/6069/ and for the Senior RA positions: http://www.jobs.cam.ac.uk/job/6070/
If you have any questions about this vacancy or the application process, please contact Miss Diane Hazell, email: firstname.lastname@example.org, Tel: +44 01223 748529.
Please submit your application by midnight on 6 March 2015.
Professor of Information Engineering
University of Cambridge
- March 2017
- November 2016
- October 2016
- August 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- November 2015
- October 2015
- September 2015
- June 2015
- May 2015
- March 2015
- February 2015
- November 2014
- October 2014
- September 2014
- August 2014
- July 2014
- June 2014
- April 2014
- February 2014
- January 2014
- November 2013
- October 2013
- July 2013
- April 2013
- November 2012
- November 2011
- September 2011
- July 2011
- March 2011
- September 2010
- May 2010
- February 2010
- September 2009