Abstract

Machine Learning (ML) has taken the world by storm. Today, all real-time and non- real-time financial transactions are evaluated using machine learning models; lane-assist and self-driving car technology are mostly based on ML; medical decisions and even prison-parole judgments are being backed by modern machine learning. As machine learning systems are mostly based on stochastic processes, this raises significant challenges in terms of determinism, repeatability, transparency, and overall safety. In this talk we will discuss the challenges and lessons learned of building large scale machine-learning system for real-time risk management and fraud prevention. Today, at Feedzai, we process over $5 billion dollars’ worth of transactions per day, every single day. If you have made a transaction, being it a card payment, an online payment, or even a mobile payment, it’s very likely that our ML systems evaluated it, deemed it worth of being honored or blocked it. In this talk we will cover not only the engineering aspects and its implications, but also the impact of having ML-based systems making autonomous decisions that impact human lives and outcomes, and what type of properties we would like such systems to exhibit.

Short Biography

Paulo is the Chief Technology Officer and co-founder of Feedzai, a leading data science and machine learning company managing risk and fraud prevention for worldwide financial institutions. Before founding Feedzai, Paulo was co-Director of the Carnegie Mellon University|Portugal Professional Master in Software Engineering program and had a dual appointment between the University of Coimbra and CMU. Over the years, he led a large number of projects for the European Space Agency, Microsoft Research, Siemens among other companies. The projects ranged the design and implementation of safety and business critical systems, programming languages and runtime environments, virtualization, and software design. Paulo holds a PhD in Distributed Systems from the University of Coimbra, has authored over 40 peer-reviewed publications and a book. He’s currently Scientific Director of the CMU|Portugal program, member of the Forbes Technology Council, and an early-stage technology investor.

Abstract

Medtronic, located in more than 150 countries and with more than 47,000 patients in its catalog, is one of the largest companies in the medical technology sector. Traditionally, Medtronic has been known as a provider of medical devices but its aim to improve patients’ lives has led to a shift towards a value-based model, providing integrated care across the entire clinical pathway, from prevention to long-term follow-up. This value-based healthcare involves considering the patient as the center of its own process, and making decisions based on consensus, using information from different actors and sources. Therefore, health data utilization strategies and techniques are considered as fundamental for Medtronic.
Hence, the project GATEKEEPER arises, financed by the European Commission with more than 40 partners from 14 different countries. The project aims to connect different actors (HC providers, businesses, citizens, etc.) to create the appropriate arena to ensure independent lives of the elderly population. GATEKEEPER will be embodied by an open source, standard-based, interoperable and secure framework available to all developers, for creating combined digital solutions. Among other things, this platform will feature intelligent services for early risk detection and care plans, and a federated data infrastructure are provided to healthcare professionals for designing, deploying and validating innovative personalised treatments and therapies.

CV

Mr. Jorge Posada (Male) graduated in Business and Management in Universidad Carlos III de Madrid in 2011 with a Post Graduate Diploma in Management Skills by the Universidad Francisco de Vitoria in 2012. He is currently the Innovation Portfolio Manager of IHS, managing a portfolio of collaborative Innovation projects and solutions, in different phases of development, from ideation to commercialization. This role includes the relationship with VC, Investment Funds and scouting of Start-up´s and SME´s in the global market. INNOVA´s projects deal with a wide variety of Disease areas, and make use of different KET´s, such as IoT, AI, Remote Monitoring, Big Data, Process Improvement, Information Systems, diagnostics, etc.  He also acts as the link between H2020 developments and results, and the Medtronic Innovation Ecosystem, especially focused on Integrated Health Solutions´ strategy and approach to market needs. In his role as Project Manager in European Projects, he has worked in the METABO project of the FP7 and has been in charge as Project Manager in MOSAIC project. Currently, he is the Project Manager of the ACTIVAGE LSP and GATEKEEPER H2020 Projects.

Abstract

An alumni from DEI FCTUC has now had a short career in tech, jumping between startups and large multi billion dollar companies. This has led to a big exposure to big data and data engineering. In this talk Francisco will share how data engineering has been present throughout his career. Starting with social media monitoring at Talkwalker, going through high user reach and mass media and A/B testing at eBay and finally sharing some of his experience at Palantir.
In this talk we’ll briefly cover:
the value of social media and how corporations use social media monitoring to understand market trends.
how large corporations use A/B testing and statistical analysis to land and validate new user experiences.
How data engineering and data operating systems are changing the day to day of market leaders, whom bias their decision making more and more in insights extracted from the data they have had available for years.
Overall this aims at sharing the professional experience of Francisco at Talkwalker, eBay and Palantir, focused on data engineering and i’s impact. It will not share particular strategic details of each of these companies due to confidentiality reasons.

CV

Francisco Ferreira holds a BSc and a MSc in Informatics Engineering from the University of Coimbra. He is an experienced engineering lead and manager, currently active as a Data Science Engineer at Palantir Technologies  in Switzerland. He leads teams and projects that span multiple continents, owing complex strategic goals ranging from digital transformation of mature industries (i.e.: aviation, banking, …), to bespoke product development. Also a highly technical business oriented engineer with a strategic mindset, startup investor and mentor.

Some key experience:

• Palantir Technologies Multiple Locations – since 2015
• Senior Architect            Remote – Zurich – 2020
• Head of Product and Infrastructure, Lead architect of Skywise – Aviation platform for airlines and Airbus. London, Toulouse, Tokyo – 2017 – 2020
• Architect – Banking São Paulo, London – 2017
• R&D Software Engineering Lead London, Palo Alto 2016-2017
• Full Stack Software engineer London, New York 2015 – 2016
• pt (Founder) Remote – since 2017
• eBay San Jose – London – 2013 to 2015
• Trendiction – Talkwalker Luxembourg – 2011 to 2013
• IQS – Quality Software Portugal – 2008 – 2011
• WIT – Software Portugal – 2008

Abstract

CV

Tomás Pariente Lobo has more than 30 years of experience in IT. His technical expertise is mainly in Artificial Intelligence, Big Data, Linked Data and knowledge management. Since June 2006 Tomas works as a project manager and technical coordinator for EC-funded projects leading a group of researchers dealing with all aspects related to the data value chain, with special focus on data architectures, data analysis and technologies such as Natural Language Processing and semantics.

Abstract

Over the last 10 years, the resurgence of the field of neural networks (Deep Learning) and its fusion with Reinforcement Learning, has led to a series of impressive results in simulated environments like computer games, as well as in practical applications like Search Engines, Recommender Systems, Machine Translation, Robotics, etc. More recently, the scientific community realized that some of these ML techniques can also be used to tackle open problems in basic sciences like Mathematics, Physics, Chemistry or Biology. In this talk we will give an overview of some recent developments in this emerging field of “Scientific Machine Learning” and try to map the landscape of opportunities that are now ahead of us.

CV

Hugo is a Machine Learning researcher and co-founder of Inductiva Research Labs, a portuguese research-driven startup that is using Machine Learning to tackle fundamental problems in science. Prior to Inductiva, he worked at Google DeepMind, Microsoft, EPFL and the European Space Agency, where he explored diverse areas like Computer Vision, Search Quality, Time Series forecasting, Reinforcement Learning and even Computational Biology. His academic background is in Informatics and Computing Engineering.