Spring Term 2026

We are delighted to welcome a number of distinguished computer scientists to the upcoming IfI colloquium in the Spring Term FS 2026. We are looking forward to inspiring encounters with our guests, presenting topics from different areas of computer science.

The IfI colloquium is a free public event for researchers, students and the interested public and does not require registration. 

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

All the talks are held onsite.

If you have further questions please contact Karin Sigg.

Flyer to download (PDF, 426 KB)

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.

19.02.2026 – From Monologue to Dialogue: Remaking Public Service Communication with Celebrity-as-a-Service  

Speaker:  Prof. Dr. Alan Dennis

Host: Prof. Dr. Gerhard Schwabe

Abstract

Public service communication aims to raise awareness and change behaviors on critical public issues, such as health, safety, and the environment. Public service communication typically takes one of two forms: a public service announcement (PSA), such as a video or radio advertisement that broadcasts information (a monologue), or a public service interaction (PSI), such as a meeting with officials that both provides and collects information (a dialogue). Celebrity involvement has traditionally been limited to PSAs due to cost and scalability challenges, but advancements in AI now enable the use of digital humans as scalable, interactive "celebrity-as-a-service" solutions. These AI-controlled digital celebrities are digital twins of real people and enable one-on-one interactions with celebrities at scale. In an online experiment, we compared a digital version of Hugh Jackman to a non-celebrity digital human delivering a skin cancer awareness message in both a PSA video and a one-on-one PSI. Results showed that both perceived celebrity (celebrity vs. non-celebrity) and interactivity (PSI vs. PSA) increased trustworthiness and enjoyment. Trustworthiness, in turn, increased the intention to comply with the message, while both trustworthiness and enjoyment increased the likelihood of sharing the message. We conclude that celebrity-as-a-service by AI-controlled digital celebrities opens the door to incorporating celebrities into one-on-one PSIs to drive more effective, scalable, and cost-effective public service communication.

Bio

Alan Dennis is a Distinguished Professor of Information Systems and holds the John T. Chambers Chair of Internet Systems in the Kelley School of Business at Indiana University. He is ranked as the second most published Information Systems researcher over the last 30 years. His research has been reported in the popular press almost 1000 times. and a recent Standford study placed him among the top 2% most influential researchers across all scientific disciplines. He received the LEO Award, the IS field’s highest honor, in 2021.

26.02.2026 – Beyond Lines of Code: Measuring Complex Developer Behavior at Google

Speaker:  Sarah D'Angelo, PhD

Host: Prof. Dr. Thomas Fritz

Abstract

Understanding what makes software developers happy and productive at work is critical to providing a great developer experience and effectively integrating AI into development workflows. At Google, we take a mixed methods approach to measuring and understanding developer experience by leveraging logs data from our tools, conducting interviews and surveys with developers, and building metrics that help us understand behavior at scale. In this talk, I’ll share our approaches to measuring and understanding complex behavior such as flow, trust, and creativity and discuss how software development is changing with AI.

Bio

Sarah D’Angelo is a Senior Staff User Experience Researcher at Google. Her research focuses on understanding and measuring developer experience and enabling new ways of working with AI. Prior to Google, she received her Ph.D. in Technology and Social Behavior from Northwestern University and her B.S. in Cognitive Science from UC San Diego.

19.03.2026 – Cognitive Scaffolding in the Age of AI: Design Principles for Appropriate Reliance

Speaker:  Prof. Harmanpreet Kaur, PhD

Host: Prof.  Abraham Bernstein, PhD

Abstract

Human-AI partnerships are increasingly commonplace, yet often ineffective as people over- or under-rely on AI for support, resulting in harmful outcomes such as propagation of biases, missed edge cases, and homogenization of ideas and skillsets. My work follows the belief that for human-AI partnerships to be effective and reliable, AI should be a tool for thought—a cognitive scaffold that helps us appropriately and effectively reflect on the information we need—rather than displace human cognition. In this talk, I will first motivate this belief by sharing work that demonstrates the cognitive underpinnings of how people differently use and trust AI. Then, through use-cases spanning explainable AI, data science workflows, and scholarly research, I will present design principles for AI as an effective cognitive scaffold. For novices learning to work with AI systems, this means designing interfaces grounded in pedagogical principles, such as using narrative structures and progressive disclosure to build genuine understanding rather than superficial familiarity. For domain experts, effective scaffolding looks different: preserving agency and providing granular mechanisms for provenance to calibrate trust. I will conclude by examining a persistent challenge, that even well-designed scaffolds face systemic barriers of time pressure and competing cognitive demands in real-world contexts.

Bio

Harman Kaur is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota. Her research areas are human-centered AI, explainability and interpretability, and hybrid intelligence systems. She studies these areas in a variety of domains (e.g., exploratory data analysis, workplace wellbeing and productivity, knowledge search and sensemaking), applying methods towards both critically evaluating existing systems on meeting their intended goals, and designing and building new human-centered systems. She has published several papers at top-tier human-computer interaction venues, such as CHI, CSCW, and IUI. Her work has won Best Paper Honorable Mention awards at CHI and IUI. Harman received her PhD in both Computer Science and Information from the University of Michigan, and a BS in Computer Science from the University of Minnesota.

07.05.2026 – Human-Centric Machine Learning: Transparency, Explainability, and Guidance by Human Feedback

Speaker:  Prof. Dr. Matthias Zeppelzauer

Host: Prof.  Dr. Jürgen Bernard

Abstract

Many machine learning models in use today exhibit black-box behavior: their internal mechanisms and decision-making processes are not readily interpretable by humans. Moreover, these models are typically user-agnostic—despite being trained on vast amounts of data, they rarely account for the specific needs, goals, or constraints of human users. In this talk, I will present methodological advances from my group on human-centric machine learning, with a particular emphasis on (i) constructing transparent (white-box) classification models, (ii) developing explanation techniques for complex deep learning architectures, and (iii) incorporating user feedback and explanation-based supervision into training to steer model behavior, reduce bias, and improve alignment with user requirements. The talk will illustrate these ideas through a range of use cases, including natural language understanding, image classification, and medical time-series analysis.

Bio

Matthias Zeppelzauer is a professor and head of the Media Computing Research Group at the University of Applied Sciences St. Pölten in Austria. He received his PhD in Computer Science from TU Wien in 2011 with highest distinction. In 2020, he completed his habilitation at TU Wien in Computer Science on Retrieval of Multimodal Media Data. His research focuses on computer vision, machine learning, visual analytics, and multimedia information retrieval. Focus topics of his ongoing research include explainable and trustworthy machine learning, multimodal machine learning, collaborative machine learning as well as social media retrieval. He thereby pursues an interdisciplinary approach to research and investigates machine learning problems in fields such as the social sciences, medicine, biology, and in the humanities. Matthias was involved intensively in the acquisition and execution of numerous basic and applied research projects at national and international levels and has contributed to raising more than 9.8 million Euro of third-party funding. He is a lecturer for undergraduate and graduate programs and a mentor for younger researchers. He was awarded by the Austrian Computer Society for outstanding achievements in the area of pattern recognition as well as with the Austrian Open Source Award.

21.05.2026 – Drones and the Foundations of Political Authority

Speaker: 
 Prof. Dr. Francis Cheneval
Prof. Dr. Gerhard Schwabe
Prof. Dr. Mateusz Dolata

Host: Prof.  Dr. Gerhard Schwabe

Abstract

This talk presents the results of our multi-year research project combining philosophy and computer science. We examined how the use of automated or semi-automated drones by public executives, such as police forces and fire brigades, reshapes the understanding and exercise of public executives in democratic governance. While drawing on state-of-the-art philosophical theories of authority and related concepts, the comparative case studies of police and fire brigade drone programs analysed how executive actors justify drone use, how organisational frameworks structure their deployment, and how affected publics perceive these practices. The findings show that drones have the potential to increase the legitimacy of public authority by improving the epistemic quality as well as the efficiency in surveillance, search and rescue, and emergency response. At the same time, automated drones de-personalise the execution of authoritative behaviour and thereby raise fundamental questions about the connection of this behaviour to the sources of legitimate authority.

Bio

Francis Cheneval, Gerhard Schwabe and Mateusz Dolata jointly lead an SNF project on the topic of the talk. Francis Cheneval has been a full professor of political philosophy at UZH since 2011. His research focusses on Theory of Democracy, Property Rights, Normative Problems of European and Multilateral Integration, Justice and History of Political Thought. Gerhard Schwabe has been a full professor of Information Management at UZH since 2002. He is currently engaged in research on Generative AI applications, Human AI collaboration, human drone collaboration, and government as a platform. Mateusz Dolata has been a senior researcher at UZH until 2024 and since then a full professor of AI at Zeppelin University since then. In his research he explores the interaction between humans and AI in various areas of life and enable the best possible design of this interaction.

28.05.2026 – Robots Learning Through Interactions

Speaker: Prof. Dr. Jens Kober

Host: Prof.  Dr. Davide Scaramuzza

Abstract

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Complexity arises from interactions with their environment and humans, dealing with high-dimensional input data, non-linear dynamics in general and contacts in particular, multiple reference frames, and variability in objects, environments, tasks, and human behavior. A human teacher is always involved in the learning process, either directly (providing data) or indirectly (designing the optimization criterion), which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective? In this talk I’ll argue that there are tremendous benefits in having a human teacher intermittently interact with a robot also while it is learning. I will discuss various methods we have developed in the fields of supervised learning, imitation learning, reinforcement learning, and interactive learning. All these concepts will be illustrated with benchmark tasks and real robot experiments ranging from fun (ball-in-a-cup) to more applied (retail environments).

Bio

Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, the 2022 RSS Early Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.

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