Fall Term 2024

The colloquia are held in English and take place from 17:15 to 18:30 in room 2.A.01 at the Department of Informatics (IfI), Binzmühlestrasse 14, 8050 Zürich. 

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, 246 KB)

* If you would like to get access to the talk please send an email until 16:00 on the day of the talk to  studies@ifi.uzh.ch.

Newsletter IfI Colloquium
We announce our IfI Colloquium talk series every semester via email. If you want to subscribe to this mailing list please send an email to Karin Sigg.

26.09.2024 – Designing Human-Centered Programming Tools for Knowledge Exchange with Data and Code

Speaker: Prof. Dr. April Wang

Host: Prof. Dr. Alberto Bacchelli

Abstract

Effective knowledge exchange with data and code is crucial for enhancing collaboration among programmers in the workplace and for improving teaching and learning in programming classrooms. This exchange can be challenging due to issues such as synchronicity, interdisciplinary backgrounds, loss of context, abstract topics, and scalability. How can we design programming tools to facilitate effective knowledge exchange with data and code in both workplace and classroom settings? In this talk, I will introduce how our lab develops technology to support users in collaboratively exploring, understanding, and communicating in a data-centric world. We will discuss three key design aspects: (1) creating interactive techniques to capture connections between information entities, (2) generating visual elements to aid in the understanding of abstract concepts, and (3) designing infrastructure to support collaborative learning and collaborative exploration of data-related tasks.

Bio

April Wang is an assistant professor in the Department of Computer Science at ETH Zurich. She leads the Programming, Education, and Computer-Human Interaction Lab (PEACH). April is passionate about creating collaborative, intelligent, and human-centric programming environments that cater to evolving educational and professional needs. Her work has been published in top-tier HCI venues such as CHI, TOCHI, and CSCW, and she has received several paper awards. April completed her Ph.D. at the University of Michigan under the guidance of Professor Steve Oney and Professor Christopher Brooks.

03.10.2024 – A Hodgepodge of Visualization Research: Provenance, User Studies, Misinformation

Speaker:  Prof. Dr. Alexander Lex

Host: Prof. Dr. Jürgen Bernard

Abstract

In this talk I want to introduce three not particularly related research topics: provenance, user studies, and visualization-based misinformation.
In visualization, provenance is widely used for action recovery, to document analysis processes, and to analyze user behavior. I will focus on an exciting new application of provenance: to bridge between code-based and interactive, visual data analysis. While traditionally these two approaches can’t be easily combined, I’ll show how we can leverage provenance data to tackle these issues and design a truly integrated analysis environment.  

Next, I will introduce the reVISit framework for designing and running empirical studies online. Traditional survey tools limit the flexibility and reproducibility of online experiments. To remedy this, we introduce a domain-specific language, the reVISit Spec, that researchers can use to design complex online user studies. reVISit Spec, combined with the relevant stimuli, is compiled into a ready-to-deploy website that handles all aspects of a user study, including sophisticated provenance-based data tracking, randomization, etc. reVISit is a community focused project and ready to use! Visit https://revisit.dev/ to get started.

Finally, I will talk about data-driven misinformation in the form of charts shared on social networks. I will demonstrate that “lying with charts” doesn’t work the way we (used to) think about it, and introduce a few strategies to “protect” charts and charting tools from being abused by malicious users.

I will conclude by discussing how these topics mesh together after all, as (a) each project benefits from developments in the others, and (b) they all are enabled by my approach of combining engineering with visualization research.

Bio

Alexander Lex is an Associate Professor of Computer Science at the Scientific Computing and Imaging Institute and the Kahlert School of Computing at the University of Utah. He directs the Visualization Design Lab where he and his team develop visualization methods and systems to help solve today’s scientific problems. Recently he is working on visualization accessibility, visual misinformation, provenance and reproducibility, and user study infrastructure. He is the recipient of an NSF CAREER award and multiple best paper awards or best paper honorable mentions at IEEE VIS, ACM CHI, and other conferences. He also received a best dissertation award from his alma mater. He co-founded datavisyn, a startup company developing visualization solutions for the pharmaceutical industry.

14.11.2024 – Measuring, Predicting, and Improving the Performance of Software Systems

Speaker:  Prof. Dr. Philipp Leitner

Host: Prof. Dr. Harald Gall

Abstract

Why is it so hard to predict if a code change will improve (or reduce) the performance of a system? Why do even mature, IT-savvy companies such as  Meta, Mozilla, or MongoDB Inc. struggle to foresee performance regressions before rolling out a change? Why does it feel like all software is constantly getting slower, not faster? In this talk, I will talk about the minefield that is performance measurement and prediction. I will talk about the factors influencing software performance, the challenges of correctly measuring the performance of even simple code, and discuss why machine learning and large language models failed to provide the simple answers we were all hoping for.

Bio

Philipp is an Associate Professor of Software Engineering at Chalmers and the University of Gothenburg, Sweden, where he leads the Internet Computing and Emerging Technologies Lab (http://icet-lab.eu/). He is currently also the head of the Software Engineering 2 unit, consisting of 6 faculty and various doctoral students and postdocs across both universities. Philipp’s primary research interest is in software performance estimation and improvement, particularly in a web- and cloud development context. Moreover, Philipp is interested in empirical and experimental software engineering research, and generally in how to improve developer experience and productivity.

21.11.2024 – Knowledge Infused Learning

Speaker:  Prof. Dr. Michael Cochez

Host: Prof.  Abraham Bernstein, Ph.D.

Abstract

Recent advances in machine learning, especially with language models, have transformed many fields, dramatically raising expectations for artificial intelligence systems. Yet, these advances have also exposed a critical weakness: a lack of reliability. In contrast, traditional AI methods offer more dependable but less flexible solutions, often limiting their broad applicability.
In this talk, we will explore the intersection of these approaches. I'll introduce how graph-based knowledge can be used by machine learning systems, retaining both flexibility and robustness. I'll also share our latest research on inductive representation learning and neural graph reasoning, highlighting how these techniques address key challenges in AI today.

Bio

Dr. Michael Cochez is an assistant professor in the Learning and Reasoning group at the Vrije Universiteit Amsterdam and manager of the Discovery Lab (an ICAI lab in collaboration with Elsevier and the University of Amsterdam). He works on bridging the gap between knowledge graphs and machine learning. His research interests include embedding of knowledge graphs for downstream machine learning tasks, dealing with missing information in graphs (link prediction, approximate graph query answering) and applications such as question answering and recommendations. 

05.12.2024 – Exiting the Anthropocene:  From Today’s Anthropomorphic AI to Tomorrow’s Non-Anthropocentric “NaturAlien AI”

Speaker:  Prof. Dr. Martin Maier

Host: Prof.  Dr. Burkhard Stiller

Abstract

Humankind’s transformation of the planet has created its own chapter in Earth history – the “Anthropocene,” or the Human Age. It denotes the geological event when human activity started to become the dominating force with significant impact on the planet’s ecosystems and climate. Another impactful human-created force is artificial intelligence (AI) and its rapid progress. In a recent Science article, a large group of worldwide leading AI experts warned that unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or even extinction of humanity. They urged that for AI to be a boon, we must reorient technical AI research and development; pushing AI capabilities is not enough. In the future, the main benefits of AI will be that it is different than us humans in that it incorporates the many other ways in which natural intelligence operates, especially in light of the emergence of generative AI models that have barely scratched the surface of embodied data modalities such as biology. In this seminar, we explore the benefits of abandoning the path dependence and “lock-in” of today’s Turing-derived anthropomorphic (i.e., human-like) AI vision and its main focus on automation and optimization by imitating humans and replacing them with machines. To reorient Turing’s AI vision, we provide new directions for the creation of a novel type of non-anthropocentric (i.e., non-human-like) AI that expands into nature’s more-than-human “NaturAlien” intelligence free from human bias to help us escape from current anthropocentric thinking and evade our conceptual dead ends, e.g., climate change.

Bio

Prof. Dr. Martin Maier is a full professor with the Institut National de la Recherche Scientifique (INRS), Montréal, Canada. He was educated at the Technical University of Berlin, Germany, and received MSc and PhD degrees both with distinctions (summa cum laude) in 1998 and 2003, respectively. In 2003, he was a postdoc fellow at the Massachusetts Institute of Technology (MIT), Cambridge, MA. He was a visiting professor at Stanford University, Stanford, CA, 2006 through 2007. He was a co-recipient of the 2009 IEEE Communications Society Best Tutorial Paper Award. Further, he was a Marie Curie IIF Fellow of the European Commission from 2014 through 2015. In 2017, he received the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt (AvH) Foundation in recognition of his accomplishments in research on FiWi-enhanced mobile networks. In 2017, he was named one of the three most promising scientists in the category “Contribution to a better society” of the Marie Skłodowska-Curie Actions (MSCA) 2017 Prize Award of the European Commission. In 2019/2020, he held a UC3M-Banco de Santander Excellence Chair at Universidad Carlos III de Madrid (UC3M), Madrid, Spain. Recently, in December 2023, he was awarded with the 2023 Technical Achievement Award of the IEEE Communications Society (ComSoc) Tactile Internet Technical Committee for his contribution on 6G/Next G and the design of Metaverse concepts and architectures as well as the 2023 Outstanding Paper Award of the IEEE Computer Society Bio-Inspired Computing STC for his contribution on the symbiosis between INTERnet and Human BEING (INTERBEING). He is co-author of the book “Toward 6G: A New Era of Convergence” (Wiley-IEEE Press, January 2021) and author of the sequel “6G and Onward to Next G: The Road to the Multiverse” (Wiley-IEEE Press, February 2023).

19.12.2024 – Inductive Biases for Robot Reinforcement Learning

Speaker:  Prof. Jan Peters, Ph.D.

Host: Prof.  Davide Scaramuzza

Abstract

Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. To accomplish robot reinforcement learning learning from just few trials, the learning system can no longer explore all learn-able solutions but has to prioritize one solution over others – independent of the observed data. Such prioritization requires explicit or implicit assumptions, often called ‘induction biases’ in machine learning. Extrapolation to new robot learning tasks requires induction biases deeply rooted in general principles and domain knowledge from robotics, physics and control. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis, juggling and manipulation of various objects.

Bio

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt since 2011, and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) since 2022. He is also is a founding research faculty member of the Hessian Center for Artificial Intelligence. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the
INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed IEEE Fellow, in 2020 ELLIS fellow and in 2021 AAIA fellow.
Despite being a faculty member at TU Darmstadt only since 2011, Jan Peters has already nurtured a series of outstanding young researchers into successful careers. These include new faculty members at leading universities in the USA, Japan, Germany, Finland and Holland, postdoctoral scholars at top computer science departments (including MIT, CMU, and Berkeley) and young leaders at top AI companies (including Amazon, Boston Dynamics, Google and Facebook/Meta).
Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master's degrees in these disciplines as well as a Computer Science PhD from USC. Jan Peters has performed research in Germany at DLR, TU Munich and the Max Planck Institute for Biological Cybernetics (in addition to the institutions above), in Japan at the Advanced Telecommunication Research Center (ATR), at USC and at both NUS and Siemens Advanced Engineering in Singapore. He has led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007-2010) and Intelligent Systems (2010-2021).

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