About

Researcher and engineer with 10+ years experience in the field of telecommunications. I have a strong theoretical background in optimization, operations research and machine learning, holding a PhD for applying these in various network optimization & analysis.

Currently working for Ericsson as telecommunications domain expert & data scientist / analyst in the fields of VoLTE Customer Experience & Network Monitoring, being the system engineer/architect of a team developing a Machine Learning based anomaly detection application. https://www.ericsson.com/en/digital-services/offerings/network-automation/expert-analytics

I was involved in various research projects, with fixed and mobile access networks, including network planning, design, dimensioning and techno-economics, and Work Package leader of European research projects FP7 COMBO.Long-term successful cooperation with Magyar Telekom, contributing as expert to R&D projects on core and transport network design. 

About

Casey is an Associate Professor of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania. His research lab develops deep learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data. This approach reveals underlying principles of an organism’s genetics, its environment, and its response to that environment. He is also the Director of the Childhood Cancer Data Lab (CCDL), powered by Alex's Lemonade Stand Foundation. The goal of the CCDL is to empower childhood cancer researchers poised for the next big discovery with the tools, data, and knowledge to reach it.

About

Csaba Szepesvari is a Canada CIFAR AI Chair, the team-lead for the "Foundations" team at DeepMind and a Professor of Computing Science at the University of Alberta. He earned his PhD in 1999 from Jozsef Attila University, in Szeged, Hungary. He has authored three books and about 200 peer-reviewed journal and conference papers. He serves as the action editor of the Journal of Machine Learning Research and Machine Learning, as well as on various program committees. Dr. Szepesvari's interest is artificial intelligence (AI) and, in particular, principled approaches to AI that use machine learning. He is the co-inventor of UCT, a widely successful Monte-Carlo tree search algorithm. UCT ignited much work in AI, such as DeepMind's AlphaGo which defeated the top Go professional Lee Sedol in a landmark game. This work on UCT won the 2016 test-of-time award at ECML/PKDD.

About

David leads Open Source Machine Learning Strategy at Azure. This means he spends most of my time helping humans to convince machines to be smarter. He is only moderately successful at this.

Previously, David led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.

When not spending too much time in service of electrons, David can be found on a mountain (on skis), traveling the world (via restaurants) or participating in kid activities, of which there are a lot more than he remembers than when he was that age.

About

Eric Tramel discusses the basic concepts underlying the federated Machine Learning approach, the advantages it brings, as well as the challenges associated with constructing federated solutions. Federated learning could be an interface between data science, machine learning, engineering, DevOps, software data, and security engineering, and bringing all this together in one spot.For machine learning test, the compute is happening where the data is, so now we have some other extra complications, we need to bring the compute to the data.If it's data that's generated in a hospital, never take it out of the hospital, leave it there.Eric is the head of Owkin’s Federated Learning Research group, where he heads a team of researchers and engineers to study and build applications of federated and privacy-aware machine learning techniques to medical data.

About

Farjola Peco has 14 years of experience in software development, research in service layer technologies, Media, IoT, analytics and strategy development in the ICT Industry. She read for a Master's degree in Computer Science at Uppsala University after obtaining a B.Sc with Honours in Information Technology and Communications Systems Engineering from the University of Malta. Farjola has co-authored 30+ patents. In her current role as a Director, Technology Strategist in Group Function Technology, she is accountable for driving Ericsson's 3-5 years technology strategy for AI and Automation. Farjola joined Ericsson as a researcher after having spent two and a half years in the start-up community as a software developer. She has since then held several positions in Research before joining the Chief Technology Office as a technology strategist for media-focused products, technology, and services across all Ericsson business units.

About

Red Hat Women in Open Source Academic Award Winner 2017, Google Summer of Code alumna and currently a Machine Learning Engineer at Twitter Inc., Jigyasa Grover is an ardent open source enthusiast along-with being a budding researcher having worked at San Diego Supercomputer Center, National Research Council of Canada and Institute of Research \u0026 Development France. She has also briefly worked on anomaly detection framework in the ads system at Facebook. A feminist by heart, she was involved as Director of Women Who Code and Lead of Google Women Techmakers for a handful of years to help bridge gender gap in technology. She has delivered keynotes and talks at Open Source 101, Red Hat Summit, University of California San Diego Guest Lecture Series, FOSSASIA OpenTech Summit Singapore, Society of Women Engineers Conference, WWC Regional Leadership Summit India, Grace Hopper Celebration of Women in Computing India, IEEE Women in Engineering International Leadership Summit \u0026 Women Techmakers Meet to name a few. In her quest to build a powerful bunch of girls and boys alike believing in “we rise by lifting others” she mentors aspiring developers in the global programs like Google Summer of Code, Google Code- In, ‘Learn IT, Girl !’

About

Lester Solbakken is a Principle Software Engineer at Verizon Media (previously Yahoo) on the vespa.ai platform, the open big data serving engine. His primary focus areas are machine learning engineering with emphasis on serving and search system ranking. Lester previously pursued a PhD within Artificial Intelligence and Machine Learning with neural networks, exploratory data analysis and self-organizing systems as main research topics.

About

Levente is a long time enthusiast of anything cognitive. Originally a programmer, but finished his masters as a Buddhist Theologian, with cognitive science specialization. He started to do AI related work in the early 2000s in knowledge representation, then switched over to become the lead of research for a quantitative finance research group. He later founded an award winning startup (Best Chatbot / AI solution Central European Startup Award, 2016) where he acted as CTO and lead researcher in NLP.

About

Miklos is a senior IT professional with strong business-related skills, he has an entrepreneurial mindset combined with "the Advocate" (INFJ) personality. He started to learn software development when he was 15, therefore he studied Information Technology and Mathematics at University of Szeged. During the 3. semester he founded his first company with other fellow students where they were working during day and night as a garage firm. Later he moved to Germany to work for Audi as a software developer then switched to Volkswagen but remained as an external colleague. Then he spent 1 year in Hungary worked for the German Audi department as an external senior software developer, but he needed new challenges, therefore he accepted a position in Beijing, China at Volkswagen Group China (VGC) as an IT Consultant. Being an expat for 3 years in the capital he managed to create a Software Quality measurement platform for the IT department which controlled the software suppliers of VGC. During this period he started to be interested in data science, machine learning and its business-related opportunities. In 2015 relocated to Hungary and founded his first startup aiwebtesting.com, and started to teach at Audi Academy as an IT Trainer. In the same year he started an MBA degree at CEU (Central European University), he successfully graduated in 2017. He works as a Data Science Trainer, he delivers Big Data and Machine Learning courses at multinational companies and soon he will launch his ML-focused podcast. 

About

Dr. Paris Buttfield-Addison is co-founder of Secret Lab a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was mobile product manager for Meebo, a ground-breaking 'Web 2.0' startup which was acquired by Google. Paris particularly enjoys game design, statistics, the blockchain, machine learning, and human-centered technology research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly Media. He is currently writing 'Practical AI with Swift', 'Head First Swift', and the 'Unity Game Development Cookbook'. He holds a degree in medieval history and a PhD in computing.

About

Reza Bosagh Zadeh is founder and CEO at Matroid and an Adjunct Professor at Stanford. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. He's served on the Technical Advisory Boards of Databricks, and has been working on Artificial Intelligence since 2005 when he worked in Google's AI research team. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award at Stanford.

About

I'm a machine learner, data scientist, ex-roboticist, and consultant.
I like turning crazy ideas into production systems using data science and software engineering.

About

Wiebe Van Ranst was born on the 9th of November 1990 in Bornem, Belgium. In 2013 he got his master’s degree in Electronics-ICT. Both Wiebe’s bachelor’s and master’s thesis were about GPU processing which was still a very small field at the time. Starting a PhD was a logical continuation of this. Wiebe started his PhD in 2013, after finishing his master’s thesis, under professor Joost Vennekens. His main interest then mainly focused on artificial intelligence in general and GPU computing. In 2016 Wiebe briefly worked on a start-up company called obtronics, focusing on computer vision, the technology of which was one year later taken over by the company RoboVision. During this experience Wiebe’s interests shifted towards GPU processing for computer vision, and later also computer vision itself. His research was then also supervised by professor Toon Goedemé. Wiebe is currently active as a post-doctoral researcher in the EAVISE research group of KU Leuven. Currently, his research is on applying neural nets on embedded hardware.

Wiebe's talk will be about recent research on real-world adversarial samples.