Invited speakers at SAIS 2022
Professor of Information Technology, Örebro University
Title: AI for robotics and improved human robot interaction
Bio: Amy Loutfi is a Professor in Information Technology at Örebro University and leads the research group - AASS Machine Perception and Interaction Lab. Amy is also active in the programme management group for Wallenberg Autonomous Sensor Systems Programme. Amy Loutfi received her Ph.D. in Computer Science in 2006 with a research topic in machine perception. Specifically, she researched about how gas sensors could be integrated onto robotic platforms and how these robots can interact with humans in order to solve a range of problems that required sensing and perception. She has since broadened her research interests to include general research directions within machine perception, where AI methods like Machine learning are used for the interpretation of sensor data. She also has broadened her research in the area of Human Robot Interaction where she has studied HRI in various platforms that include fully autonomous robots, but also teleoperated robots. She has a long experience working with industry and the public sector on research projects dealing with AI, robotics and human-robot interaction.
Additionally, Amy has worked with a strong engagement in strategic issues in AI for all of Sweden. In addition to her role in WASP, she is involved in AI Sweden, AI competence of Sweden, and the governments’ regeringens samverkansprogram on digitalisation.
In 2020, Amy was elected as a member of the Royal Swedish Academy of Engineering Sciences (Ingenjörsvetenskapsakademin, IVA)
Chair of AI and Computational Linguistics, LMU Munich
Professor of Natural Language Processing, IT University of Copenhagen
Title: Is Human Label Variation Really So Bad for AI?
Summary: Human variation in labeling is typically considered noise. Annotation projects in computer vision and natural language processing typically aim at minimizing human label variation, in order to maximize data quality and in turn optimize and maximize machine learning metrics. However, is all human variation just noise, or can we turn such information into signal for machine learning? In this talk, I will first illustrate the problem and then discuss approaches to tackle this fundamental issue.
Bio: Barbara Plank is chair (Full Professor) of AI and Computational Linguistics at LMU Munich. She leads a research lab in Natural Language Processing (NLP) at the Center for Information and Language Processing (CIS). She is also professor (part-time) at ITU (IT University of Copenhagen), and co-lead of NLP North.
Adj. professor of Mechatronics, Chalmers
VP of Product, Zenseact
Title: Towards Zero faster for safer roads with AI
Summary: Approx 1,3 million fatal traffic accidents every year, our mission is to reduce that to Zero with the help of AI and safe software development.
Bio: Erik Coelingh is VP of Product at Zenseact, and Adjunct Professor at Chalmers University of Technology in Gothenburg Sweden. He received the M.Sc and Ph.D. degrees in electrical engineering from the University of Twente, Enschede, The Netherlands, in 1995 and 2000, respectively. After his studies he joined Volvo Car Corporation where he was responsible for Volvo's first application of Automatic Emergency Braking and led the advanced engineering activities for Pedestrian Detection. From 2008 he pioneered self-driving vehicle technology in the SARTRE and Drive Me programs. In April 2017 he joined Zenuity, later renamed Zenseact in 2020, as VP Technology Advisor with focus on strategy and development of vehicle software enabling autonomy and world-leading safety.
Co-Director of Scientific Vision, AI Sweden
Director of Chalmers AI Research Center (CHAIR)
Bio: Daniel Gillblad is co-Director of Scientific Vision for AI Sweden and Director of the Chalmers AI Research Center (CHAIR). He holds a PhD in Computer Science from the Royal Institute of Technology, has led development efforts and worked on strategy development for Swedish companies, has been part of setting up several AI startups, and serves as an appointed member of the Swedish Government collaboration program on digitalisation and as an appointed expert in the Global Partnership on AI (GPAI). His interests are focused around Machine Learning, large-scale Data Mining and their practical applications.
Vice President of Computer Science, RISE Research Institutes of Sweden
Bio: Hanifeh Khayyeri holds a PhD in biomechanics from Trinity College Dublin and has developed several computational and simulation tools. She has also worked on research funding and research infrastructure both in Sweden and the EU, with a focus on digital infrastructures. Hanifeh has served on a governmental public inquiry and as an expert for the government in several contexts, including on issues such as AI, high performance computing (HPC) and open data.
Manager of Government Affairs & Public Policy, Google Cloud in the Nordics
Bio: Irene Ek has a PhD in management from Stockholm University with a focus on digital transformation and 20 years experience in providing strategic policy advice on digitalisation and AI to the Swedish Government, the EU, UN and the OECD. Previously she was an international AI expert in the OECD AI policy Observatory and vice chair of the OECD Working Party for the measurement and analysis of the digital economy (MADE). She is currently Manager of Government Affairs & Public Policy at Google Cloud in the Nordics.
Strategic Area Lead of Digital transformation, Vinnova
Jessica Svennebring is strategy area lead for Digital transformation, one of ten areas at Vinnova, and has a strong focus on the green and digital transformation of industry and international collaborative projects within digitalisation. She holds a PhD in Applied Physics from the Royal Institute of Technology and has worked with the Government’s innovation partnership programme Digital transformation of industry.