Net AI
Paul Patras is currently the Co-Founder & CEO of Net AI since August 2020. Paul previously held the position of Associate Professor at The University of Edinburgh from April 2019 to the present. Before that, they were a Lecturer (Assistant Professor) and Chancellor's Fellow at The University of Edinburgh from September 2013 to March 2019. Additionally, they volunteered at Shift.ms starting in February 2015. Paul has also served as a Visiting Professor at the University of Brescia from March to May 2017, as well as a Visiting Researcher at Northeastern University College of Computer and Information Science in July and August 2016, and at Technische Universität Darmstadt in May and June 2016. In April to June 2015, they were a Visiting Professor at the University of Brescia. Further, they held the position of Research Fellow at NUI Maynooth from April 2011 to September 2013. Lastly, they worked as a Research Assistant at Institute IMDEA Networks from October 2007 to March 2011, where they focused on control-theoretic adaptive mechanisms for improving performance of IEEE 802.11 networks, under the supervision of Dr. Albert Banchs. Paul also worked as a Visiting Researcher at Rice University from January to July 2010.
Paul Patras completed their education as follows:
1. From 1997 to 2001, Paul attended Colegiul National "Liviu Rebreanu" Bistrita, where they obtained their GED in Computer Programming.
2. From 2001 to 2006, they attended the Technical University of Cluj Napoca, earning a Dipl.Eng degree in Telecommunications.
3. In 2006, Paul studied at the Universitat Politècnica de Catalunya as an intern, focusing on Advanced Broadband Communications.
4. From 2007 to 2008, they pursued an MSc in Telematics Engineering at Universidad Carlos III de Madrid.
5. Lastly, from 2007 to 2011, they continued their studies at Universidad Carlos III de Madrid, completing a PhD in Telematics Engineering.
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Net AI
Net AI had developed a cloud-native platform that uses AI to provide real time analytics, which can drive the optimization of virtualized mobile networks. The volume of mobile data traffic is exploding and 5G will have to fulfill a growing variety of performance requirements, ranging from extreme mobile broadband to low-latency automotive IoT. Toaccommodate such demands, enhanced flexibility in managing the infrastructure is needed. Network slicing allows operators to customize resources on a per-service basis, by virtually partitioning the physical infrastructure, thereby enabling new lucrative revenue streams. However, without deep intelligence into the traffic flowing over slices, and where it originates, it is impossible to effectively and efficiently monetize them. To address this need, our Microscope software uses AI to perform mobile traffic decomposition. Microscope identifies and quantifies the nature and source of individual streams (e.g. Netflix, Google cloud services, Facebook, etc; down to individual base station) from aggregate streams. This allows data to be collected in the cloud rather than from expensive location-based probes, and also works with encrypted data sources which are impossible to analyze with traditional approaches such as using Deep Packet Inspection (DPI). Our technology tackles the challenges of decomposition through deep learning, due to its effectiveness in operating on large-scale mobile traffic in real-time, as demonstrated by our own research. Microscope is fast, cheap, encryption agnostic, scalable, and compatible with current NFV and open RAN initiatives