Professor Björn Schuller

Björn Schuller is:
- Full Professor & Head of the Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany,
- Professor of Artificial Intelligence & Head of GLAM - Group on Language, Audio & Music, Imperial College London, London/U.K,
- Chief Scientific Officer (CSO) and Co-Founding CEO, audEERING GmbH, Gilching/Germany,
- Visiting Professor, School of Computer Science and Technology, Harbin Institute of Technology, Harbin/P.R. China.

Lecture on Deep Learning for Signal Processing in Health and Wellbeing

Professor Ludovic Denoyer

Ludovic Denoyer is a Research Scientist at FAIR, mainly focusing on various machine learning problems, particularly on reinforcement learning and human-machine interaction. He previously was Full Professor in the Machine Learning and Information Access Team at Sorbonne University (machine/deep/reinforcement learning), Staff Research Scientist at Criteo and Co-head of the Data-Science Master - Data, Machine Learning and Knowledge (DAC). 

Lecture on Budgeted Learning and Adverse Model Disentanglement

Professor Li Erran Li

Li Erran Li is Head Of Science - HIL team at Amazon Web Services (AWS) and also an Adjunct Professor with the Computer Science Department, Columbia University.. He was a Researcher with Bell Labs and Uber. His research interests are in machine learning algorithms, artificial intelligence, and systems and wireless networking. He is an ACM Distinguished Scientist. He was an Associate Editor of the IEEE Transactions on Networking and the IEEE Transactions on Mobile Computing. He co-founded several workshops in the areas of machine learning for intelligent transportation systems, big data, software defined networking, cellular networks, mobile computing, and security.

Lecture on Deep Learning for Autonomous Vehicles and Deep Reinforcement Learning

Professor Vittorio Ferrari

Vittorio Ferrari is a Senior Staff Research Scientist at Google and Honorary Professor at the University of Edinburgh.
His research field is computer vision. He currently works on two primary areas: learning visual models with little human supervision, and 3D Deep Learning. In the past he has worked on several other computer vision problems, including action recognition, human pose estimation, learning visual attributes, shape matching, contour-based object class detection, specific object recognition, multi-view wide-baseline stereo, tracking in video.

Lecture on Training Object Localisation Models in Weakly Supervised Settings