Title:Machine learning with problematic datasets in diverse applications
Date: August 13, 2019 (Tuesday)
Time: 11:00 am - 12:00 pm
Venue: Room 121, 1/F, Ho Sin-Hang Engineering Building, The Chinese University of Hong Kong, Shatin, N.T.
Speaker: Prof. Chris Willcocks
Durham University


Machine learning scientists often ask the question "What was the distribution from which the dataset was generated from?" and subsequently "How do we learn to transform observations from what we are given, to what is required by the task?". This seminar highlights successful research where our group took explicit steps to deal with problematic datasets in several different applications, from building robust medical diagnosis systems with a very limited amount of poorly labeled data, to how we hid secret messages in plain sight in tweets without changing the underlying message, how we captured plausible interpolations and successful dockings of proteins despite significant dataset bias, through to recent advances in meta learning to tackle the evolving task distribution in the ongoing anti-counterfeiting arms race.

Speaker’s Bio: 

Chris G. Willcocks is a recently appointed Assistant Professor in the Innovative Computing Group at the Department of Computer Science at Durham University in the UK, where he currently teaches the year 3 Machine Learning and year 2 Cyber Security sub-modules. Before 2016, he worked on industrial machine learning projects for P&G, Dyson, Unilever, and the British Government in the areas of Computational Biology, Security, Anti-Counterfeiting and Medical Image Computing. In 2016, he founded the Durham University research spinout company Intogral Limited, where he successfully led research and development commercialisation through to Series A investment, deploying ML models used by large multinationals in diverse markets in Medicine, Pharmaceutics, and Security. Since returning to academia, he has recently published in top journals in Pattern Analysis, Medical Imaging, and Information Security, where his theoretical interests are in Variational Bayesian methods, Riemannian Geometry, Level-set methods, and Meta Learning.

Enquiries: Ms. Shirley Lau at tel. 3943 8439

For more information, please refer to http://www.cse.cuhk.edu.hk/en/events