Keynote Speakers

Tuomas Virtanen

Laboratory of Signal Processing

Tampere University of Technology


Tuomas Virtanen is Professor at Laboratory of Signal Processing, Tampere University of Technology (TUT), Finland, where he is leading the Audio Research Group. He received the M.Sc. and Doctor of Science degrees in information technology from TUT in 2001 and 2006, respectively. He has also been working as a research associate at Cambridge University Engineering Department, UK. He is known for his pioneering work on single-channel sound source separation using non-negative matrix factorization based techniques, and their application to noise-robust speech recognition and music content analysis. Recently he has done significant contributions to sound event detection in everyday environments. In addition to the above topics, his research interests include content analysis of audio signals in general and machine learning. He has authored more than 100 scientific publications on the above topics, which have been cited more than 5000 times. He has received the IEEE Signal Processing Society 2012 best paper award for his article “Monaural Sound Source Separation by Nonnegative Matrix Factorization with Temporal Continuity and Sparseness Criteria” as well as three other best paper awards. He is an IEEE Senior Member, member of the Audio and Acoustic Signal Processing Technical Committee of IEEE Signal Processing Society, Associate Editor of IEEE/ACM Transaction on Audio, Speech, and Language Processing, and recipient of the ERC 2014 Starting Grant.

Computational Analysis of Sound Events in Realistic Multisource Environments

Realistic everyday soundscapes often consists of multiple concurrently present sound sources, containing important information for many applications. Recent development in machine learning, especially deep learning has allowed the development of methods to automatically recognize sounds even in these challenging multisource environments. This talk will give an overview of the recent developments in the field. We will start by first discussing the typology of various audio analysis tasks, including classification, detection, and tagging, of both higher-level sound scene categories and also individual sound events. We will then present specific methods based on deep neural networks, which use multilabel classification for efficient multi-source recognition. We will present a convolutional recurrent neural network based approach that has successfully been used in many scene analysis problems. We present how such a network allows simultaneously learning relevant acoustic features for recognition and modeling longer temporal context. The talk will also summarize findings from the recent public DCASE evaluation campaigns.

Orly Alter

Scientific Computing & Imaging Institute

Huntsman Cancer Institute

University of Utah, USA

Orly Alter is a USTAR associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute at the University of Utah, and the principal investigator of an NCI Physical Sciences in Oncology U01 project grant. Inventor of the “eigengene,” she pioneered the matrix and tensor modeling of large-scale molecular biological data, which, as she demonstrated, can be used to correctly predict previously unknown physical, cellular and evolutionary mechanisms. Alter received her Ph.D. in applied physics at Stanford University, and her B.Sc. magna cum laude in physics at Tel Aviv University. Her Ph.D. thesis on “Quantum Measurement of a Single System,” which was published by Wiley-Interscience as a book, is recognized today as crucial to the field of gravitational wave detection.

Comparative Spectral Decompositions for Personalized Cancer Diagnostics and Prognostics

I will describe the development of novel, multi-tensor generalizations of the singular value decomposition, and their use in the comparisons of brain, lung, ovarian, and uterine cancer and normal genomes, to uncover patterns of DNA copy-number alterations that predict survival and response to treatment, statistically better than, and independent of, the best indicators in clinical use and existing laboratory tests. Recurring alterations have been recognized as a hallmark of cancer for over a century, and observed in these cancers’ genomes for decades; however, copy-number subtypes predictive of patients’ outcomes were not identified before. The data had been publicly available, but the patterns remained unknown until the data were modeled by using the multi-tensor decompositions, illustrating the universal ability of these decompositions – generalizations of the frameworks that underlie the theoretical description of the physical world – to find what other methods miss.

Andrzej Cichocki

Brain Science Institue

Riken, Japan

Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering, from Warsaw University of Technology in Poland. He spent several years at University Erlangen-Nuerenberg in Germany, at the Chair of Applied and Theoretical Electrical Engineering directed by Professor Rolf Unbehauen, as an Alexander-von-Humboldt Research Fellow and Guest Professor. In 1995-1997 he was a team leader of the laboratory for Artificial Brain Systems, at Frontier Research Program RIKEN (Japan), in the Brain Information Processing Group directed by Professor Shun-ichi Amari. He was recently senior team leader and the head of the Cichocki laboratory for Advanced Brain Signal Processing, at RIKEN Brain Science Institute in Japan, till April 2018. He is author and co-author of more than 600 technical scientific papers and 5 internationally recognized monographs (two of them translated to Chinese). Under the guidance of Professor Cichocki the new Laboratory “Tensor Networks and Deep Learning for Applications in Data Mining” is established at SKOLTECH. It won the fifth competition for the Russian Federation Government grants for state support in 2017-2019 of scientific research conducted under the supervision of leading scientists at Russian universities and scientific organizations (“Megagrant”). The mission of the Laboratory is to perform cutting-edge research in the design and analysis of deep neural networks, tensor decompositions, tensor networks and multiway component analysis with many potential biomedical applications.

Tensor Networks and their Potential Applications in Dimensionality Reduction and Blind Signal Processing

In this talk we discuss briefly tensor networks which provide a natural sparse and distributed representation for large scale data, and address both established and emerging methodologies for tensor-based decomposition and optimization. Our particular focus will on low-rank tensor network representations, which allow for huge data tensors to be approximated (compressed) by interconnected low-order core tensors. The usefulness of this concept is illustrated over a number of applied areas, including multiway analysis, multilinear ICA/BSS, deep learning, generalized regression, tensor canonical correlation analysis and higher order partial least squares. Special emphasis will be given to the links between tensor networks and deep learning and abilities of some specific tensor networks that significantly compress both the fully connected layers and the convolutional layers of deep neural networks.
  • Cichocki, A., Lee, N., Oseledets, I., Phan, A. H., Zhao, Q., & Mandic, D. P. (2016). Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 1 Low-Rank Tensor Decompositions. Foundations and Trends® in Machine Learning, 9(4-5), 249-429.
  • Cichocki, A., Phan, A. H., Zhao, Q., Lee, N., Oseledets, I., Sugiyama, M., & Mandic, D. P. (2017). Tensor Networks for Dimensionality Deduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives. Foundations and Trends® in Machine Learning, 9(6), 431-673.
  • Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C., & Phan, H. A. (2015). Tensor Decompositions for Signal Processing Applications: From Two-Way to Multiway Component Analysis. IEEE Signal Processing Magazine, 32(2), 145-163.