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Department of Informatics

IfI Summer School 2019 on Data Science

The 2019 IfI Summer School is a week-long event for PhD students and research assistants in informatics and related fields, where invited experts teach a number of different topics in day-long courses on Data Science.

Dates and Location

The summer school will take place June 24-28, 2019 at the University of Zurich, Department of Informatics.

Course location:
Binzmühlestrasse 14
8050 Zurich
Class rooms BIN 2.A.01 und 2.A.10

Courses will be held from 9 a.m. - 5 p.m. (check-in starts at 8:45) with coffee and lunch breaks.

List of Courses

Day Course Instructor ECTS credits
MON, June 24 Research on Digital Trace Data: Collecting online data, ethical perspectives and biases Prof. Dr. Aniko Hannak 0.5 Doctoral or 0.5 Methodology
TUE, June 25 Text Mining with Neural Embeddings Prof. Dr. Mark Fishel 0.5 Doctoral
WED, June 26 Network Science and Machine Learning for High-Resolution Social Networks Dr. Ciro Cattuto 0.5 Doctoral
THU, June 27 Morning: Inequalities in Social Networks Prof. Dr. Markus Strohmaier 0.5 Doctoral
  Afternoon: Visualization Techniques Benjamin Felis
FRI, June 28 Data Visualization Primer Prof. Dr. Miriah Meyer 0.5 Doctoral or 0.5 Methodology

Please note: Mon, Tue, Wed, Fri courses cover a full day; Thu consists of two half-day courses. You need to attend both to get the 0.5 ECTS credits!

Please make sure your register for each course you want to attend separately.


Daily schedule (subject to change as needed by instructors)

08:45 - 09:00 Check-in
09:00 - 10:15 Instruction
10:15 - 10:45 Coffee break
10:45 - 12:00 Instruction
12:00 - 13:00 Lunch (@mensa, not included in cost)
13:00 - 15:00 Instruction
15:00 - 15:30 Coffee break
15:30 - 17:00 Instruction

All students who register for the course on Wednesday are also invited to attend the IfI BBQ. Details to follow.


The Summer School is primarily targeted towards doctoral students in computer science and related fields from the University of Zurich as well as other universities. Registration is free for IfI research assistants, IfI doctoral students, and IfI postdocs. For all other participants, fees are 40 CHF for individual courses. The fee can only be paid by credit card, PostFinance or TWINT using the link below. Attendance will be capped at 40 people per course.

Remember: all participants have to register for EACH course using the link below.

Please note that we cannot issue any invitation letters for visa issues.

Registration is closed.

ECTS Credits

For UZH students, you can find the ECTS credit awarded by each course in the overview above. For non-IfI students who would like to acquire cedits, you need to talk with the person who is in charge of credit transferring at your university first and find out if the ECTS credits awarded by IfI at UZH are accepted/recognized by your university.

Courses and Instructors

June 24


Research on Digital Trace Data: Collecting online data, ethical perspectives and biases.

Course Description

In this class students will learn about designing research using digital trace data. We will walk though tools and methods for collecting data online, ethical and legal considerations when carrying out such research, and finally quality assessment of the collected data with particular focus on potential biases. Students will receive an overview of important lines of research in computational social science and solve practical exercises around data collection.

Instructor Prof. Dr. Aniko Hannak
Short Bio Aniko is an assistant professor at the Vienna University of Economics and Business, and faculty member of the Complexity Science Hub.
She received her PhD from the College of Computer & Information Science at Northeastern University, where she was part of the Lazer Lab and the Algorithmic Auditing Group. Aniko’s main interest lies in computational social sciences, more specifically she is focusing on the co-evolution of online systems and their users. Broadly, her work investigates a variety of content serving websites such as search engines, online stores, job search sites, or freelance marketplaces and uncovers potential negative consequences of the big data algorithms that these websites deploy.

June 25


Text Mining with Neural Embeddings

Course Description

Text mining is the task of extracting structured information from unstructured text, for example discovering events, detecting sentiment or generating tet summaries. In this course we will give a general description of various text mining methods and will focus on automatically extracted vector representations (or embeddings) as the main source of info of text analysis. We will cover various types of embeddings (words or sentences, fixed or contextualized), how they are learned and what their strengths and weaknesses are; we will then focus on practical tasks in text mining that can be tackled using neural embeddings: sentiment analysis, similarity matching, and text summarization.

The curriculum includes alternating theoretical lectures on the background of the introduced methodology as well as practice sessions where the participants can try the methods in practice. The main prerequisite for course participants is a strong background in computer science and mathematics, experinece in programming (especially Python) is also required.

Instructor Prof. Dr. Mark Fishel
Short Bio

Mark Fishel is the head of NLP at the University of Tartu, Estonia. He did his PhD in 2011 in Tartu, and was a post-doc at the University of Zurich between 2011 and 2015. His current research work mainly focuses on the area of low-resource multilinguality, including machine translation, error correction, speech processing and vector space applications; in parallel with theoretical research his group also does applied R&D as part of industrial collaborations.

June 26


Network Science and Machine Learning for High-Resolution Social networks

Course Description

Access to large-scale, fine-grained data on specific human behaviors is changing the way we study social systems and collective behaviors, stimulating the growth of interdisciplinary research domains like Computational Social Science and Network Science, and motivating the study of interesting problems in Computer Science. This course will focus on high-resolution social network data from digital platforms such as smartphones and wearables sensors, and on a variety of applied problems motivated by access to time-resolved relational data. The first part of the course will briefly review elementary notions of network science and supervised machine learning, and then provide an historical overview of low-dimensional vector space representations for nodes in networks, together with hands-on exercises with popular algorithms for node embedding. The second part of the course will focus on high-resolution social networks, illustrating the complex properties and rich structures found in empirical data, and providing an overview of relevant concepts and techniques from social network analysis and unsupervised machine learning. We will close with a second hands-on session based on real data collected by means of wearable sensors.

Instructor Dr. Ciro Cattuto
Short Bio

Dr. Ciro Cattuto is the Scientific Director of ISI Foundation (Torino, Italy and New York, NY, USA). His research interests include data science, network science, computational social science and public health. He is a founder and principal investigator of the SocioPatterns collaboration, an international effort on studying human and animal social networks with wearable sensors, with applications to public health and computational social science. Dr. Cattuto holds a PhD in Physics from the University of Perugia, Italy and has carried out interdisciplinary research at the University of Michigan (Ann Arbor, USA), at the Enrico Fermi Center and Sapienza University (Rome), and at the Frontier Research System of the RIKEN Institute (Japan). He is an adjunct professor at the University of Torino and at Sapienza University, and a Scientific Board member of the PhD in Data Science of Sapienza University. He is an editorial board member of EPJ Data Science, Nature Scientific Data, Journal of Computational Social Science, PeerJ Computer Science and Data & Policy, and he has been an organizer and chair of leading conferences in computer science, complex systems and network science.

June 27


Data Analysis (am) \& Data Visualization (pm)


Inequalities in Social Networks

Course Description

Homophily can put minorities in social networks at a disadvantage by restricting their ability to establish links with people from a majority group. This can limit the overall visibility of minorities in the network, and create biases. In this talk, I will show how the visibility of minority groups in social networks is a function of (i) their relative group size and (ii) the presence or absence of homophilic behavior. In addition, the results show that perception biases can emerge in social networks with high homophily or high heterophily and unequal group sizes, and that these effects are highly related to the asymmetric nature of homophily in networks. This work presents a foundation for assessing the visibility of minority groups and corresponding perception biases in social networks in which homophilic or heterophilic behaviour is present. Overall, the talk will motivate and exemplify new research endeveaors on the intersection between computational and social sciences.

Instructor Prof. Dr. Markus Strohmaier
Short Bio

Markus Strohmaier is the Professor for Methods and Theories of Computational Social Sciences and Humanities at RWTH Aachen University (Germany), and the Scientific Coordinator for Digital Behavioral Data at GESIS - Leibniz Institute for the Social Sciences. Previously, he was a Post-Doc at the University of Toronto (Canada), an Assistant Professor at Graz University of Technology (Austria), a visiting scientist at (XEROX) Parc (USA), a Visiting Assistant Professor at Stanford University (USA) and the founder and scientific director of the department for Computational Social Science at GESIS (Germany). He is interested in applying and developing computational techniques to research challenges on the intersection between computer science and the social sciences / humanities.


Visualization Techniques

Course Description

The ability to visualize manually is an increasingly important competence for many occupational fields. Under
terms such as Visual Facilitation, Visual Recording, Graphic Facilitation, Sketchnoting or Graphic Recording it describes the way in which content can be documented and presented creatively. This workshop allows you to enter this exciting world of hand drawn visualization - whether analogue on the flip chart, digitally on the tablet or simply with any pen on a piece of paper. No prior knowledge needed! With a few colors, simple shapes and small tricks, you can improve your visualization techniques superior to many others.

Instructor Benjamin Felis
Short Bio

Benjamin Felis is a full time graphic recorder & illustrator since 2013. Trainer, consultant and former graffiti artist, with training in graphic recording, visual facilitation, visual presencing (Theory U), Business Model Canvas, coaching and organizational development. Longstanding experience with workshops and as moderator in a variety of industries and positions. 

June 28

Data Visualization Primer

Course Description

Visualizing data is a core activity in data science workflows. In this lecture we’ll look at the basics behind generating effective data visualizations: matching data types to visual channels; perceptual principles that motive design guidelines; and strategies for understanding *what* you want to visualize. We’ll also cover current research approaches for visualizing graphs and for conducting visualization design projects

Instructor Prof. Dr. Miriah Meyer
Short Bio

Miriah is an associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute. She co-directs the Visualization Design Lab, which focuses on the design of visualization systems for helping analysts make sense of complex data, as well on the development of design methods for helping visualization designers make sense of real-world problems. She obtained her bachelors degree in astronomy and astrophysics at Penn State University, and earned a PhD in computer science from the University of Utah. Prior to joining the faculty at Utah Miriah was a postdoctoral research fellow at Harvard University and a visiting scientist at the Broad Institute of MIT and Harvard.

Miriah is the recipient of a NSF CAREER grant, a Microsoft Research Faculty Fellowship, and a NSF/CRA Computing Innovation Fellow award. She was named a University of Utah Distinguished Alumni, both a TED Fellow and a PopTech Science Fellow, and included on MIT Technology Review's TR35 list of the top young innovators. She was also awarded an AAAS Mass Media Fellowship that landed her a stint as a science writer for the Chicago Tribune.



Weiterführende Informationen


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