The 2013 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 24-28, 2013 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 24||Financial Intelligence and Applications||
Prof. J. Leon Zhao|
Prof. Daning Hu
|0.5 Doctoral Course|
|Mon, June 24||Scholarship Skills||Prof. Emerson Murphy-Hill||0.5 Didactics|
|Tue, June 25||Robust Statistics and Outlier Detection||Prof. Mia Hubert||0.5 Methodology|
|Tue, June 25||Design Theory and Design Theorizing||Prof. Shirley Gregor||0.5 Methodology|
|Wed, June 26||Qualitative Research Methods in IS Research||Prof. Natalia Levina||0.5 Methodology|
|Wed, June 26||How to build a driver-less car||Dr. Andrea Censi|
Prof. Davide Scaramuzza
|0.5 Doctoral Course|
|Thurs, June 27||Software Architectures for Mobile Systems||Prof. Nenad Medvidovic||0.5 Doctoral Course|
|Thurs, June 27||
Preference Elicitation in Multiagent Domains:|
Mechanism Design, Voting and Stable Matching
|Prof. Craig Boutilier||0.5 Doctoral Course|
|Fri, June 28||Multicore Software Engineering||Dr. Victor Pankratius||0.5 Doctoral Course|
|Fri, June 28||Demo-Driven Research||Dr. Tudor Girba||0.5 Didactics|
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 26th or Thursday, June 27th 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 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 for the Summer School 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 calssrooms 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.
Financial Intelligence and Applications
Instructors: J. Leon Zhao and Prof. Daning Hu
Financial intelligence is the study of business intelligence techniques and applications in financial institutions and markets, integrating technical and business issues. In this short course, we focus our attention on technical issues and business applications in financial risk management in financial institutions and markets. Particularly, the 2008 financial tsunami provides a business scenario that indicates the importance of risk control at the occurrence of rare events as burst of market bubbles. This course will cover several topics including 1) Financial risk management: frameworks and processes, 2) Modeling and simulation of financial processes and applications, and 3) Business intelligence for banking networks. In each topic, we will introduce basic concepts and frameworks, overview core research issues, and present example research projects. This course would be of value to researchers who are interested in developing research topics in data mining and business intelligence in the context of financial applications.
Instructor: Prof. Emerson Murphy-Hill
Scholarship Skills covers the mechanics of doing computer science
research. The day will be split up into 4 sections, 90 minutes each:
- Searching, reading, and reviewing. This topic will cover how to find and organize papers relevant to your research, how to read research papers, and how the scientific review process is done.
- Written presentations. This topic will cover tips for better writing (e.g., active voice, concision), style in writing English, and the structure of a scientific paper.
- Oral presentations. This topic will discuss the different kinds of oral presentation (elevator pitch, scientific presentation, industry presentation, examination) and tips for effectively communicating orally.
- Beyond grad school. This topic will discuss what job options are available after graduate school, what you should be doing now to prepare for them (beyond the research), and what the professor job search is like.
Robust Statistics and Outlier Detection
Instructor: Prof. Mia Hubert
The aim of this course is to acquire knowledge and insight in robust statistical methods and outlier detection techniques. Robust statistical methods are resistant to outlying observations in the data, and hence are also able to detect these outliers.
In this course we will first introduce general notions of robustness such as breakdown value, influence function and maxbias curve. Then we will study several robust estimators of univariate location, scale and skewness. Next we consider robust estimators of multivariate location, covariance and linear regression. Finally we discuss robust estimators for principal component analysis, which are useful for the analysis of high-dimensional data . Throughout we illustrate how the methods can be applied on real data using freely available software in Matlab and R.
Design Theory and Design Theorizing
Instructor: Prof. Shirley Gregor
This workshop is targeted at all those interested in conducting or better understanding design science research (DSR) in information technology and related fields. DSR concerns the design and construction of artifacts (including methods and interventions in the world) and knowledge contributions concerning artifact design and construction.
Topics will include:
1. The background to DSR and an overview of methodology and theory development in DSR
2. Positioning DSR research so that its likelihood of publication and contributions to knowledge and theory are enhanced
3. Structuring a design science publication, and
4. A framework for reflecting and extracting design principles and theory.
The workshop will be run in participatory format and participants will have the opportunity to work in small groups on problems.
Students who take this course will be provided with materials that they are required to read prior to taking the course.
Qualitative Research Methods in IS Research
Instructor: Prof. Natalia Levina
This is an introductory daylong course of Qualitative Research Method in Information Systems. The purpose of day is to introduce students to a variety of paradigms and approaches motivating the use of qualitative methods in IS research as well as to narrow in on several specific methods such as interviews, case studies and ethnographies. The key postulates of the grounded theory methods used to develop theory from data will be discussed. Finally, special attention will be paid to publishing qualitative research in premier IS journals.
Students who take this course will be provided with materials that they are required to read prior to taking the course.
How to build a driver-less car
Instructors: Dr. Andrea Censi and Prof. Davide Scaramuzza
In this course, you will get an introduction about the four cognitive layers of a driverless car: 1) Perception, 2) Localization, 3) Path Planning, 4) Control. In Perception, you learn which sensors can be used to make the car perceive the environment and learn a model of it. Notably, you will learn how to extract and process the information from cameras and laser scanners onboard the vehicles. in Localization, you will learn how a robot can use its onboard sensors to estimate its ego-motion and localize its position within a given map. Both GPS-enabled and GPS-denied environments will be analyzed. In Path Planning, you will learn how program a robot to plan a suitable trajectory between two points in the map. In Control, you will learn the fundamentals to control a car to follow a given trajectory. The lecture will be supported by many videos and examples.
Software Architectures for Mobile Systems
Instructor: Prof. Nenad Medvidovic
Over the past two decades software architecture has come to the forefront of a number of critical software engineering activities: modeling, design, analysis, simulation, implementation, deployment, maintenance, and evolution. Architecture is advocated as an effective conceptual tool for addressing the many challenges of developing large, complex, distributed systems. Largely in parallel to these developments, significant advances have also been made in the domains of mobile, embedded, and most recently, cloud-based computing. The systems in these domains are also large, complex, and distributed; they are frequently expected to dynamically adapt to failures as well as changing requirements and execution contexts. At first blush, a number of the advances in these domains appear not to have resulted from an explicit software architectural focus. However, this course will show that architecture offers clear, and often critical, benefits in these domains. In support of this argument, the course will overview the state-of-the-art in these domains, with a specific focus on the role software architecture should and actually does play in them. The course will highlight the characteristics of software architectures as well as specific architecture-based approaches that make them particularly suitable to developing mobile, embedded, and cloud-based systems.
Preference Elicitation in Multiagent Domains: Mechanism Design, Voting and Stable Matching
Instructor: Prof. Craig Boutilier
Effective decision support systems must address the "preference bottleneck," specifically, the need to elicit or otherwise assess the preferences of users. This course surveys a variety of techniques that have been developed in AI, and related fields, for preference and utility assessment, as well as certain forms of interactive optimization. The question of preference assessment has been tackled in a variety of fields: decision analysis, operations research, engineering design, economics, marketing, and others. While concepts from these fields will be discussed, the technical development will focus largely on methods that are especially well-suited to tackling the preference bottleneck for autonomous/AI systems. The first half of the course will introduce basic principles and illustrate key concepts. The second half will emphasize multiagent aspects of the preference elicitation and discuss how to apply these methods in mechanism design, voting and stable matching problems.
Multicore Software Engineering
Instructor: Dr. Victor Pankratius
Multicore processors with several cores on a chip turn PCs, laptops, servers, and mobile devices into parallel computers. This tutorial prepares software engineers for the challenging task of writing parallel applications of all sorts. It presents an overview of concepts and techniques, such as basics of parallel programming, design patterns for parallelism, parallelism in modern programming languages, and testing and debugging techniques. The tutorial also discusses automatic performance tuning methods on multicore.
Instructor: Dr. Tudor Girba
Research is less about discovering the fantastic, as it is about revealing the obvious. Thus, the most important research challenge is not the fight against nature, but against our own entrentched assumptions. One way of fighting against our own assumptions is to expose them and get feedback. I advocate the practice of demo-driven research, a way of doing research that puts emphasis on presenting the state of research with any given chance and to any audience willing to listen. Strike that: it's not just presenting, it's demoing. It's demoing the story of your idea. I want to get our hands dirty and make our ideas palpable and exciting enough to spark interest and raise helpful feedback. You might think it is difficult or even impossible to demo what you do. It's not, but it does require practice. You might think your subject is dry and not exciting for outsiders. It's not, but it does require you to adopt a less obvious point of view. In this course, we will take the time to tackle these points. And along the way, we will discover some more interesting side-effects of this approach.
Prof. J. Leon Zhao (City university of Hong Kong)
J. Leon Zhao is Head and Chair Professor in Information Systems, City University of Hong Kong. He was Interim Head and Eller Professor in MIS, University of Arizona. He holds Ph.D. from Haas School of Business, University of California at Berkeley. His research is on information technology and management, with a particular focus on collaboration and workflow technologies and business information services. He is director of Lab on Enterprise Process Innovation and Computing funded by NSF, RGC, SAP, and IBM among other sponsors. He received IBM Faculty Award in 2005 and was awarded Chang Jiang Scholar Chair Professorship at Tsinghua University in 2009. He has been associate editor of ACM Transactions on MIS, IEEE Transactions on Services Computing, Information Systems Research, Decision Support Systems, Electronic Commerce Research and Applications, Information Systems Frontiers among others. He has chaired numerous conferences including the IEEE International Conference on Service Economics (SE'12), the International Conference on Design Science Research in Information Systems and Technology, Switzerland (DESRIST'10), the 2010 International Conference on Electronic-Business Intelligence (ICEBI'10), and IEEE International Conference on Services Computing, Bangalore, India (SCC'09
Prof. Emerson Murphy-Hill (NC State University)
Emerson Murphy-Hill is an assistant professor at North Carolina State University. His research interests include the intersection between human-computer interaction and software engineering. In 2010, completed a post-doc with Gail Murphy at the University of British Columbia. He completed a Ph.D. in Computer Science from Portland State University in 2009 under Andrew P. Black. He holds a B.S. from the Evergreen State College.
Prof. Mia Hubert (University of Leuven)
Mia Hubert is professor at the department of Mathematics of the University of Leuven, and member of the Leuven Statistics Research Centre (LStat). She is teaching several courses in statistics for bachelor and master students in mathematics, statistics and social sciences. She is specialized in the development and study of robust and nonparametric methods for univariate, multivariate , high-dimensional and functional data. She has been an associate editor of Journal of Computational and Graphical Statistics and Computational Statistics and Data Analysis. Currently she is associate editor of Technometrics and member of the editorial board of Journal of Chemometrics.
Prof. Shirley Gregor (Australian National University)
Shirley Gregor is Professor of Information Systems at the Australian National University, Canberra, where she is a Director of the National Centre for Information Systems Research. Professor Gregor’s current research interests include the innovative and strategic use of information and communications technologies, intelligent systems, human-computer interaction and the philosophy of technology. Dr Gregor has published in journals such as MIS Quarterly, Journal of the Association of Information Systems, International Journal of Electronic Commerce, International Journal of Human Computer Studies, European Journal of Information Systems and Information Technology & People. Professor Gregor was made an Officer of the Order of Australia in the Queen’s Birthday Honour’s list in June 2005 for services as an educator and researcher in the field of information systems and for work in e-commerce in the agribusiness sector. She is a Fellow of the Australian Computer Society and a Fellow of the Association for Information Systems. She was a Senior Editor of MIS Quarterly 2008-2010 and is Editor-in-Chief of the Journal of the Association of Information Systems 2010-2013
Prof. Natalia Levina (New York University Stern School of Business)
Natalia Levina is an Associate Professor in the Information, Operations, and Management Sciences department at the Stern School of Business, New York University and a Visiting Full Professor of Information Systems at the Warwick Business School. Prof. Levina uses organizational theories to understand strategic and operational complexities involved in managing multi-party collaborative relationships focused on innovation. She investigates how diverse professional, organizational, and cultural backgrounds of project participants influence collaboration effectiveness and innovation on projects. Her current research focuses on open innovation, global sourcing, and crowdsourcing. She has been awarded an NSF grant for studying open innovation and crowdsourcing as well as Alfred P. Sloan Industry Studies Fellowship and IBM faculty fellowship for studying innovation management practices in global sourcing. Prof. Levina’s work has been published in numerous academic journals including among others Information Systems Research, MIS Quarterly, Journal of MIS, Decision Sciences, Organization Science, and Academy of Management Journal and received a number of awards from academic societies. She serves as a senior editor at Information Systems Research and as invited Senior Editor on a special issue of the European Journal of IS as well as invited Associate Editor at MIS Quarterly (2 special issues). She is an editorial board member of Information and Organizations and has been an editorial board member at Organization Science. She has served as an Associated Editor in ICIS conferences throughout the past ten years and is the incoming program co-chair for ICIS 2016 Dublin. She is the founding vice chair for the AIS Special Interest Group (SIG) on Grounded Theory Methods (GTM). She is also an executive board member of OCIS division of the Academy of Management. Prof. Levina’s teaching portfolio includes such courses as “Globalization, Open Innovation, and Crowdsourding,” (MBA and Exec MBA), “IT in Business and Society” (undergraduate), and “IT in Organizations” (PhD seminar). She has received her B.A. in Computer Science and Mathematics from Boston University, M.A. in Mathematics from Boston University, and Ph.D. in Information Technologies from the Massachusetts Institute of Technology, Sloan School of Management.
Dr. Andrea Censi (California Institute of Technology)
Andrea Censi is a postdoctoral scholar in Computing and Mathematical Sciences at the California Institute of Technology. He received the Laurea and Laurea Specialistica degrees (summa cum laude) in control engineering and robotics from Sapienza University of Rome, Italy, in 2005 and 2007, respectively, and a Ph.D. in Control & Dynamical Systems from the California Institute of Technology in 2012. He is broadly interested in perception and decision making problems for natural and artificial embodied agents, and in particular in estimation, filtering, and learning in robotics.
Prof. Nenad Medvidovic (University of Southern California)
Nenad Medvidović is a Professor and Associate Chair for Ph.D. Affairs in the Computer Science Department at the University of Southern California (USC). Between 2009 and 2013 Medvidović served as Director of USC’s Center for Systems and Software Engineering (CSSE). He was the Program Co-Chair of the 2011 International Conference on Software Engineering (ICSE 2011). Medvidović received his Ph.D. from the Department of Information and Computer Science at the University of California, Irvine. He is a recipient of the National Science Foundation CAREER (2000) award, the Okawa Foundation Research Grant (2005), the IBM Real-Time Innovation Award (2007), and the USC Mellon Mentoring Award (2010). He is a co-author of the ICSE 1998 paper titled “Architecture-Based Runtime Software Evolution”, which was recognized as that conference’s Most Influential Paper. Medvidović’s research interests are in the area of architecture-based development of distributed software-intensive systems. He is a co-author of a textbook on software architectures. Medvidović is a member of ACM, ACM SIGSOFT, IEEE, and IEEE Computer Society.
Prof. Craig Boutilier (University of Toronto)
Craig Boutilier is a Professor in the Department of Computer Science at the University of Toronto and co-founder of Granata Decision Systems. He received his Ph.D. in Computer Science from the University of Toronto in 1992, and worked as an Assistant and Associate Professor at the University of British Columbia from 1991 until his return to Toronto in 1999. He served as Chair of the Department of Computer Science at Toronto from 2004-2010. Boutilier was a consulting professor at Stanford University from 1998-2000, an adjunct professor at the University of British Columbia from 1999-2010, and a visiting professor at Brown University in 1998, at the University of Toronto in 1997-98, at Carnegie Mellon University in 2008-09, and at Université Paris-Dauphine (Paris IX) in the spring of 2011. He served on the Technical Advisory Board of CombineNet, Inc. from 2001 to 2010. Boutilier's research interests have spanned a wide range of topics, from knowledge representation, belief revision, default reasoning, and philosophical logic, to probabilistic reasoning, decision making under uncertainty, multiagent systems, and machine learning. His current research efforts focus on various aspects of decision making under uncertainty: preference elicitation, mechanism design, game theory and multiagent decision processes, economic models, social choice, computational advertising, Markov decision processes, reinforcement learning and probabilistic inference. He has published over 200 articles in refereed journals, conference proceedings, and other edited collections. He holds four patents (with six more pending). Boutilier is currently Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). He also serves as an Associate Editor with the ACM Transactions on Economics and Computation (TEAC); is a past Associate Editor with the Journal of Artificial Intelligence Research (JAIR), the Journal of Machine Learning Research (JMLR), and Autonomous Agents and Multiagent Systems (AAMAS); and he has sat on the editorial/advisory boards of several other journals. Boutilier has organized several international conferences and workshops, including his work as Program Chair of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000) and Program Chair of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09). He has also served on the conference program committees of over 40 leading international conferences. Boutilier is a Fellow of the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He has been awarded the Isaac Walton Killam Research Fellowship and an IBM Faculty Award. He also received the Killam Teaching Award from the University of British Columbia in 1997
Dr. Victor Pankratius (Massachusetts Institute of Technology)
Dr. Victor Pankratius' research embraces multicore parallel systems and software engineering. He advances automation in performance tuning, in debugging, and in empirical studies for programming language productivity and usability. He worked with industry partners such as Intel, Sun Labs, Oracle to identify research problems and advance multicore programming. Victor currently advances this field as a computer research scientist at the Massachusetts Institute of Technology, Haystack Observatory, and as a collaborative partner of the Computer Science and Artificial Intelligence Laboratory (CSAIL). Following his passion for space science and astronomy, Victor works with computer scientists, astronomers, geoscientists, and physicists to advance multicore in astroinformatics. Victor Pankratius earned a Habilitation degree in Computer Science from the Karlsruhe Institute of Technology and Doctorate in Economics and Business Engineering with distinction from the University of Karlsruhe. At the University of Muenster, he earned a Diplom (M.S.) in Business Computer Science best of class and a Bachelor of Science in Information Systems (BScIS).
Dr. Tudor Girba (CompuGroup Medical Schweiz)
Tudor Gîrba (http://tudorgirba.com) attained his PhD in 2005 from the University of Berne, and he now works as Innovation Lead at CompuGroup Medical Schweiz, and as software assessment consultant through netstyle.ch. Among others, since 2003 he leads the work on Moose, an extensive open-source platform for software and data analysis (http://moosetechnology.org). He is advocating that assessment must be recognized as a critical software engineering activity. He developed the humane assessment method (http://humane-assessment.com), and he is currently helping companies to rethink the way they manage complex software systems and data sets.