IfI Summer School 2022

MON
27 June

Introduction to Social Computing

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.

TUE
28 June

Programming Languages - a Journey into Abstraction and Composition

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. 

TUE-WED
28-29 June
(2-day-course)

Design Science Research Methodology

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.

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.

WED
29 June

From Scripts to Reusable Software and Reproducible Research

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.

THU
30 June

Advances in Reinforcement Learning and Knowledge Transfer

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 https://abhishekunique.github.io

FRI
1 July

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

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.