Keynote Speakers

Prof. Kai Rannenberg

Deutsche Telekom Chair of Mobile Business

Goethe University Frankfurt; Germany

Title: "Privacy and Security Issues in the Internet of Things"

Time: 25 October 2017 at 14:00 


The Internet of Things, preannounced or ca. 20 years now, is meanwhile becoming a reality in many application areas. Moreover, it is arriving in several areas of private, public and business life, that had not been exposed to Internet connectivity and related challenges so far.

This presentation will discuss the respective privacy and security issues focussing on examples from the smart home, sensing in the public space and Industry 4.0 (smart manufacturing).”


Kai Rannenberg holds the Deutsche Telekom Chair (formerly T-Mobile Chair) of Mobile Business & Multilateral Security since 2002. Before he was with the System Security Group at Microsoft Research Cambridge, UK focussing on „Personal Security Devices & Privacy Technologies“.
1993-1999 Kai worked at Freiburg University and coordinated the interdisciplinary “Kolleg Security in Communication Technology”, sponsored by Gottlieb Daimler & Karl Benz Foundation researching Multilateral Security. After a Diploma in Informatics at TU Berlin he had focused his PhD at Freiburg University on IT Security Evaluation Criteria and their potential and limits regarding the protection of users and subscribers.

Since 1991 Kai is active in the ISO/IEC standardization of IT Security and Criteria (JTC 1/SC 27/WG 3 “Security evaluation criteria”). Since March 2007 he is Convenor of the SC 27/WG 5 “Identity management and privacy technologies”.
Since October 2015 Kai is an IFIP Vice President, before he was an IFIP Councillor since 2009. From May 2007 till July 2013 he chaired IFIP TC-11 “Security and Privacy Protection in Information Processing Systems”, after having been its Vice-Chair since 2001. Kai is active in the Council of European Professional Informatics Societies (CEPIS) chairing its Legal & Security Issues Special Interest Network (CEPIS LSI) since 2003.
From July 2004 till June 2013 Kai served as the academic expert in the Management Board of the European Network and Information Security Agency, ENISA and is now a member of ENISA's Permanent Stakeholder Group .
Kai`s awards include the IFIP Silver Core, the Alcatel SEL Foundation Dissertation Award and the Friedrich-August-von- Hayek-Preis of Freiburg University and Deutsche Bank.


Prof. Ali Ghodsi

Statistics and Actuarial Science

University of Waterloo

Title: "Generative Mixture of Networks"

Time: 25 October 2017 at 10:30 


I present a  generative model based on training deep architectures. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks together and update the clusters of the data. We call this model Mixture of Networks. The provided model is a platform that can be used for any deep structure and be trained by any conventional objective function for distribution modeling. As the components of the model are neural networks, it has high capability in characterizing complicated data distributions as well as clustering data. We apply the algorithm on MNIST handwritten digits and Yale face datasets. We also demonstrate the clustering ability of the model using some real-world and toy examples. This is a joint work with Ershad Banijamal and Pascal Poupart


Professor Ghodsi is currently a member of the Centre for Computational Mathematics in Industry and Commerce, and the Artificial Intelligence Research Group at the University of Waterloo. He has worked in two other world-class research environments at the University of Toronto and the University of Alberta.

In particular, over the past three years he has spent a significant amount of research time at the Probabilistic and Statistical Inference Group at the University of Toronto and at the Alberta Ingenuity Centre for Machine Learning at the University of Alberta, where he collaborated on statistical machine-learning methods applied to robotics and pattern recognition problems. Since 1992 he has spent five years in industry where he was involved with both software design and implementation.



Prof. Alexander Wong

Canada Research Chair in Medical Imaging Systems

Associate Professor in the Department of Systems Design Engineering at the University of Waterloo

Title: Deep Learning with Darwin: Evolutionary Synthesis of Operational Deep Intelligence


Deep learning has given rise to a major revolution in the field of artificial intelligence (AI). A major challenge with the democratization and proliferation of deep learning as commodity AI for all is the sheer complexity of current deep neural networks, making them ill-suited for operational use in a large number of scenarios. Taking inspiration from biological evolution, this talk explores the idea of “Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?”. Recent findings will be presented that support such an evolutionary synthesis paradigm for achieving operational deep intelligence across a wide variety of scenarios.


Professor Alexander Wong is the Canada Research Chair in Medical Imaging Systems, co-director of the Vision and Image Processing Research Group, and an associate professor in the Department of Systems Design Engineering at the University of Waterloo.  He has published over 400 refereed journal and conference papers, as well as patents, in various fields such as computational imaging, artificial intelligence, computer vision, and multimedia systems.  In the area of artificial intelligence, his focus is on operational deep intelligence (a pioneer in evolutionary deep intelligence, discovery radiomics, and random deep intelligence via deep-structured fully-connected graphical models).


Prof. Vincenzo Piuri

Candidate for 2018 IEEE President-Elect

Department of Computer Science, Università degli Studi di Milano

Title: Computational intelligence technologies for industrial and environmental applications


Adaptability and advanced services for industrial manufacturing require an intelligent technological support for understanding the production process characteristics also in complex situations. Quality control is specifically one of the activities in manufacturing which is very critical for ensuring high-quality products and competitiveness on the market. Computational intelligence can provide additional flexible techniques for designing and implementing monitoring and control systems, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems. This talk will analyze the opportunities offered by computational intelligence technologies to support the realization of adaptable operations and intelligent services in industrial applications, specifically focusing on manufacturing processes and quality control.


Professor Vincenzo Piuri has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He is Full Professor at the University of Milan, Italy (since 2000), where he was also Department Chair (2007-2012). He was Associate Professor at Politecnico di Milano, Italy (1992-2000), visiting professor at the University of Texas at Austin, USA (summers 1996-1999), and visiting researcher at George Mason University, USA (summers 2012-2016). He founded a start-up company, in the area of intelligent systems for industrial applications (leading it from 2007 until 2010) and was active in industrial research projects with several companies.

His main research interests are: intelligent systems, computational intelligence, neural networks, signal and image processing, machine learning, pattern analysis and recognition, intelligent measurement systems, industrial applications, biometrics, distributed processing systems, internet-of-things, cloud computing, fault tolerance, and arithmetic architectures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters.

Vincenzo Piuri has been very active in promoting activities, cooperation, and internationalization in the IEEE. He has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-17) and Associate Editor of the IEEE Transactions on Computers, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.

He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications. He is Honorary Professor at the Obuda University, Budapest, Hungary (since 2014), and Guest Professor at Guangdong University of Petrochemical Technology, China (since 2014) and at the Muroran Institute of Technology, Japan (since 2016).

Prof. Ernesto Damiani

Full Professor at Department of Computer Science, and Research Coordinator at SESAR Lab, Università degli Studi di Milano.

Title: Toward Big Data Analytics as a Service


The advent of Big Data created new challenges in the design, development and deployment of applications. Big data applications involve multiple components (collection and cleaning, data lake creation and management, analytics parallelization etc.) that have different features and lifecycles, and correspond to a rich panoply of software tools. Experience has shown that model-based approaches leading from technology independent to technology dependent models and finally to deployment support well the design of data-intensive applications. However, while classic MDA transformations introduce each technology-dependent feature at a pre-set stage of the model refinement chain, Big Data computations deliver their best in term of scalability and efficiency by making binding architectural and data modelling decisions at the last possible moment, i.e. when information on Big Data distribution, volume and variety becomes available. Big-Data-as-a-Service, where data features can be different for each deployment of the analytics, can benefit from a Software Product-Line (SPL) parametric approach to keep multiple alternative models alive and postpone binding modelling decisions via variation points, in order to make them at the right (i.e., the last possible) moment. The talk presents the TOREADOR approach to delay modelling decisions for all key aspects of a Big Data application, including data preparation, representation and storage, analytics parallelization and visualization.


Ernesto Damiani is a professor of computer science at the University of Milan, where he leads the SEcure Service-oriented Architectures Research (SESAR). His research spans cyber-security, Big Data and service-oriented computing, where he has published over 400 peer-reviewed articles and books. Ernesto is a Distinguished Scientist of ACM. He is a recipient of the Stephen Yau award from Services Society, of the Chester Sall Award from the IEEE Industrial Electronics Society and of the IFIP TC II Outstanding Service Award. He received an honorary doctorate from Institut National des Sciences Appliqués de Lyon, France (2017). Ernesto served as Program and General Chair of many international conferences, including IEEE ICWS, IEEE Big Data Congress, IEEE DEST, ACM SAC and DEXA. He serves in the editorial board of several international journals; among others, he is the EIC of the Services Transactions on Big Data and of the International Journal of Knowledge and Learning. He is Associate Editor of IEEE Transactions on Service-oriented Computing and of the IEEE Transactions on Fuzzy Systems.


Masoud Makrehchi

Prof. Masoud Makrehchi

Associate Professor - Faculty of Engineering and Applied Science
University of Ontario Institute of Technology (UOIT), Osahawa, Canada

Filter and channel theory play essential roles in modeling many engineering problems. A channel can be a transmission line, computer memory, a learning machine, or a compression algorithm. We spend a great deal of effort to design trustworthy channels or filters. Trustworthy channels don’t lie, don’t betray the source, or mislead the destination. But these all are based on one strong assumption: source is good, trustworthy, and honest. Almost in all models, the honesty of the source is out of context. In the case of natural and man-made sources such as sensors, engineers model and estimate sampling and measurement error. But dishonesty is different from error. In social context, error is an honest mistake but with good faith. Error is not because of bad intention and mostly caused by poor judgement. 

Dishonesty has two features to be detected: insincere intention and lack of fairness. Unfortunately in most social and human behavior studies which are based on data collection from questionnaires and surveys, these two variables are hard to measure. As a matter of fact, we humans demonstrate our true intentions and honest opinion when we unintentionally express our views. It means we don’t manipulate our thought for the sake of interests or to prevent any threat. But most lab experiments, surveys, and questionnaires are based on this fact that participants know they are under study.

In this talk, first the theory of Honest Data is informally introduced and discussed and then we show that Social Data is honest. This is where Social Computing and Human Computation come to play. Some flagship projects are introduced and a summary of our research group is presented.


Prof. Ali Mohammad-Djafari

Research Director at The National Center for Scientific Research (CNRS), France


Title: Error variable splitting forward model and sparsity enforcing priors in a Bayesian approach for linear inverse problems.

Regularization and Bayesian inference based methods have been successfully applied for linear inverse problems. In these methods, often simple Gaussian or Poisson models for the forward model errors have been considered. In this work, first a complete Bayesian inference framework with more appropriate models for accounting the errors and uncertainties is presented. In particular, we use variable splitting for the errors to model different sources of errors and their possible non-stationarity or impulsive nature using Student-t or other heavy tailed distributions.

Also, as a prior model, a sparsity enforcing hierarchical model of Infinite Gaussian Mixture model is introduced. With these prior models, we obtain a complete Bayesian
inference framework which can efficiently be implemented for any linear inverse problem.
Interestingly, many recent regularization-based algorithms such as Alternating Direction Method of Multipliers (ADMM) as well as more  classical Bayesian based methods such as Sparse Bayesian Learning (SBL)  are obtained as particular cases.

One advantage of the Bayesian approach is the possibility to estimate, jointly with the reconstruction, the hyper-parameters such as the regularization parameter, thus the
capability of proposing un-supervised methods.
Examples of implementation of the proposed methods in biological signal processing, and in computed tomography are mentionned and refrenced.


Ali Mohammad-Djafari received the B.Sc. degree in electrical engineering from Polytechnic of Teheran, in 1975, the diploma degree (M.Sc.) from Ecole Supérieure d'Electricité (SUPELEC), Gif-sur-Yvette, France, in 1977, the "Docteur-Ingénieur" (Ph.D.) degree and "Doctorat d'Etat" in Physics, from the University of Paris Sud 11 (UPS), Orsay, France, respectively in 1981 and 1987.

He has supervised more than 20 Ph.D. Thesis, more than 20 Post-doc research activities and more than 50 M.Sc. Student research projects. He has more than 60 full journal papers and more than 300 papers in national and international conferences. He has organized or co-organized more than 10 international workshops and conferences. He has been expert for a great number of French national and international projects. Since 1988 he has many teaching activities in M.Sc. and Ph.D. Level in SUPELEC, University of Paris Sud, ENSTA, Ecole centrale de Paris and Université Paris Saclay.

He also participated and managed many industrial contracts with many French national industries such as EDF, RENAULT, THALES, SAFRAN and great research institutions such as CEA, INSERM, INRIA as well as the regional (such as Digiteo), national (such as ANR) and European projects (such as ERASYSBIO).

For an overview and acces to more details of his activities and publications, please see his web page: for general, for news and activities and for the list of publications.