17 and 18 July – 2020
17 and 18 July – 2020
DSPT Day is an independent and international conference in Portugal, organized by DSPT and dedicated exclusively to topics related with Data Science, bringing together professionals, companies, trainers and enthusiasts of related fields.
As a result of the imposed restrictions on events due to the COVID-19 pandemic, but mostly having our attendees’ safety in mind, we’ve shifted the 2020 edition of DSPT Day to a fully online event – it is now called DSPTDay Online. Besides delivering amazing talks and incredible Data Science content, DSPTDay Online will be completely free!
100% online space for discussion and networking with other experts
Leaders, practitioners and trainers from all over the world.
Energizing and high quality talks.
Case studies and/or personal experiences related with Data Science.
Alexander Jung has obtained a Phd (Sub auspiciis Praesidentis) in statistical signal processing from TU Vienna in 2012. He has been Post-Doc at ETH Zurich and TU Vienna before he joined Aalto University as Assistant Professor for Machine Learning in 2015. The focus of his research is to understand fundamental limits and efficient methods for machine learning problems arising in various application domains. The quality of his research has been recognized by a Best Student Paper Award at the conference IEEE ICASSP 2011 and an Amazon Web Services Machine Learning Award in 2018. He co-authored a paper that was finalist for the best student paper award at Asilomar 2017. While at Aalto University, he has redesigned the main course on Machine Learning and developed a new online course "Machine Learning with Python".
He has been selected as Teacher of the Year 2018 by the Department of Computer Science at Aalto University. He serves as the chair of the Signal Processing and Circuits & Systems Chapter within the IEEE Finland Section.
GrAI Matter Labs
Dr. Orlando Moreira is Fellow and Chief Architect at GrAI Matter Labs, a neuromorphic chip company, where he is responsible for the design of the neuromorphic processor core. He graduated from the University of Aveiro and he holds a PhD in Electrical Engineering from the Eindhoven University of Technology. He worked at Philips Research (2000-2007), NXP Semiconductors (2007-2008), ST-Ericsson/Ericsson (2008-2015) and Intel (2015-2018). From 2007 to 2009, he lead a joint Nokia/NXP/ST-Ericsson team in developing a real-time software architecture for multi-radio. At Ericsson, he designed a system architecture for a Dual Sim Dual Call modem. At Intel, he lead a tools team delivering the software development kit for imaging processors, including simulators, compilers and debuggers. He has published research on neuromorphic computing, reconfigurable computing, real-time multiprocessor scheduling, resource management, and temporal analysis of data flow applications.
IBM Research Zürich
Amira is a predoc researcher in the Quantum Research Group at the University of KwaZulu-Natal in South Africa and former research scientist at STANLIB Multi-Manager. She is also part of the IBM Quantum Computing Research team in Zürich where her current research focuses on the intersection of quantum mechanics and machine learning theory in order to solve problems that are not possible to compute classically. Amira holds an undergraduate degree in actuarial science, an honours degree in quantitative finance, a masters degree in physics and is currently pursuing her PhD in quantum computing. She is an active member of numerous community driven initiatives centered around strengthening science and technology in Africa.
Pedro Saleiro is a senior research manager at Feedzai where he leads the FATE (Fairness, Accountability, Transparency, and Ethics) research group. He is responsible for several initiatives related to improving model explainability in the context of financial crime prevention, bias auditing and algorithmic fairness, experimentation and A/B testing, ML governance and reproducibility. Previously, Pedro was a postdoc at the University of Chicago and research data scientist at the Center for Data Science and Public Policy working with Rayid Ghani, developing new methods and open source tools (aequitas.dssg.io), and doing data science for social good projects with government and non-profit partners in diverse policy areas.
Diana Borsa is a senior research scientist at DeepMind (Alphabet) working on fundamental concepts and algorithms in reinforcement learning (RL) and continual learning.
She is a mathematician by training and at heart, with a natural curiosity towards the world, how things work, how things behave and why, how learning and intelligence emerge. She loves research and especially projects at the interplay of many disciplines: computer science, mathematics, neuroscience and cognitive science.
More formally, she holds Bachelor degrees in Mathematics, Electrical Engineering and Computer Science as well as International Business. She came to London, and joined University College London for her graduate studies in 2011, where she completed a MSc. Machine Learning and further on a MRes./Phd in the Computational Statistics and Machine Learning Centre. She spent quite a bit of time between 2013-2016 in Cambridge at the Microsoft Research lab, working on automatic machine learning, generative models and variational methods, as well as reinforcement learning and games. Finally, at the beginning of 2017, she joined one of DeepMind's core research teams working on RL.
She is very passionate about education and promoting diversity and inclusion in Machine Learning and STEM.
Sara Hooker is a researcher at Google Brain doing deep learning research on reliable explanations of model predictions for black-box models. Her main research interests gravitate towards interpretability, model compression and security. In 2014, she founded Delta Analytics, a non-profit dedicated to bringing technical capacity to help non-profits across the world use machine learning for good. She grew up in Africa, in Mozambique, Lesotho, Swaziland, South Africa, and Kenya.
Leo Dirac started his career building software at big companies including Microsoft and Google. In 2012 he fell in love with neural networks when he first saw AlexNet, and subsequently dedicated himself to applying deep learning. He spent six years at Amazon where he launched Amazon's first deep learning projects for visual search, product similarity, and recommendations. He was the lead engineer on AWS's first ML service called Amazon Machine Learning, and used those lessons to design the core of AWS SageMaker, and led the teams that built SageMaker's AutoML features including Automatic Model Tuning and AutoPilot. Leo continues seeking ways to accelerate the pace of technological advancement for all of society.
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