Plenary and Keynote Speakers
7th International Conference on Machine Vision and Machine Learning (MVML’21)
Due to COVID'19 pandemic it will be done VIRTUALLY
We are pleased to announce the following Plenary and Keynote Speakers for 7th International Conference on Machine Vision and Machine Learning (MVML'21):
Dr. Ruslan Salakhutdinov
Carnegie Mellon University, USA
Russ Salakhutdinov is a UPMC Professor of Computer Science in the Department of Machine Learning at CMU. He received his PhD in computer science from the University of Toronto. After spending two post-doctoral years at MIT, he joined the University of Toronto and later moved to CMU. Russ’s primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research, served as a program co-chair for ICML2019, served on the senior programme committee of several top-tier learning conferences including NeurIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Google Faculty Award, and Nvidia’s Pioneers of AI award.
Topic of Keynote: From Differentiable Reasoning to Self-supervised Embodied Active Learning
Dr. Chris Williams
University of Edinburgh, UK
Chris Williams is Professor of Machine Learning and Director of Research in the School of Informatics, University of Edinburgh. His main areas of research are in visual object recognition and image understanding, models for understanding time-series, AI for data analytics, unsupervised learning, and Gaussian processes. He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was elected a Fellow of the Royal Society of Edinburgh in 2021, is a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), a Turing Fellow at the Alan Turing Institute (UK), and was program co-chair of the NeurIPS conference in 2009.
Topic of Keynote: Towards Automating the Data Analytics Process
Dr. Max Welling
University of Amsterdam, Netherlands
Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam (AMLAB) and a VP Technologies at Qualcomm. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and at the European Lab for Learning and Intelligent Systems (ELLIS). Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015 and is on the board of the NeurIPS foundation since 2015 and has been program chair and general chair of NeurIPS in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. He is recipient of the ECCV Koenderink Prize in 2010. Welling is co-founder and board member of the Innovation Center for AI (ICAI) and the European Lab for Learning and Intelligent Systems (ELLIS). He directs the Amsterdam Machine Learning Lab (AMLAB), and co-directs the Qualcomm-UvA deep learning lab (QUVA), the Bosch-UvA Deep Learning lab (DELTA) and the Amsterdam ELLIS Unit.
Topic of Keynote: How Can Machine Learning Help Computer Vision in the Next Decade?
Dr. Sepp Hochreiter
Johannes Kepler University, Austria
Sepp Hochreiter is heading the Institute for Machine Learning, the LIT AI Lab and the AUDI.JKU deep learning center at the Johannes Kepler University of Linz and is director of the Institute of Advanced Research in Artificial Intelligence (IARAI). He is regarded as a pioneer of Deep Learning as he discovered the fundamental deep learning problem: deep neural networks are hard to train, because they suffer from the now famous problem of vanishing or exploding gradients. He is best known for inventing the long short-term memory (LSTM) in his diploma thesis 1991 which was later published in 1997. LSTMs have emerged into the best-performing techniques in speech and language processing and are used in Google’s Android, in Apple’s iOS, Google’s translate, Amazon’s Alexa, and Facebook’s translation. Currently, Sepp Hochreiter is advancing the theoretical foundation of Deep Learning, investigates new algorithms for deep learning, and reinforcement learning. His current research projects include Deep Learning for climate change, smart cities, drug design, for text and language analysis, for vision, and in particular for autonomous driving.
Topic of Keynote: Modern Hopfield Networks
Dr. Dana Ballard
University of Texas at Austin, USA
Dana Ballard received his PhD from the University of California, Irvine, in 1974. His main research interest is in computational theories of the brain with emphasis on human vision and motor control. Currently Ballard is interested in pursuing this research by using high DOF models of humans’ natural behavior in virtual reality environments. View Profile
Topic of Keynote: The Use of Gaze in Human and Machine Vision