Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.
Bio: Albert Bifet is Professor at Telecom ParisTech, Head of the Data, Intelligence and Graphs (DIG) Group, and Honorary Research Associate at the WEKA Machine Learning Group at University ofWaikato. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the co-author of a book on Machine Learning from Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2018-2012), and ACM SAC Data Streams Track (2019-2012)
Networking as a field is on the verge of a massive paradigm shift towards "deep programmability" in which both the network control plane and the data plane are becoming fully programmable. Thanks to their ability to observe and react to traffic changes in real-time, fully programmable networks hold many promises including increased security, performance, and manageability. Yet, how to realize these promises in practice is still a widely open question. In this talk, I will share some of my views on where I see the field evolving and my experiences conducting research in "deep" programmable networks. I will focus on the topic of network monitoring and explain our quest in enabling programmable measurements; how we leverage them to extract high-quality network data; and how we can analyze and use these data to automatically drive network decisions.
Bio: Laurent Vanbever is a tenure-track assistant professor at ETH Zürich since 2015. Before that, Laurent was a Postdoctoral Research Associate at Princeton where he collaborated with Jennifer Rexford. He obtained his PhD degree in Computer Science from the University of Louvain in 2012. His research focuses on making large network infrastructures more manageable, scalable and secure. Laurent has won several awards for his research including: the SIGCOMM best paper award, the NSDI community award, and four IETF/IRTF Applied Networking Research Prizes.
Often, advances in hardware have been at the base of success of new computing paradigm, algorithms and techniques. This is, e.g., what might happen in the future for quantum computers, and what has recently happened in the field of Artificial Intelligence (AI) and Neural Networks in particular, whose potential has been fully unleashed by commoditization of general-purpose GPUs.
In this keynote, we first introduce recent hardware advances, namely a new family of specialized architectures that are promising enablers for a deeper integration of AI at all network segments, particularly at the edge, and at all layers of the stack. We next discuss challenges and opportunities that are specific to the networking domain, putting them in perspective with advances in other fields.
Bio: Dario Rossi is a Chief Expert on Network AI at Huawei Technologies. Previously, he was Chair professor at the Computer Science department of Telecom ParisTech (2006-2018) and Professor at Ecole Polytechnique (2012-2019). He received his MSc and PhD degrees in from Politecnico di Torino in 2001 and 2005 respectively, and was a visiting researcher at University of California, Berkeley during 2003-2004. He has coauthored 9 patents and over 150 conference/journal papers on different aspects of networking, received 7 best paper awards, a Google Faculty Research Award (2015) and an IRTF Applied Network Research Prize (2016). He is a Senior Member of IEEE and ACM.