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.
Dates and Location
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.
Overview of the week
|Mon, June 23||
theory-motivated design - or how to make use of empirical|
models for designing better information systems
|Prof. Jan Marco Leimeiser||0.5 Doctoral|
|Mon, June 23||Design and Evaluation of Recommender Systems||Prof. Pearl Pu||0.5 Doctoral|
|Tue, June 24||Introduction to Machine Learning with Python||Konstantin Tretjakov||0.5 Methodology|
|Tue, June 24||Design Mobile Adaptive Systems||Prof. Anind K. Dey||0.5 Doctoral|
|Wed, June 25||
Software Product Management: State-of-Art Exemplified|
from a Researcher's Perspective
|Prof. Samuel A. Fricker||0.5 Doctoral|
|Wed, June 25||
Dancing with Ambiguity: managing the |
|Prof. Larry Leifer||0.5 Doctoral|
|Thurs, June 26||Economics of Information Technology||Prof. Michael Zhang||0.5 Methodology|
|Thurs, June 26||The Data Pipeline||Prof. Jennifer Mankoff||0.5 Methodology|
|Fri, June 27||Discrete Structures: Graphs, Networks, Matrices, Analysis||Prof. Gilbert Strang||0.5 Doctoral|
|Fri, June 27||Measuring the Internet by Flows||Brian Trammell||0.5 Methodology|
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 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.
theory-motivated design - or how to make use of empirical models for designing better information systems
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.
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.
Design and Evaluation of Recommender Systems
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
Evaluation of recommender systems
Decision quality and accuracy
Explanation and trust inspiring interfaces
User acceptance model
Introduction to Machine Learning with Python
Instructor: Konstantin Tretjakov
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.
Design Mobile Adaptive Systemstba
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.
Software Product Managmenet: State-of-Art Exemplified from a Researcher's Perspective
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.
Dancing with Ambiguity: managing the human-machine experience
Instructors: Prof. Larry Leifer
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.
Economics of Information Technologys
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.
The Data Pipeline
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.
Applications of Linear Algebra
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.
Measuring the Internet by Flows
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)
Jan Marco Leimeister is a Full Professor and Director of the Institute of Information Management (IWI-HSG), University of St. Gallen and he is furthermore Chair for Information Systems at Kassel University, Germany. His teaching and research areas include Digital Business, IT innovation management, Service Science, Collaboration Engineering, Ubiquitous Computing and Crowdsourcing. Jan Marco Leimeister serves on the editorial board of various international journals and is regularly member of programme committees of international conferences in the field of Information Systems. He runs several research groups and his research projects are funded by European Union, German Ministries, DFG, various foundations and industry.
Prof. Pearl Pu (EPFL)
Pearl Pu currently leads the HCI Group in the School of Computer and Communication Sciences at the Swiss Federal Institute of Technology in Lausanne (EPFL). Her research interests include recommendation technology, electronic commerce, user adoption of technology, online consumer decision behavior, decision support, content-based product search, travel planning tools, trust-inspiring interfaces for recommender agent, music recommenders, scalable user experience, and social media. She will soon move to the business school of the University of Lausanne where she has been offered a full professor position in “design of emerging technologies”.
She grew up in Shanghai, China, and holds a Ph.D. in Computer and Information Sciences from the University of Pennsylvania in the United States. After graduation, she won the Research Career Award from the United States’ National Science Foundation, which placed her in the top 20% of all Ph.D.s graduated. She is most credited for inventing the critiquing-based recommender system, which was featured in the massive online course on Recommender Systems. This course was diffused to more than 50K students worldwide in the fall of 2014 alone. She was first to show recommender technology’s ability to improve decision accuracy with real users. She was elected two times as the chairperson of the ACM international conference on recommender systems, in 2008 and 2013.
She has previously co-founded two startup companies, where she played major roles in raising VC funds, bringing talents into the team, and designing products. The first company, in the online travel market, was successfully sold to Europe’s number one business travel solution provider, i:FAO. The second one, Fairnez Inc., is a consulting and technology provider in the general e-commerce sector (2004 – present). In 2008, she received the Sina Rising Star award at CHINICT in recognition of her entrepreneurial work.
Konstantin Tretjakov (University of Tartu)
I am currently (as of 2014) working as a researcher at the University of Tartu, dealing with pretty much anything related to machine learning, data analysis and artificial intelligence. The most recent projects had to do with bioinformatics, social network analysis and robotics. I blog (albeit not too frequently in the recent years), at http://fouryears.eu/.
Prof. Anind K. Dey (Carnegie Mellon University)
||Anind K. Dey is the Director of the Human-Computer Interaction Institute at Carnegie Mellon University, and holds the Charles M. Geschke Chair. He is also an Associate Professor and director of the Ubicomp Lab, which performs research at the intersection of ubiquitous computing, human-computer interaction and machine learning, in the areas of mobile computing, health, education and sustainability among others. He has authored over 100 papers on these topics and serves on the editorial board of several journals. Anind received his PhD in computer science from Georgia Tech, along with a Masters of Science in both Computer Science and Aerospace Engineering. He received his Bachelors of Applied Science in Computer Engineering from Simon Fraser University.|
Prof. Samuel A. Fricker (Blekinge Institute of Technology)
Dr. Samuel A. Fricker is assistant professor in the Software Engineering Research Laboratory (SERL Sweden) at Blekinge Institute of Technology (BTH). He has more than ten years experience as senior researcher, lecturer, consultant, and process responsible with companies at any scale, from startups to Fortune500. To pursue his interests in software product management and requirements engineering, he is leading the definition and evaluation of product strategies for the seven pilot products of www.fi-star.eu. Enablers of his research are a small team of PhD and MSc students and cooperation with industry partners through www.ispma.org.
Prof. Larry Leifer (Stanford University)
Larry Leifer is a Professor of Mechanical Engineering Design and founding Director of the Center for Design Research (CDR) at Stanford University. Leifer's engineering design thinking research is focused on instrumenting design teams to understand, support, and improve design practice and theory. Specific issues include: design-team research methodology, global team dynamics, innovation leadership, interaction design, design-for-wellbeing, and adaptive mechatronic systems. As a member of the faculty since 1976, he teaches the industry sponsored master's course ME310, "Global Product-Based Engineering Design, Innovation, and Development” in which the design thinking methodology is applied to the real world problems. This course forms the core of a larger international movement in academia - SUGAR network. Leifer developed the Hasso Plattner Design-Thinking-Research Program and is the editor of the “Design Thinking Research,” a peer commentary journal. He promotes a notion of “d.swiss”, a Switzerland-wide design thinking community of practitioners and academics to popularize values like innovation, creativity, and user-centricity.
Prof. Michael Zhang (The Hong Kong University of Science and Technology)
Professor Michael Zhang is an Associate Professor of Information Systems at the Hong Kong University of Science and Technology, and an affiliated faculty at MIT Center for Digital Business. He holds a PhD in Management from MIT Sloan School of Management, an MSc in Management, a BE in Computer Science and a BA in English from Tsinghua University. Before joining the academia, he worked as an analyst for an investment bank, and as an international marketing manager for a high-tech company. He holds a US patent, and started a social network company. Professor Zhang’s research interests are on issues related to creation, dissemination and processing of information in business and management contexts. His works study entrepreneurship, innovation, pricing, online word-of-mouth, online advertising, incentives of creation in open source and open content projects, and use of information in financial markets. His research has appeared in American Economic Review, Management Science,Journal of Marketing, MIS Quarterly, Journal of MIS, Decision Support Systems, and Journal of Interactive Marketing. He has also been actively involved in professional services, including serving as an Associate Editor for Information Systems Research, a Guest Associate Editor for MIS Quarterly, and a member of the editorial boards of Production and Operations Management andElectronic Commerce Research and Applications.
Prof. Jennifer Mankoff (Carnegie Mellon University)
Dr. Jennifer Mankoff is an Associate Professor in the Human Computer Interaction Institute at Carnegie Mellon University. She earned her B.A. at Oberlin College and her Ph.D. in Computer Science at the Georgia Institute of Technology. Her research embodies a human-centered perspective on data-driven applications. Her goal is to combine empirical methods with technological innovation to construct middleware (tools and processes) that can enable the creation of impactful data-driven applications. Example application areas include sensing and influencing energy saving behavior, web interfaces for individuals with chronic illness, and assistive technologies for people with disabilities. She helped found the sustainable-chi group (firstname.lastname@example.org). Her research has been supported by Google Inc., the Intel Corporation, IBM, Hewlett-Packard, Microsoft Corporation, and the National Science Foundation. She was awarded the Sloan Fellowship and the IBM Faculty Fellowship.
Prof. Gilbert Strang (Massachusetts Institute of Technology)
Gilbert Strang was an undergraduate at MIT and a Rhodes Scholar at Balliol College, Oxford. His Ph.D. was from UCLA and since then he has taught at MIT. He has been a Sloan Fellow and a Fairchild Scholar and is a Fellow of the American Academy of Arts and Sciences. He is a Professor of Mathematics at MIT, an Honorary Fellow of Balliol College, and a member of the National Academy of Sciences. Professor Strang has published eleven books.
He was the President of SIAM during 1999 and 2000, and Chair of the Joint Policy Board for Mathematics. He received the von Neumann Medal of the US Association for Computational Mechanics, and the Henrici Prize for applied analysis. The first Su Buchin Prize from the International Congress of Industrial and Applied Mathematics, and the Haimo Prize from the Mathematical Association of America, were awarded for his contributions to teaching around the world. His home page is math.mit.edu/~gs/ and his video lectures on linear algebra and on computational science and engineering are on ocw.mit.edu (mathematics/18.06 and 18.085).
Brian Trammell (ETH Zürich)
Prior to his work with CSG, he was Engineering Technical Lead at the CERT Network Situational Awareness group, and a veteran of a variety of short-lived Internet start-ups. He is a member of the Internet Architecture Board, and the co-author of 15 RFCs, mainly related to network flow measurement (the IPFIX protocol). He earned his BS in Computer Science from the Georgia Institute of Technology in 2000.