Andrada Fiscutean

Andrada FiscuteanAndrada Fiscutean is a technology journalist and radio news editor. She writes about hackers, nation-state malware, women in tech, and startups and companies based in Eastern Europe. Her stories have appeared in Ars Technica, WIRED, Vice Motherboard, ZDNet, The Outline and more. Passionate about the history of technology in Eastern Europe, Andrada owns several home computers made in the region, all of them clones of the British ZX Spectrum. She lives in Bucharest, Romania. You can follow her on Twitter @AFiscutean. Below is some of her writing:

Andrada Fiscutean

Hacking the Iron Curtain. From smuggling computer parts to owning the world

In the late 1980s, when the Berlin Wall was still in place and Eastern Europe was in the last throes of communism, a few students decided to make their own computers, in their dorm room. They used smuggled or discarded parts bought from electronics dealers who came to the Politehnica University of Bucharest campus. After communism collapsed and internet arrived in Romania, the same dorm rooms hosted some of the world's greatest hackers at the time. Late at night, they bribed the doorkeeper with cheap vodka to let them enter the ED011 computer lab. Quietly, with their computer screens dimmed, they challenged themselves to hack NASA, the Pentagon, and the US Army. The 1980s and 1990s were romantic times in technology. Back then, the focus was on discovering and building things with minimum resources. Hackers usually wanted to learn and to test themselves, not to spy, steal secrets or make money.

Marit Hansen

Marit HansenSince 2015 Marit Hansen is the Data Protection Commissioner of Land Schleswig-Holstein and Chief of Unabhängiges Landeszentrum für Datenschutz (ULD; in English: Independent Centre for Privacy Protection). Before being appointed Data Protection Commissioner, she has been Deputy Commissioner for seven years. Within ULD she established the "Privacy Technology Projects” Division. Since her diploma in computer science in 1995 she has been working on privacy and security aspects. Her focus is on “data protection by design” and “data protection by default” from both the technical and the legal perspectives.

Building in Privacy and Data Protection - what is demanded by the GDPR?

The General Data Protection Regulation (GDPR) is the new legal framework for data protection throughout Europe. A few new instruments are introduced, among others "Data Protection by Design" and "Data Protection by Default". The talk will give an overview of the GDPR, go into detail concerning those provisions that are related to built-in privacy and data protection as well as security, and inform about requirements and options for developers.

Elisa Celis

Elisa CelisElisa Celis is a Senior Research Scientist at the School of Computer and Communication Sciences at EPFL. Prior to joining EPFL, she worked as a Research Scientist at Xerox Research where she was the worldwide head of the Crowdsourcing and Human Computation research thrust. She received a B.Sci. degree in Computer Science and Mathematics from Harvey Mudd College and a Ph.D. in Computer Science from the University of Washington. Her research focuses on studying social and economic questions that arise in the context of the Internet and her work spans multiple areas including fairness in AI/ML, social computing, online learning, network science, and mechanism design. She is the recipient of the Yahoo! Key Challenges Award and the China Theory Week Prize.

Fairness and Diversity in Online Social Systems

Social systems are now fueled by algorithms that facilitate and control connections and information. Simultaneously, computational systems are now fueled by people -- their interactions, data, and behavior. Consequently, there is a pressing need to design new algorithms that are socially responsible in how they learn, and socially optimal in the manner in which they use information. Recently, we have made initial progress in addressing such problems at this interface of social and computational systems. In this talk, we will first understand the emergence of bias in data and algorithmic decision making and present first steps towards developing a systematic framework to control biases in classical problems such as data summarization and personalization. This work leads to new algorithms that have the ability to alleviate bias and increase diversity while often simultaneously maintaining their theoretical or empirical performance with respect to the original metrics.

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