SDS2020 – Keynote & Invited Speakers
AI for Public Goods
Marcel Salathé is a professor of life science and computer science at EPFL, where he heads the Digital Epidemiology Lab. He is the Academic Director of the EPFL Extension School. He is a co-founder of multiple startups, and advises companies and governments on digital technologies.
Towards Learning Systems that Require Less Annotation
Recent advances in Artificial Intelligence have enabled a wide range of breakthroughs in many domains, including image recognition, speech recognition, machine translation, learning to play video games, learning to control simulated and real robots, and mastering the classical game of Go. A lot of this advanced have been powered by supervised learning (where learning happens from human annotation) and reinforcement learning (where learning happens through human scoring of behavior). Both of these can be tedious and costly. In this talk I will discuss recent advances that have promise towards learning from less annotation, through learning-to-learn, transfer learning, and unsupervised learning.
LUCA MARIA GAMBARDELLA
AI based Humans-UAVs proximity interaction
The most recent and interesting results on the use of machine learning and artificial intelligence for Humans-UAVs proximity interaction are presented. In particular Volaly, a pointing gesture system for controlling the motion of UAVs in mostly structured environments and a machine learning UAV system which learns to follow people in indoor and outdoor environments.
Luca Maria Gambardella is Professor at the Faculty of Informatics at USI, Professor at Dalle Molle institute for artificial Intelligence (USI-SUPSI) and CTO of Artificialy sa in Lugano. He has published more than 300 scientific publications with h-index of 71 and he has managed academic and industrial projects where artificial intelligence is applied in various sectors such as robots, industry, finance and logistics.
Digital Servitization: The Next Competitive Frontier
The growing digital transformation is blurring industry boundaries and altering established positions of firms. While firms are investing strategically in data collection, analytics capabilities, and in cloud-based platforms, many remain concerned about how to best address digital disruption and enable new service business models. In fact, many firms with a proven track record of running successful field service organizations struggle with implementing digitally enabled services. In his talk, Dr. Kowalkowski will address both the pitfalls of digital servitization, and what it takes to succeed.
Dr. Christian Kowalkowski is Professor of Industrial Marketing at Linköping University in Sweden and Research Fellow at Hanken School of Economics in Finland. He has rapidly established himself as a leading authority in the field of service strategy research and has developed Sweden’s first university course on B2B service-led growth. For over a decade, through research, consulting, and educational activities about service strategy and implementation, he has worked with market-leading multinationals in various product industries. He has published about 50 papers in scholarly journals, such as Journal of Service Research, Journal of Business Research, and Industrial Marketing Management. In 2019, magazine Fokus ranked him as the 18th most productive and cited researcher in Sweden in the Social Sciences category. Christian is the author of Service Strategy in Action: A Practical Guide for Growing Your B2B Service and Solution Business (www.ServiceStrategyInAction.com; Service Strategy Press, 2017).
Applications of Machine Learning for Non-Conventional Use Cases
In this talk, Tanvi Singh will outline the evolution of some of the machine learning products and use cases developed by her team at Credit Suisse in the area of Risk and Compliance. She’ll outline some of the key challenges – both technical and non-technical – as well as insights and success factors developing solutions in a big corporation in the banking industry, how these can be developed, grown and integrated into the overall business and how she and her team generate and develop ideas for new products in the Regtech and risk space.
At Credit Suisse, Tanvi has built and is leading one of the largest Analytics & Data Science teams in the space of Banking, Financial Regulatory Compliance and Risk in Switzerland. She has two decades of experience in diverse industries and domains, including extensive expertise in AI, Data Science, Machine Learning, Statistics and Digital Analytics. She has been implementing a wide range of use cases in creating and monetizing value from Big Data in the Banking and Finance industry, particularly in the area of Anti-Money Laundering, Employee Surveillance, KRI and KPI Management, and Private Banking.
Quantifying and Mitigating Algorithmic Discrimination
Algorithmic (data-driven learning-based) decision making is increasingly being used to assist or replace human decision making in a variety of domains ranging from banking (rating user credit) and recruiting (ranking applicants) to judiciary (profiling criminals) and journalism (recommending news-stories). Recently concerns have been raised about the potential for discrimination and unfairness in such algorithmic decisions. Against this background, Krishna Gummadi will discuss foundational questions about algorithmic discrimination.
Krishna Gummadi is a scientific director and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He also holds an honorary professorship at the University of Saarland. He received his Ph.D. (2005) and B.Tech. (2000) degrees in Computer Science and Engineering from the University of Washington and the Indian Institute of Technology, Madras, respectively.
Krishna’s research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems.