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Department of Informatics

IfI Summer School 2022

The 2022 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 one or two day-long courses.

Dates and Location

The IfI Summer School will take place between 27 June - 1 July 2022 at the University of Zurich, Department of Informatics.

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

Exception: Thursday 30 June, this course will take place online only.

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, 27 June

Introduction to Social Computing

Prof. Dr. Kevin Crowston 0.5 Doctoral
TUE, 28 June Programming Languages - a Journey into Abstraction and Composition   Prof. Dr. Guido Salvaneschi 0.5 Doctoral
28-29 June
Design Science Research Methodology
Prof. Dr. Robert Winter 1.0 Methodology
WED, 29 June From Scripts to Reusable Software and Reproducible Research

Dr. André Anjos
Prof. Dr. Manuel Günther

0.5 Methodology

THU, 30 June
online only!

Advances in Reinforcement Learning and Knowledge Transfer

Dr. Markus Wulfmeier
Abhishek Gupta

0.5 Doctoral
FRI, 1 July HCI and AI: The human aspects of designing and building AI and ML systems

Dr. Dan Russell

0.5 Doctoral

Please note: All courses cover a full day. You need to attend the full day to get the 0.5 ECTS credits!
Exception: The 2-day-course with Prof. Winter (28-29 June) covers two days. You need to attend the full two days to get the 1.0 ECTS credits.

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


Daily schedule

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 break
13:00 - 15:00 Instruction
15:00 - 15:30 Coffee break
15:30 - 17:00 Instruction

Please note: Thursday 30 June course will be operated online only.


Registration and Fees

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. Attendance will be capped at 40 people per course.

Registration is closed
- The fees can only be paid by credit card, PostFinance or TWINT using the registration links.
- Please make sure to book only one course per day. 
Please note that we cannot issue any invitation letters for visa issues.
- Contact: Karin Sigg

Links for Registration
- Registration is free for IfI  and CL research assistants, IfI and CL doctoral students, and IfI and CL postdocs. 
- For all other UZH participants, fees are 50 CHF per course day and 100 CHF for the 2-day-course. 
- For all other participants, fees are 100 CHF per course day and 200 CHF for the 2-day-course. 


ECTS Credits

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

Courses and Instructors

27 June

Introduction to Social Computing

Course Description

Social computing systems are those in which people interact with what they believe to be the contributions of others. There is a broad range of social computing systems including email and chat; crowdsourcing, from micro tasks to Wikipedia to open source software to task platforms; social networks, such as Twitter and Facebook; massive online courses and games; and someday, the Metaverse. The course will provide a brief introduction to the main types of social computing systems and an overview of the computing and social research opportunities in the field.

Instructor Prof. Dr. Kevin Crowston


Kevin Crowston is a Distinguished Professor of Information Science at the Syracuse University School of Information Studies (aka the iSchool). He received his A.B. (1984) in Applied Mathematics (Computer Science) from Harvard University and a Ph.D. (1991) in Information Technologies from the Sloan School of Management, Massachusetts Institute of Technology. His research examines new ways of organizing made possible by the use of information technology. He approaches this issue in several ways: empirical studies of coordination-intensive processes in human organizations (especially virtual organization); theoretical characterizations of coordination problems and alternative methods for managing them; and design and empirical evaluation of systems to support people working together. Specific domains of interest include free/libre open source software development projects, citizen science projects and the impacts of artificial intelligence on work. 

28 June


Programming Languages - a Journey into Abstraction and Composition

Course Description

Programming languages are a fundamental interface between humans and computers. Since the early days of computing, scientists have leveraged programming languages to raise the level of abstraction in computing with the goal of expressing programs in a way closer to human thinking than to machines' internal processing of information. Over the years, this process has led to a variety of languages that--similar to human languages--derive from common ancestors, evolve over time, and can be grouped into families that share a common design.
This course identifies some key paths in this large and often unstructured corpus of knowledge. We first provide an historical perspective on programming language evolution. We introduce some of the foundations of programming languages and present how languages have been studied rigorously via proper mathematical tools. We discuss the main families of programming languages, including object-oriented and functional. We then focus on important themes that cross-cut language evolution, such as concurrency, distribution, streams, and domain specific languages. Finally, we discuss recent trends in the programming language landscape, and newcomer languages that made their debut over the last few years. 

Instructor  Prof. Dr. Guido Salvaneschi


Prof. Guido Salvaneschi is leading the Programming group at the School of Computer Science, University of St. Gallen, Switzerland. He holds a PhD in Information Technology from Politecnico di Milano, Italy. He has been a postdoc and an Assistant Professor at the at the Technical University of Darmstadt, Germany. His research has been supported, among the others, by the German Research Foundation (DFG) and by the Swiss National Science Foundation (SNSF). He has published papers in programming languages and software engineering venues including OOPSLA, PLDI, ECOOP, ICSE, FSE and ASE. Personal website:

28-29 June


Design Science Research Methodology

Course Description

This course gives a workshop-style introduction to the Design Science Research (DSR) methodology state of the art. The goal is to enable designing independent DSR studies on Ph.D. level. Students will engage in preparatory readings (1-2 papers, individually assigned), lecturer input, presentations of preparatory readings, in-class discussions, and most importantly project work. The course format offers an interactive learning experience and the unique opportunity to obtain feedback as well as develop preliminary research designs for own DSR studies. The number of feedback rounds and the group size will be determined based on the number of participants.

Instructor Prof. Dr. Robert Winter


Robert Winter is a full professor of business & information systems engineering at the University of St. Gallen. After having served as vice editor-in-chief of the Business & Information Systems Engineering journal and senior editor of the European Journal of Information Systems, he is currently serving on the editorial boards of MIS Quarterly Executive and the Enterprise Modelling and Information Systems Architectures journal. His research interests include design science research methodology, enterprise-level coordination as well as governance of enterprise transformation and digital platforms. He publishes in leading information systems conferences and journals such as MIS Quarterly, European Journal of Information Systems, Journal of Information Technology, and Journal of the AIS. He is engaged in design science research methodology education over many years (over 1000 students from over 20 countries as of December 2021), also serving as primary supervisor for over 60 PhD dissertations and as mentor of nine habilitations.

29 June

From Scripts to Reusable Software and Reproducible Research

Course Description One of the key principles of proper scientific procedure is the act of repeating an experiment or analysis and being able to reach similar conclusions. In the optimal case, researchers should be able to reproduce results from papers that they read with very little effort. The reality is, however, often different. Most authors do not provide any source code leading to their findings, and even when they do, information is incomplete or does not reproduce the results from related works. The course will provide actionable guidelines on how to convert non-reproducible scripts with hard-coded parameters into reusable software. We will exercise topics such as version control, documentation, unit testing and packaging. While most of the concepts are independent of the programming language, examples will be provided in Python.
Instructor Dr. André Anjos
Prof. Dr. Manuel Günther






André Anjos received his Ph.D. degree in signal processing from the Federal University of Rio de Janeiro in 2006. He joined the ATLAS Experiment at European Centre for Particle Physics (CERN, Switzerland) from 2001 until 2010 where he worked in the development and deployment of the Trigger and Data Acquisition systems that are nowadays powering the discovery of the Higgs boson. During his time at CERN, André studied the application of neural networks and statistical methods for particle recognition at the trigger level and developed several software components still in use today. In 2010, André joined the Biometrics Security and Privacy Group at the Idiap Research Institute where he worked with face and vein biometrics, presentation attack detection, and reproducibility in research. Since 2018 André heads the Biosignal Processing Group at Idiap. His current research interests include medical applications, biometrics, image and signal processing, machine learning, research reproducibility and open science. Among André's open-source contributions, one can cite Bob and the the BEAT framework for evaluation and testing of machine learning systems. He teaches graduate-level machine learning courses at the École Polytechnique Fédérale de Lausanne (EPFL) and master courses at Idiap's Master of AI. He serves as reviewer for various scientific journals in pattern recognition, machine learning, and image.

Prof. Dr. Manuel Günther received his diploma in Computer Science and his doctoral degree (Dr.-Ing.) from the Technical University of Ilmenau, Germany, in 2004 and 2012.
Between 2012 and 2015, Prof. Günther was a postdoctoral researcher in the Biometrics Group at the Idiap Research Institute in Martigny, Switzerland.
From 2015 to 2018, Prof. Günther was working as Research Associate at the Vision and Security Technology Lab at the University of Colorado Colorado Springs, Colorado, USA.
After a short industry excursion at trinamiX GmbH in Ludwigshafen, Germany, in July 2020 Prof. Günther started his position as Assistant Professor for Artificial Intelligence and Machine Learning at the Department of Informatics (IfI) of the University of Zurich, Switzerland.

30 June


Advances in Reinforcement Learning and Knowledge Transfer

Course Description In recent years, reinforcement learning (RL) has seen its successes advance from simulation to the real world including domains such as robotics, fusion, chip design, or energy management. Different from supervised learning, RL systems generate their own sources of data by interacting with their environment. Interaction in the physical world brings many additional challenges including cost of data and interaction, safety constraints, partial observability, stochasticity or unspecified rewards. This lecture will be split into two general parts with the first focusing on the fundamental mechanisms underlying RL and the second diving deeper into the challenges of applying RL in the real world with a particular focus on transfer learning.
Instructor Dr. Markus Wulfmeier
Abhishek Gupta (co-lecturer)


Markus Wulfmeier is a Senior Research Scientist at DeepMind with a background in reinforcement learning, transfer, learning from demonstration for robot autonomy. Prior to joining DeepMind he spent his postdoc and PhD at the Oxford Robotics Institute and has been a visiting scholar at the University of California Berkeley, Swiss Federal Institute of Technology in Zürich, and the Massachusetts Institute of Technology. He has been awarded best paper awards at IROS 2016 and GVSETS 2012.

Abhishek Gupta is a postdoctoral fellow at MIT, working with Pulkit Agrawal and Russ Tedrake. He completed his PhD at UC Berkeley working with Pieter Abbeel and Sergey Levine, building systems that can leverage reinforcement learning algorithms to solve robotics problems. He will start as an assistant professor at the University of Washington in Fall 2022. He is interested in research directions that enable directly performing reinforcement learning directly in the real world — reward supervision in reinforcement learning, large scale real world data collection, learning from demonstrations, and multi-task reinforcement learning. He has also spent time at Google Brain. He is a recipient of the NDSEG and NSF graduate research fellowships, and several of his works have been presented as spotlight presentations at top-tier machine learning and robotics conferences. A more detailed description can be found at

1 July

HCI and AI: The human aspects of designing and building AI and ML systems

Course Description As we build and build increasingly sophisticated systems, they’re going to have large pieces that will be AI/ML powered.  This class is about how we can take on the challenge of building systems that are AI-based, yet work in ways that are understandable by humans.  We’ll take on issues of capabilities, fairness, interpretability, and accountability.  We’ll come to learn about what AI systems can (and cannot) do, about what kinds of mental models people have about such systems, and what we can do to design a user experience to make these systems comprehensible.  Ultimately, this is a class about the intersection of human intelligence with artificial intelligence—the two don’t necessarily fit well together, and each makes demands on the other.  As we design and build out AI-based systems, we will need to have our own deep understanding of the materials of AI, and understand what’s possible.
Instructor Dr. Dan Russell


Daniel M. Russell has a Ph.D. in AI, but has spent the past 25 years working in the field of HCI, both from design and research perspectives.  His work at Google has him walking that fine line between artificial intelligence and human intelligence on a daily basis.  His recently published book, The Joy of Search: An Insider’s Guide to Going Beyond the Basics is a compendium of the tactics and strategies everyone needs to be fast and effective online searchers.  He has taught Artificial Intelligence at Stanford University, Santa Clara University, and has taught HCI courses at the University of Zürich, the University of Maryland, and UC San Diego.

Weiterführende Informationen


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