Spring Term 2023

The colloquia are held in English and take place from 5.15 to 6.30 p.m. in room 2.A.01 at the Department of Informatics (IfI), Binzmühlestrasse 14, 8050 Zürich. After the talks there is time for discussions and to exchange ideas while enjoying refreshing beverages and munchies.

Visiting a colloquium is free of charge and does not require registration. 

Details about the format of the talk shall be checked always just ahead of a certain presentation date.

If you have further questions please contact Karin Sigg.
 

Flyer to download (PDF, 228 KB)

The IFI colloquium talks will be held at the date indicated below. They start at 5:15 p.m. or see time below date.

* If you would like to get access to the talk please send an email to  studies@ifi.uzh.ch

23.02.2023 – When Algorithms Are Your Boss: Algorithmic Control and the Future of Work

Speaker: Prof. Dr. Martin Wiener

Host: Prof. Dr. Gerhard Schwabe

Abstract

Control has been portrayed as management’s most fundamental problem. In this context, algorithmic control (AC) – broadly defined as the managerial use of intelligent algorithms and advanced digital technology as a means to align worker behaviors with organizational objectives – represents an emerging phenomenon. For example, enabling the effective scaling of operations, AC represents a key ingredient to the success of platform-based gig firms such as Uber. After a short introduction to the topic and review of core concepts, my talk will give an overview of our (ongoing) research on AC and discuss some (preliminary) empirical findings from different projects, with a particular focus on three questions: (1) How to conceptualize and measure AC? (2) How do (platform) workers experience AC? (3) How do they push back against AC? The talk will conclude with an outlook on the future of AC and related directions for further research.

Bio

Martin Wiener is a Chaired Professor of Information Systems and Business Engineering at TU Dresden. He is also an Affiliated Researcher at the Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg and the Stockholm School of Economics (SSE) Institute for Research. His current research focuses on algorithmic control/management and transparency, data-driven organizations, and digital transformation processes. His work has been published in leading IS journals, such as Information Systems Research, Journal of Management Information Systems, and MIS Quarterly. He is currently an Associate Editor for Business & Information Systems Engineering, Information Systems Journal, and Information Systems Research, as well on the Editorial Review Board of Information & Management. In 2022, he served as Program Co-Chair of the European Conference on Information Systems (ECIS) in Timișoara, Romania.

02.03.2023 – Reflections from Working on Visualisation with Industry Partners – and ahead

Speaker:  Dr. Johanna Schmidt

Host: Prof. Dr. Jürgen Bernard

Abstract

With the digitization of the manufacturing industry, energy supplies, and other domains, enormous amounts of IoT (Internet-of-Things) data are collected. Expectations regarding quality, costs, delivery time, durability, and environmental aspects are rising at a similar speed. Data-driven manufacturing and planning opens up unprecedented opportunities to understand the impact of decisions on engineering performance and customer satisfaction. Visual Analytics plays a very important role in turning data into actionable decisions, which more and more becomes a major part for companies for remaining competitive. Visual Analytics has already proven its usability in analyzing IoT data in various projects, as outlined in this talk. However, there are still some major challenges ahead of us. We are still facing Big Data problems when it comes to providing concise overview over large amounts of data. Further, there are many challenges attached to applying visualization successfully, both from the manufacturing industry and energy sector domains, and also from visualization research perspectives. In this talk I will reflect on our past experiences in applying Visual Analytics in research projects together with industry partners and describe the main challenges we are facing now.

Bio

Johanna received her master's degree in computer science in 2011. She continued with a PhD in data visualization at TU Wien, which she completed in 2016. Afterward, Johanna joined the AIT Austrian Institute of Technology GmbH as a Scientist. She was responsible for developing Visual Analytics solutions for large-scale mobility and trajectory data. In 2019, Johanna joined the VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH in Vienna, where she became the head of the Visual Analytics research group in 2020. She now leads a team of senior and junior researchers, research engineers, and students and interns. Johanna's primary research focuses on information visualization and visual analytics of large, multi-dimensional data. In this respect, she is especially interested in web-based, mobile, and progressive visualization solutions. Johanna teaches data visualization to bachelor's and master's students at TU Wien, the University of Applied Sciences in Salzburg, and the University of Applied Sciences in Krems.

09.03.2023 – Five Challenges for "AI for Code" -- And a Few Solutions

Speaker:  Prof. Vincent Hellendoorn, Ph.D.

Host: Prof. Dr. Alberto Bacchelli

Abstract

In the past few years, AI has powered a surge in new tools, such as GitHub's Copilot, that automate and simplify software development. This paradigm shift is driven by the adoption of Large Language Models, including OpenAI's Codex. Tools that are powered by these models hold the promise of automating many of the more tedious parts of software development, but also create new risks, such as generating vulnerable code that novice programmers are unable to debug. In this talk, I first describe the evolution of AI methods that has led to this point, explaining how these models work by discussing how we trained PolyCoder, the first multi-lingual open-source LLM of code. Then, I review the use-cases, strengths, and weaknesses of tools powered by these models by discussing the impact of five capabilities that current models lack: using the full development context, learning to edit code, working with the compiler, using programming resources, and working with developers. For each challenge, innovations have been proposed in recent work, including my own, that can help unlock further improvements and new applications. Overall, this area is poised to bring major changes to the software development process in the near future, but academic labs face growing hurdles contributing to this field due to the fast-rising cost of training and evaluating state-of-the-art models.

Bio

Prof. Hellendoorn’s research concerns all topics at the intersection of machine intelligence and software engineering research. His work leverages AI to power novel tools that support the many facets of the software development process, and identifies and addresses shortcomings that impact model success in practice. His work has been published at major conferences in both the AI and SE fields including ICSE, FSE, ASE, ICLR, and NeurIPS. He has worked as a visiting researcher at Google Brain and GitHub Next, and as a research consultant for Microsoft Research. He is currently an assistant professor at Carnegie Mellon University.

16.03.2023 – Optimizing Recursive Queries

Speaker:  Prof. Dan Suciu, Ph.D.

Host: Prof. Dr. Dan Olteanu

Abstract

Modern data analytics requires iteration, yet relational database engines are mostly optimized for non-recursive queries.  SQL supports only a limited form of recursion.  A better formalism for recursive queries is datalog, which has some elegant properties (recursion always terminates), and led to the development of two powerful optimizations techniques: semi-naive evaluation, and magic set rewriting.  But standard datalog is restricted to monotone queries over sets and does not support aggregates, which has limited its adoption.

In this talk I will describe a new approach to recursive queries and their optimization.  First, we extend datalog to semirings, while preserving some of the elegant properties of datalog, and also supporting aggregates naturally.  Then, I will describe a simple, yet very powerful optimization rule, called the FGH rule, that rewrites a recursive program into a different recursive program.  The rule captures many optimizations discussed in the literature, such as magic set optimization, the PreM rule, and semi-naive evaluation, and as well as new semantics optimizations.  Our implementation of the FGH rule is based on the egg term rewriting engine, and the Rosette program synthesizer.

Joint work with: Yisu Remy Wang, Mahmoud Abo Khamis, Hung Q. Ngo, Reinhard Pichler
 

Bio

Dan Suciu is a Microsoft Endowed Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Suciu is conducting research in data management, on topics such as query optimization, probabilistic data, data pricing, parallel data processing, data security. He is a co-author of two books Data on the Web: from Relations to Semistructured Data and XML, 1999, and Probabilistic Databases, 2011. He received the ACM SIGMOD Codd Innovation Award, received several best paper awards and test of time awards, and is a Fellow of the ACM. Suciu is currently an associate editor for the Journal of the ACM. Suciu's PhD students Gerome Miklau, Christopher Re and Paris Koutris received the ACM SIGMOD Best Dissertation Award in 2006, 2010, and 2016 respectively, and Nilesh Dalvi was a runner up in 2008.

04.05.2023 – User Preferences in Visualization-Based Choice-Making

Speaker:  Lena Cibulski

Host: Prof. Dr. Jürgen Bernard

Abstract

This talk provides different perspectives on using data visualization to leverage user preferences in making choices. We face many choices in our personal and professional lives. Computing has made it easy to compile large numbers of options to choose from. Identifying the best solution among such a set is called multi-attribute choice. With no objectively optimal solution present, our human judgment is needed to trade off conflicting goals. 
Data visualization is a powerful tool to help us explore and make sense of available courses of action. While many interactive visualizations already live in the context of decision-making, how to design for humans who make decisions with visualized data continues to be a vibrant research area. In this talk, I will touch upon different properties of multi-attribute choices including subjectivity. I will particularly address the role of user preferences and how they might be elicited, expressed, and considered in visualization-based choice support. This will include some usage scenarios where our visualizations helped users apply their preferences to balance the levels of performance that are achievable under different conditions.

Bio

Lena Cibulski is a visualization researcher at Fraunhofer IGD and a PhD candidate at Technical University of Darmstadt, Germany. She received her master’s degree in computer science in 2017 from Otto-von-Guericke University Magdeburg, where she soon found her way into visualization research. She completed her bachelor studies with a six-month stay at the VRVis Research Center in Vienna. Recently, she was a visiting researcher at JKU Linz. Her research is at the intersection between visualization and multi-attribute decision-making, with an emphasis on design studies for engineering applications. Lena conducts industrial and research projects that aim at assisting and informing decisions by using interactive visualization. She is particularly interested in multidisciplinary collaborations to encourage discussions on human factors, methodological aspects, and applications.

01.06.2023 – Troubling Visualization

Speaker:  Prof. Miriah Meyer, Ph.D.

Host: Prof. Dr. Jürgen Bernard

Abstract

Visualization is at an inflection point where the field is filled with increasingly diverse research interests and approaches, but where we are also struggling to make our tried-and-true approaches to research answer an increasingly complex range of questions. How do we consider people’s affective, emotional, and subjective relationships to data and visualization? How do we design novel visualizations in an increasingly complex and uncontrollable technology landscape? What are our ethical responsibilities to our collaborators, our participants, and each other? In this talk I’ll argue that it is time to trouble the foundational perspectives we hold around how we, as researchers, make sense of the world and design within it. I’ll talk about new perspectives we are working within the Vis Collective at Linköping University, and the myriad of research opportunities they are opening us to.

Bio

Miriah is a professor in the Division of Media & Information Technology at Linköping University, supported through the WASP program. Her research focuses on the design of visualization systems for helping people make sense of complex data, and on the development of methods for helping visualization designers make sense of the world. She obtained her bachelors degree in astronomy and astrophysics at Penn State University, earned a PhD in computer science from the University of Utah, and completed a postdoctoral fellowship at Harvard University. Prior to joining the faculty at LiU she was an associate professor in the School of Computing at the University of Utah and a member of the Vis Design Lab in the Scientific Computing & Imaging Institute.

Miriah has received numerous awards and recognitions for her work including being named a University of Utah Distinguished Alumni, both a TED Fellow and a PopTech Science Fellow, a Microsoft Research Faculty Fellow, and included on MIT Technology Review's TR35 list of the top young innovators. She was also awarded an AAAS Mass Media Fellowship that landed her a stint as a science writer for the Chicago Tribune.
 

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