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Data Science Lab2021-09-23T11:52:29+02:00
Digital Science Lab
Data Science Lab

In an increasingly digital society, data generated by human activity is the new black gold. New methods are being developed to acquire, transmit, sort, store and analyze this exponentially growing data in order to obtain useful information. Data science is the algorithmic analysis of massive amounts of structured and unstructured digital data (big data) through the use of analytical methods and I.T. These processes are made possible through the development of new infrastructures for data storage and processing (cloud computing; high performance computing), data exploration (data mining), automatic learning (machine learning; deep learning) and through the use of artificial intelligence.

The gathering and analysis of data, in particular of digital records, allows the humanities and social sciences give a sense to human and social behaviors. Social networking sites and platforms thus become key observation and analysis tools in regard to the evolution of our society, especially in the field of information, or inversely, of disinformation and fake news.

The use of econometric and statistical methods not only allows for the identification of patterns and of information on market trends, but also makes it possible to predict and influence a person’s purchasing behavior. In the legal field, LegalTechs are developing algorithms that can cross-reference and process case law data in order to render or predict legal decisions (predictive justice).

The use of digital data for the profiling of individuals raises social and ethical issues. The large scale processing of individuals’ digital data, often without the knowledge of the users who generate it, calls for the possibility to protect personal and sensitive data which are often difficult to anonymize. A consideration regarding the right to control one’s personal data, the right to prevent unauthorized disclosure, as well as the right to be forgotten or the right to have one’ s data removed from a database is necessary.

Projects
Publications
Publications in the field of Science

Maissen Pascal/Felber Pascal/Kropf Peter/Schiavoni Valerio, FaaSdom: a benchmark suite for serverless computing, DEBS’20: Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems, 2020, p. 73-84.

Rocha Isabelly/Morris Nathaniel/Chen Lydia/Felber Pascal/Birke Robert/Schiavoni Valerio, PipeTune: Pipeline parallelism of hyper and system parameters tuning for deep learning clusters, Middleware’20: Proceedings of the 21st International Middleware Conference, 2020, p. 89-104.

Birke Robert/Rocha Isabelly/Pérez Juan Fernando/Schiavoni Valerio/Felber Pascal/Chen Lydia, Differential approximation and sprinting for multi-priority big data engines, Middleware’19: Proceedings of the 20th International Middleware Conference, December 2019, p. 202-214.

Ghiassi Amirmasoud/Younesian Taraneh/Zhao Zilong/Birke Robert/Schiavoni Valerio/Chen Lydia, Robust (Deep) Learning Models Against Dirty Labels, Proceedings of IEEE TPS 2019, International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, Los Angeles 2019.

Publications in the field of Economics and Business

Ammann Manuel/Fischer Sebastian/Weigert Florian, Factor exposure variation and mutual fund performance, Financial Analysts Journal, 2020, p. 101-118.

Burri Marc/Kaufmann Daniel, A daily fever curve for the Swiss economy, Swiss Journal of Economics and Statistics, 2020.

Moro Arielle/Holzer Adrian, A framework to predict consumption sustainability levels of individuals, Sustainability, 2020.

Ungeheuer Michael/Ruenzi Stephan/Weigert Florian, Joint extreme events in equity returns and liquidity and their cross-sectional pricing implications, Journal of Banking and Finance, 2020.

Moro Arielle/Holzer Adrian, A framework to predict fine-grained sustainable consumption behavior levels of individuals, International Conference on Information Systems (ICIS), 2019.

Pignard-Cheynel Nathalie/Standaert Olivier/Van Dievoet Lara/Ballarini Loïc, Experimenting how Facebook’s algorithm works. Feedback on a case study with journalism students, 5th World Journalism Education Congress, Paris 2019.

Chabi-Yo Fousseni/Ruenzi Stefan/Weigert Florian, Crash sensitivity and the cross section of expected stock returns, Journal of Financial and Quantitative Analysis, 2018, p. 1059-1100.

Ruenzi Stefan/Weigert Florian, Momentum and crash sensitivity, Economics Letters, 2018, p. 77-81.

Agarwal Vikas/Ruenzi Stefan/Weigert Florian, Tail risk in hedge funds : A unique view from portfolio holdings, Journal of Financial Economics, 2017, p. 610-636.

Weigert Florian, Crash aversion and the cross-section of expected stock returns worldwide, The Review of Asset Pricing Studies, 2016, p. 135-178.

Community
Name Surname
Profile

Prof. Pascal Felber
Faculty of Science

Name Surname
Profile

Prof. Florence Guillaume
Faculty of Law

Name Surname
Profile

Prof. Florian Weigert
Faculty of Economics and Business

Name Surname
Profile

Prof. Tim Kroencke
Faculty of Economics and Business

Name Surname
Profile

Prof. Adrian Holzer
Faculty of Economics and Business

Name Surname
Profile

Prof. Nathalie Pignard-Cheynel
Faculty of Economics and Business

Name Surname
Profile

Prof. Daniel Kaufmann
Faculty of Economics and Business

Name Surname
Profile

Dr. Valerio Schiavoni
Faculty of Science

Name Surname
Profile

Dr. Arielle Moro
Faculty of Economics and Business

Name Surname
Profile

Dr. Matthieu Mulot
Faculty of Science

Name Surname
Profile

Dr. Rebecca Stuart
Faculty of Economics and Business

Name Surname
Profile

Marc Burri
PhD student, Faculty of Economics and Business

Name Surname
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Nikolay Pugachyov
PhD student, Faculty of Economics and Business

Name Surname
Profile

Emanuele Guidotti
PhD student, Faculty of Economics and Business

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