IFI Summer School 2014

The 2014 IFI Summer School is a week-long event for graduate students and reasearch assistants in informatics and related fields, where invited experts teach a number of different topics in day-long courses on a variety of topics in Computer Science.

The summer school will take place June 23-27, 2014 at the University of Zurich BIN (Department of Informatics, Binzmühlestrasse 14, 8050 Zürich). The courses will be held in two parallel sessions in room 2.A.01 and 2.A.10 from 9:00 - 17:00 (check-in starts at 8:45am) with coffee and lunch breaks.

All registered students are also invited to attend the summer school social event, which will take place either on Wednesday, June 25th or Thursday, June 26th directly following the course. Details to follow.

The Summer School is open to 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 and ifi doctoral students. For all other students, fees are 90 CHF for the entire five-day summer school, or 20 CHF for individual courses. Attendance will be capped at 40 people per course.

Preference will be given to IfI doctoral students and research assistants and other participants will be admitted on a first-come-first-served basis.

Registration is now closed. For further information please contact Daniela Meier.

The fee will be paid on site in cash only at the Check-in desk outside the classrooms every day between 8:45 and 9:00am.

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 transfering in your school first and find out if your school accepts/recognizes the ECTS credits awarded by IfI at UZH.

Instructors: Prof. Jan Marco Leimeister

The purpose of the course is to develop an understanding of theory-motivated design in the Information Systems (IS) scientific discipline and to understand the research scientific research process when employing theory-motivated design.

Outline of the Course
The course will comprise an introductory lecture with discussion sections on seminal papers. The course is structured as follows:
1) The course begins with a short background lecture by the instructor.
2) Student presentations are included on selected topics for seminal papers.
3) In-depth discussions within the class are made after each student presentation.
4) Exercises to apply theory-motivated design.

Content
The course will address the four key elements of theory-motivated design:
1) Principles of scientific research
2) Theories in the IS discipline
3) Design-orientated science in the IS discipline
4) Using theory-motivated design

Each student will be assigned one paper by the instructor for review and presentation to the class after registration is completed. The presentations will be scheduled accordingly as part of the relevant session, and are expected to be of 15 minutes length plus discussion time. Students are required to read the required readings prior to the course and to prepare a critical review of the assigned papers as a presentation that is presented within the course.

Instructor: Prof. Pearl Pu

As online stores offer practically an infinite shelf space, recommender systems are playing an increasingly important role in helping users search and discover items that they want to buy. In this course, I will introduce the problematic of product recommenders, the challenges and some of the solutions.

In particular I will give an overview of recent literature in decision theory, explaining the discrepancies between normative models of how people should reason and empirical studies of how they in fact think and decide. I then describe a recommender technology that helps users make better decisions while minimizing their cognitive effort.

The second part of the course will be dedicated to the interface design and evaluation of recommender systems, such as explanation interfaces, trust building, and user acceptance of recommender systems.

The course will be organized as a whole-day event, with a style of teaching that promotes learning by examples and classroom discussions.

Table of Content
Design of recommender systems
A brief overview of recommender technology
What is product search?
How do people choose items from a large set of options?
Prescriptive vs. descriptive theories of decision making

Decision recommenders
Evaluation of recommender systems
Decision quality and accuracy
Explanation and trust inspiring interfaces
User perceptions
User acceptance model

Instructor: Konstantin Tretjakov

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The process of observing the world, discovering patterns in observations, and describing them in terms of concise models has always been at the core of science (and perhaps even human life in general). Nowadays, however, thanks to the development of computing technologies, the data that we can collect and store is so vast and diverse, that no single human is capable of processing it. Machine learning (also known as "data mining" or "pattern analysis") is a field, which deals with algorithms for discovering patterns and estimating ("learning") useful models from the data. In just a couple of decades, machine learning has grown from a rather niche area of computer science and statistics into a flourishing field, which lies at the heart of countless pieces of software and hardware, used by us in everyday life. The course offers a gentle, hands-on introduction to the core principles and techniques of machine learning. Topics covered are general probabilistic estimation, trees, linear models and some instance-based techniques, such as kernel methods. Students will implement and apply most of the discussed algorithms using the interactive environment IPython. Some extent of basic familiarity with programming, probability theory and linear algebra (or at least a vague recollection of those areas) is expected from the participants. The description and the materials of the course are available at http://kt.era.ee/ifiss2014.

Instructor: Prof. Anind K. Dey

The world is becoming increasingly mobile. What we used to do on a desktop computer 5 years ago, we only do on mobile devices today. Despite the fact that our mobile devices are increasingly "smart", they continue to act incredibly dumb. In this course we will discuss how to design and build usable, intelligent and adaptive mobile applications that will provide a compelling user experience. The goal of the course is to teach and give you practice with mobile design and prototyping skills, and to investigate new research and commercial opportunities that can be made available through truly smart mobile devices.

Instructor: Prof. Samuel A. Fricker

Strategic positioning, high impact with low cost, and active management along the lifetime are some of the critical ingredients of successful software. In this course, we explore how to use literature research methods for identifying state-of-art in common software product management (SPM) areas. We then use principles of action research to evaluate SPM methods that we select among the methods we have identified. The evaluation involves the application of the methods in real-world cases, which have been initiated in answer to EU FP7 and Horizon 2020 challenges. The course concludes by roadmapping a vision for the SPM field based on the obtained insights from analysis of state-of-art and from evaluating the SPM methods. For the participants interested in managing software products, this course gives an understanding of what this discipline is and how to apply it in practical situations. For the participants interested in research, this course gives a hands-on introduction to the work with literature and empirical research and provides a basis to plan own relevant research to further state-of-art. The course will be run in participatory format and participants will have the opportunity to work in small groups on problems. The participants will be provided with materials for reading prior to taking the course.

Students who take this course will be provided with materials that they are required to read prior to taking the course.

Instructors: Prof. Larry Leifer

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People relate to machines and media as people (Reeves and Nass, “The Media Equation”). Therefore the experience we have with products and services must be examined through the lens of social conventions and dialog. Think of this as teamwork. Thirty years of engineering-design teamwork research confirms the power of this point of view. Insights from that body of research will inform the workshop and our activities.

The workshop aims to engage participants in this line of reasoning through hands-on activities. A solid academic research protocol informs each activity. Imagine, human computer interaction as team-work. To explore this point of view closely, one member of each 3-person team will take the role of the “computer” while others assume the role of “humans,” the later may be the more difficult. Examples will be taken from the automotive industry (semi-autonomous cars relating to their drivers); rehabilitation engineering (robots assisting persons with physical, sensory, and cognitive impairments); and engineering design-team performance measurement (including pair programmers).

The human-machine relationship will be examined through a framework that include three concurrent dimensions:

1. Negotiation - exchanging information between humans and machines
2. Feeling - understanding human emotions towards their machines and vice versa
3. Learning - the process of mutual adjustment between humans and their smart devices

In a series of activities using Design Thinking techniques, participants will experience the interactive processes in teamwork. Based on their own observations and research results provided by the instructor, they will explore the topic of social dynamics in teams while relating it to the human-machine interaction.

Instructor: Prof. Michael Zhang

This seminar will be devoted to the development of empirical methods for causal inference in economics. In particular, we will discuss various empirical techniques that address endogeneity issues in the literature, and look how these different approaches illuminate our understanding in reality.

The purpose of this seminar is to prepare students for conducting rigorous research that can potentially have an impact on the real world.

We will use lectures, class discussions, exercises and team projects to examine a variety of topics.

Instructor: Prof. Jennifer Mankoff

The increasing availability of data has created a sea change in the way we build interactive systems. It is possible to easily access information about a person’s activities, online and offline, about the state of the world around them, and about the activities of other people connected to them either directly or through their use of shared resources. This information can help to contextualize interaction, support inference, provide recommendations, or be directly investigated by the user themselves. The goal of this course is to provide you with the tools to build data-driven interactive systems and explore the new opportunities enabled by this data of discussion of best practices, and practical skills development.

Instructor: Prof. Gilbert Strang

The program will begin with the most important model in discrete applied mathematics: the n nodes and m edges that form a graph. We focus on the matrices that allow to understand the properties of the graph : the node-edge incidence matrix, the node-node adjacency matrix, and the graph Laplacian matrix. So much of classical network theory (voltages and currents) and modern network theory (social interactions, cliques, communication) are expressed in the language of graphs and matrices.

We also develop the analysis of banded matrices (close interactions) and their inverses (full matrices with special properties). The particular roles of Markov matrices and positive definite matrices and the Fourier matrix are essential to understand in applications.

Instructor: Brian Trammell

The course is a general introduction to network flow measurement for research purposes. A flow is a record detailing the endpoints, time, and volume of a TCP/IP connection. Starting with a general introduction to the concepts of passive measurement in general and flow measurement in particular, including how this differs from packet trace analysis, it then covers conceptual and implementation-level details of IPFIX, the IETF-standard export protocol for flow data, including a hands-on exploration using open-source software (the python-ipfix package, the QoF flow meter, and the pandas data analysis environment). Further topics include analysis of enhanced flow data for passive TCP performance measurement, an exploration of what can go wrong in network data analysis in general and flow measurement in particular, and a discussion of how to do science with passively-measured traffic data while preserving the privacy of the users of the network.

Prof. Jan Marco Leimeister (University of St. Gallen and University of Kassel)

Prof. Pearl Pu (EPFL)

Konstantin Tretjakov (University of Tartu)

Prof. Anind K. Dey (Carnegie Mellon University)

Prof. Samuel A. Fricker (Blekinge Institute of Technology)

Prof. Larry Leifer (Stanford University)

Prof. Michael Zhang (The Hong Kong University of Science and Technology)

Prof. Jennifer Mankoff (Carnegie Mellon University)

Prof. Gilbert Strang (Massachusetts Institute of Technology)

Brian Trammell (ETH Zürich)