Jonathan Huggins aka jonathan huggins, jonathan h huggins, jhuggins. Chan School of Public Health. Coauthored with Ryan Adams and Tamara Broderick, has been accepted with a spotlight talk to the Conference on Neural Information Processing Systems. We have updated our preprint. Quantifying the Accuracy of Approximate Diffusions and Markov Chains. James Zou and I have updated our preprint.

We seek to understand how populations of neurons collectively process sensory input, perform computations and control behaviour. To this end, we develop statistical models and machine learning algorithms for large-scale analysis of neural data, and collaborate with experimental laboratories performing measurements of neural activity and behaviour. Are choice probabilities explained by serial dependencies? Paper accepted to Journal of Neuroscience. Three papers accepted at NIPS.

Postdoctoral Fellow, Bayesian Statistics, Aerosol Science. ALP wants to teach kids how to program, and I agree. This entry was posted in Blog. Marrying differential equations and regression.

Women in Probability is an organization for women active in probability research in North America. Our primary purpose is to provide networking and mentoring opportunities for early career women. Our activities are funded by the US National Science Foundation. Women in Probability organizes various activities to help develop interpersonal contacts which will facilitate early career transitions and ultimately improve the retention of talented women in our field.

Produces innovative solutions for key challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies. Fulfilling the promise of the big data revolution, we invent deep analytical tools to extract knowledge from complex data and deliver BIG INSIGHT. We wish you a nice summer! Each Innova.

NIPS 2016 Workshop; December 9, 2016. Room 112, Centre Convencions Internacional Barcelona, Barcelona, Spain.

BAIR includes over two dozen faculty and more than a hundred graduate students pursuing research on fundamental advances in the above areas as well as cross-cutting themes including multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities.