Author: Philipp Wicke

Lecturer: Kerem Şenel
Fields: Artificial Intelligence, Machine Learning, Natural Language Processing

Content

This hands-on course takes you from transformer fundamentals to cutting-edge agentic AI systems. You’ll learn how large language models work under the hood, train and fine-tune your own models, and build autonomous agents that use tools, access external resources, and collaborate to solve complex tasks. Each session combines theory with practical implementation using industry-standard frameworks like Hugging Face, LangChain, and the Model Context Protocol.

Session 1: Foundations & Architecture
Understanding Transformers: The Engine Behind Modern AI

Discover how LLMs predict and generate text through self-attention mechanisms. We’ll demystify the transformer architecture—from tokenization to embeddings—and you’ll implement a tokenizer from scratch while visualizing how models “pay attention” to different parts of text.

Session 2: Training Fundamentals
Training Your Own Language Model

Learn what it takes to train an LLM: pre-training objectives, dataset curation, and scaling laws. You’ll fine-tune a real model (GPT-2 or TinyLlama) on custom data using Hugging Face tools, understanding the trade-offs between model size, compute, and performance.

Session 3: Advanced Training & Alignment
Making Models Helpful: Instruction Tuning & RLHF

Explore how base models are transformed into helpful assistants through instruction tuning and alignment techniques like RLHF. You’ll fine-tune a model to follow instructions and experiment with advanced prompting techniques including chain-of-thought reasoning.

Session 4: Cutting-Edge Applications & Agentic Systems
Autonomous AI: Building Agents That Think and Act

Go beyond chatbots to autonomous agents that use tools, access databases, and collaborate on complex tasks. You’ll implement function calling, integrate the Model Context Protocol (MCP) to connect LLMs with external resources, and build multi-agent systems using LangGraph—culminating in an autonomous agent demo.

Lecturer

Kerem Şenel received his PhD in Computer Science from LMU Munich in 2025, specializing in Natural Language Processing. During his doctoral research at the Center for Information and Language Processing (CIS), he explored diverse topics including interpretability, multilinguality, and evaluation of language models. He currently works as an IT consultant in industry, specializing in AI applications and solutions.

Affiliation: TNG Technology Consulting
Homepage: https://www.tngtech.com/en/

Lecturer: Léa Pillette
Fields: Brain-Computer Interface, Neurofeedback, Virtual Reality, Human-Computer Interaction, Neurology, Computer Science

Content

This course provides a comprehensive overview of how Virtual Reality (VR) intersect with Brain-Computer Interfaces (BCIs), which are neurotechnologies that introduce promising possibilities to interact with digital devices solely through the acquisition and analysis of brain activity, typically measured using electroencephalography.

BCIs enhance VR applications in two key ways: by enabling direct control of virtual elements through mental commands, such as imagining hand movements to guide a virtual character, and by gathering real-time neural data to adapt and personalize the VR experience to the user\’s cognitive and emotional state. Conversely, VR platforms offer immersive environments that facilitate BCI user training and rehabilitation, creating tailored scenarios that improve brain activity modulation and learning outcomes.

The course is structured into four sessions: the first two cover the mutual benefits of integrating BCI with VR technologies. The final two sessions will focus on therapies that utilize VR-based and BCI-based approaches independently, as well as innovative interventions at the intersection of both technologies. These combined VR-BCI therapies harness neurofeedback, and immersive environments to promote functional recovery, for instance in motor rehabilitation after stroke. This integrated approach provides patient-centered, adaptable, and motivating rehabilitation protocols that leverage real-time brain activity monitoring to enhance neuroplasticity and clinical outcomes.

Session 1 and 2: mutual benefits of integrating BCI with VR technologies
Sessions 3 and 4: state of the art on VR and BCI-based therapies

Literature

• • Drigas, A., & Sideraki, A. (2024). Brain neuroplasticity leveraging virtual reality and brain–computer interface technologies. Sensors, 24(17), 5725.
• • Kober, S. E., Wood, G., & Berger, L. M. (2024). Controlling virtual reality with brain signals: state of the art of using VR-based feedback in neurofeedback applications. Applied psychophysiology and biofeedback, 1-20.
• • Lotte, F., Faller, J., Guger, C., Renard, Y., Pfurtscheller, G., Lécuyer, A., & Leeb, R. (2012). Combining BCI with virtual reality: towards new applications and improved BCI. In Towards practical brain-computer interfaces: Bridging the gap from research to real-world applications (pp. 197-220). Berlin, Heidelberg: Springer Berlin Heidelberg.
• • Roc, A., Pillette, L., Mladenovic, J., Benaroch, C., N’Kaoua, B., Jeunet, C., & Lotte, F. (2021). A review of user training methods in brain computer interfaces based on mental tasks. Journal of Neural Engineering, 18(1), 011002.

Lecturer

Dr. Léa Pillette is a CNRS researcher and member of the Seamless team at IRISA, Rennes, France, since 2022. She obtained her PhD in computer science from the University of Bordeaux in 2019. Her research focuses on developing innovative methods to train individuals to regulate their brain activity, enabling more accessible and effective use of brain-computer interfaces for applications such as medical interventions and virtual world interactions.

Affiliation: Univ. Rennes, Inria, CNRS, IRISA, Rennes, France
Homepage: https://lea-pillette.ovh/

Lecturer: Tim Tiedemann
Fields: Robotics, Machine Learning

Content

This course will show a bridge between machine learning and robotics — which does not use ML to solve robotics’ tasks but the other way around! Further, the course tries to show where using robots could be a method to help finding biological or cognitive psychological insights.

– Session 1 will give an introduction first to bio-robotics, and afterwards to robotics in general — including robotic software frameworks and potential starting points for own robotic experiments. Here, examples are shown where biology gained new insights from robotics (and the other way around, too).

– Session 2 will continue the introduction to robotics and starts the introduction to active learning with methods that are NOT active learning (but that could help solving problems you have with large data sets you need to label…).

– Session 3 will continue on active learning and combine both, robotics and active learning. We will also talk about the idea of embodiment, here.

Literature

• Siciliano et al. (Eds.) (2016, 2024): Handbook of Robotics. Springer
• Burr Settles: Active Learning, Morgan & Claypool Publishers, 2012
• Robert (Munro) Monarch: Human-in-the-Loop Machine Learning : Active Learning and Annotation for Human-Centered AI, Shelter Island, NY: Manning, 2021

Lecturer

Since 2016: Professor of Intelligent Sensing at the University of Applied Sciences Hamburg (HAW Hamburg, Hamburg, Germany). Main research interests are sensors and sensor data processing (including machine learning methods) and robotics. 2010-2016 Postdoc in the area of space and underwater robotics at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany. 2009 Ph.D. in biorobotics, focus on the transfer of (neuro-) biological concepts to the robotic domain 2003-2010 Research assistant at the Computer Engineering Group, Bielefeld University 2003 Research assistant at the Cognitive Psychology Group, Bielefeld University 2003 Diploma in computer science (i.e. Master degree level), study computer science with a focus on robotics and neural networks at Bielefeld University, Bielefeld, Germany

Affiliation: HAW Hamburg

Lecturer: Sven Mayer
Fields: Artificial Intelligence, Human-Computer Interaction, Human-AI Interaction

Content

The course Introduction to Intelligent User Interfaces (IUI) introduces participants to key concepts at the intersection of Human-Computer Interaction (HCI) and Artificial Intelligence (AI). It explores how methods from Machine Learning and AI can be transferred to the design of interactive systems that act intelligently, adapt to users, and support human goals. Emphasis is placed on a human-centered perspective that prioritizes usability, transparency, and user trust. Across four sessions, participants will gain a conceptual understanding of the foundations, design principles, and open challenges of intelligent user interfaces, preparing them to critically assess and discuss current and future developments in this field.
* 1. Session: Motivation and Introduction
* 2. Session: Machine Learning and Human-Computer Interaction basics
* 3. Session: Designing, Building, and Evaluating Human-AI Systems
* 4. Session: Human-Centered Challenges and Future Directions

Literature

• Andy Field and Graham Hole (2002). How to Design and Report Experiments
• Kasper Hornbæk, Per-Ola Kristensson, and Antti Oulasvirta (2025). Introduction to Human-Computer Interaction. Oxford University Press.
• Course: Intelligent User Interfaces, https://iui-lecture.org/
• Course: Practical Machine Learning, https://sven-mayer.com/pml/
• Course: Human-Computer Interaction, https://hci-lecture.org/

Lecturer

Sven Mayer is a full professor of computer science at the TU Dortmund University (Germany) and the Research Center Trustworthy Data Science and Security, where he is the head of the chair for Human-AI Interaction. His research focuses on Human-AI Interaction at the intersection between Human-Computer Interaction and Artificial Intelligence, where he focuses on the next generation of computing systems. He uses artificial intelligence to design, build, and evaluate future human-centered interfaces. In particular, he envisions enabling humans to outperform their performance in collaboration with the machine. He focuses on areas such as augmented and virtual reality, mobile scenarios, and robotics.

Affiliation: TU Dortmund University
Homepage: https://sven-mayer.com/

Lecturer: Katharina Krämer, Sophia Reul, Annekatrin Vetter
Fields: Psychology, Psychotherapy

Content

Where do apparently opposite qualities of experience show up in our inner lives? How do thinking and feeling fit together — and where don’t they? What shapes the connection between body and mind? How do we experience the outer world, and what do we experience within — and how can these be linked and integrated? What stays unconscious, and what becomes conscious?
These and similar questions are at the heart of our self-experience workshop. Based on experiential exercises drawing from psychoanalysis, humanistic psychology, and body-oriented approaches, participants are invited into a reflective space to explore self-awareness, perception, and communication. No prior knowledge is required — all you need is a little curiosity and a willingness to gently step beyond the edge of your comfort zone. Then self-experience can become a bridge to new realities of being and relating.

Session 1: Bridging Self and Others
Session 2: Bridging Bodys
Session 3: Bridging Emotions
Session 4: Bridging Distance

Lecturer

Katharina Krämer is a psychologist and analytic psychotherapist. She works as a professor for psychology at the Rheinische Hochschule Köln, University of Applied Sciences, Cologne, Germany, and as a lecturer and supervisor for psychotherapists in training. Additionally, she works as a psychotherapist in private practice. In 2014, Katharina Krämer received her doctoral degree from the University of Cologne, Germany, on a thesis investigating the perception of dynamic nonverbal cues in cross-cultural psychology and high-functioning autism. Her research interests include the application of Mentalization-Based Group-Therapy with patients with autism and the vocational integration of patients with autism.

Affiliation: Rheinische Hochschule Köln, University of Applied Sciences
Homepage: https://rh-koeln.de/dozierende/katharina-krmer

Sophia Reul is a clinical psychologist and analytic psychotherapist. She works as a psychotherapist in private practice. In 2021, she received her doctoral degree from the Westfälische Wilhelms-University Münster, Germany, on a thesis investigating the impact of neuropsychological methods in diagnoses of early dementia. Today, her research interests include the application of Mentalization-Based Group-Therapy (MBT-G) with patients with autism.

Affiliation: Praxis für Psychotherapie und Psychoanalyse Sophia Reul, Kirchweidach (Bay.)

👤

Annekatrin Vetter is a clinical psychologist and analytic psychotherapist. As a psychotherapist, she treats patients with different mental disorders in private practice. Additionally, she works as a lecturer and supervisor for psychotherapists in training and as a trainer for Coaches at Inscape – Coaching & Counselling, Cologne, Germany.

Affiliation: Praxis für Psychotherapie und Psychoanalyse, Supervision und Coaching Annekatrin Vetter, Cologne

Lecturer: Marius Klug
Fields: Neuroscience, Psychology

Content

This course provides an introduction to psychophysiology — the study of how psychological phenomena are expressed in, and can be revealed through, physiological signals. Starting from the historical roots of the field and the basic anatomy of the nervous system, the course builds up to the practical measurement and analysis of peripheral and central physiological signals. Participants will learn how biosignals are recorded, digitized, and processed, how to design psychophysiological experiments that avoid common pitfalls, and how specific measures — electromyography, electrodermal activity, electrocardiography, and electroencephalography — relate to psychological states. No prior background in physiology or signal processing is assumed.

Session 1: Introduction, Nervous System, and Measurement.
From the earliest attempts to diagnose lovesickness by pulse to Hans Berger\’s first human EEG recording, this session traces how psychophysiology became a scientific discipline. It then covers the structural and functional organization of the nervous system and the anatomy of the brain relevant to psychophysiological measures. The second half introduces the fundamentals of biosignal measurement: electrode types and placement, bipolar versus unipolar recording, digitization, spectral analysis, and digital filtering.

Session 2: Psychophysiological Experimentation.
This session teaches you to think like an experimental scientist. It covers experimental design as it applies specifically to psychophysiology. Starting from general principles — variables, operationalization, confounds — it addresses the particular challenges of psychophysiological inference, where the independent variable is a psychological state and the dependent variable a physiological measure. Topics include within- versus between-subjects designs and their trade-offs, serial dependency, continuous versus event-related analysis, epoching and ERP averaging, and the identification and control of external and internal artifacts.

Session 3: Peripheral Physiology (EMG, EDA, ECG).
This session covers three peripheral psychophysiological measures. Electromyography (EMG): the physiology of motor units, recording of surface EMG, and EMG signal processing. Electrodermal activity (EDA): eccrine sweat gland physiology and innervation, skin conductance measurement, and the distinction between tonic skin conductance level and phasic skin conductance responses. Electrocardiography (ECG): cardiac physiology, the ECG waveform, and the extraction of heart rate and heart rate variability, including spectral HRV analysis as a window into sympathetic–parasympathetic balance.

Session 4: Electroencephalography (EEG).
This session covers the physiological origins of EEG, EEG electrode placement and measurement, and the major analysis approaches: frequency domain analysis (spectral power in canonical bands such as alpha, theta, and mu, with examples from motor imagery and workload paradigms), time domain analysis (event-related potentials, epoching, difference waves, and topographic mapping), time-frequency analysis (event-related spectral perturbation), and time-time analysis (ERP images).

Literature

• Gramann, K., & Schandry, R. (2009). Psychophysiologie (4th ed.). Basel, Switzerland: Beltz.

Lecturer

Marius Klug studied cognitive science in Tübingen and was already in contact with EEG as a measurement method and brain-computer interface during that time. He subsequently earned his doctorate in the field of mobile brain research under Prof. Klaus Gramann at TU Berlin. There, he extensively dealt with EEG analysis methods and virtual reality as an experimental method. Specifically, the application of EEG in a mobile context, the cleaning of data, and their interpretation in conjunction with other measurements, such as body and eye movements, were the focus of the research. The continuation of this research can now be found at BTU in the form of the practical use of psychophysiological measurement methods as an interface for real-time applications.

Affiliation: BTU Cottbus-Senftenberg

Lecturer: Herbert Jaeger
Fields: all IK disciplines and some more

Content

In this talk I want to explain my love affair with a scientific field that has no name – yet. Or rather, it has too many names. The current most popular branding is ‘neuromorphic computing’. However, when you hear ‘natural computing’, ‘in-materio-computing’, ‘physical computing’, ‘unconventional computing’, ‘non-digital computing’, ‘fluent computing’ (I have a list of twenty more), it might mean the same thing – or something else. The idea behind all of this is to engineer novel kinds of microchips to get novel kinds of ‘computing’ directly out of nanoscale physics, just like real brains pull their cognition magic from neuronal biophysics, without the detour of digital simulation of neural dynamics. Current big-scale funding for this sort of research thrives on the hope to replace Gigawatt AI server farms with artificial microbrains that burn only 20 Watts, like our brains. And as a bonus, to get implantable neuro-implants that need no batteries, or principally un-hackable computing machines, or robustly self-adapting edge computing. Super fascinating. Alas, nobody seems to have a clue how to do it – yet.

Literature

• H. Jaeger, B. Noheda, W.G. van der Wiel (2023): Toward a formal theory for computing machines made out of whatever physics offers. Nature Communications 14, 4911 (or the long version, 70 pages: https://arxiv.org/abs/2307.15408)

Lecturer

Herbert Jaeger studied mathematics and psychology in Freiburg (Germany), got his PhD in Computer Science / AI in Bielefeld (Germany) and then did a postdoc fellowship at the (then) German National Research Institute for Mathematics and Computer Science (GMD) in Sankt Augustin (Germany), where he subsequently founded the research unit on modeling intelligent dynamical sytsems (MINDS); then from 2001 to 2019 he served as professor of Computing Science at Jacobs University Bremen (Germany). Since 2019 he has been Professor for Computing in Cognitive Materials at the University of Groningen. Current research focus: mathematical foundations for computing in non-digital physical substrates. Jaeger retired 2025 and now has almost enough time for thinking. With only one exception (stupid flu), he attended all IKs, and served on various functions for the IK community since the beginning in 1997.

Affiliation: University of Groningen
Homepage: https://www.ai.rug.nl/minds/

Lecturer: Timothy Drysdale
Fields: Artificial intelligence, education, practical work

Content

This evening talk will reflect on the challenge facing educators, particularly younger educators with many years of teaching ahead of them. The joint pressure of readily-available artificial intelligence affecting the validity of traditional processes, and massification of education reducing the resources available per student, pose a difficult pinch point that is generating demand for authentic, interactive activities but placing a lot of pressure on the available time and space for students to experiment with real equipment in a traditional manner. I\’ll introduce a solution in the form of laboratories in a box, which we have been doing doing at the University of Edinburgh for a number of years, and describe the elements that make these successful for us, how you can adopt a similar approach, the pitfalls to avoid and some fruitful future directions for our communities of educators to explore, in particular in expanding what we do with the data streams to support better learning and in taking our concept of experiments beyond what we are used to doing in traditional laboratories.

Literature

• Reid, D., & Drysdale, T. (2024). Student-facing learning analytics dashboard for remote lab practical work. IEEE Transactions on Learning Technologies, 17, 1037-1050. https://doi.org/10.1109/TLT.2024.3354128
• D.Reid, J. Burridge, D. Lowe, T. Drysdale “Open-source remote laboratory experiments for controls engineering education,” International Journal of Mechanical Engineering Education. February 2022. doi:10.1177/03064190221081451
• T. D. Drysdale, S. Kelley, A.-M. Scott, V. Dishon, A. Weightman, R. J. Lewis & S. Watts “Opinion piece: non-traditional practical work for traditional campuses,” Higher Education Pedagogies, 5:1, 210-222, 2020, DOI: 10.1080/23752696.2020.1816845
• G. L. Knight & T. D. Drysdale The future of higher education (HE) hangs on innovating our assessment – but are we ready, willing and able?, Higher Education Pedagogies, 5:1, 57-60, 2020, DOI: 10.1080/23752696.2020.1771610

Lecturer

Prof Timothy Drysdale is the Chair of Technology Enhanced Science Education and Director of Strategic Digital Education in the School of Engineering. His main research activity is in Engineering Education, where he leads the Remote Laboratories group. He and his team have developed an entirely new infrastructure and approach for operating online remote laboratories on traditional campuses (practable.io), winning international awards from the Global Online Laboratories Consortium (Remote Experiment Award 2024) and the Association for Learning Technology / Jisc Award for Digital Transformation in 2023. His prior research activities were in the area of terahertz component design and testing, microwave antennas, and optical plasmonics. He has a long-standing involvement with public outreach in science and engineering, including the Royal Society Summer Science Exhibition, Science Day at Buckingham Palace, and giving the Isambard Kingdom Brunel Award Lecture at the British Science Festival.

Affiliation: University of Edinburgh
Homepage: https://eng.ed.ac.uk/about/people/professor-timothy-drysdale

Lecturer: Jutta Kretzberg, Katja Hellekes
Fields: Personal / professional development

Content

Are you a student? Have you ever considered doing a PhD? Or a career in academia?
Does the idea of doing a PhD appeal to you? Or does it seem like hard work, or even a painful experience?
Many Master’s students struggle with the decision of whether a PhD would be the right choice for their career. In fact, a significant proportion of PhD students continue to question their decision until they graduate, and sometimes even afterwards.
There is no general advice on who should pursue a PhD. Whether to pursue a PhD is a personal decision that depends on factors such as your personality, personal situation, and the job opportunities available. The aim of this workshop is to help you develop a clearer personal perspective on this decision.

This workshop is primarily aimed at Master’s and advanced Bachelor’s students. However, the method of developing your personal perspective can also be applied to future career steps. PhD students, PhD holders and non-PhDs who are willing to share their perspectives are highly welcome!

Session 1: Background information
In the first session, we will begin by providing some background information on undertaking a PhD in Germany or Austria. What are the motivations for pursuing a PhD? What skills are gained through a PhD? How can a PhD be structured and funded? How do PhDs differ between disciplines and countries?

Session 2: External perspectives
In the second session, we will explore the different stakeholders’ perspectives interactively. What do Master’s students expect from a PhD? What do PhD supervisors expect from their students? What do employers expect from PhD versus Master’s degree applicants? What about the perspective of family and friends? And which personality traits might be useful for pursuing a PhD?

Session 3: Your personal perspective
During the third session, you will write down your hopes, neutral expectations and fears relating to a PhD. Working with a fellow participant, categorize these into the groups: ‘tasks/skills’, ‘topics/scientific questions’, ‘working environment’ and ‘personal factors’. Sharing your thoughts and listening to those of your teammate can help you gain a clearer perspective on your career decisions.

Session 4: How to become a PhD candidate?
After sharing our conclusions from the previous sessions, we will discuss the practical steps involved in becoming a PhD candidate, such as: How do you choose a topic? How do you find a project and a supervisor? How can you finance the PhD? We will also consider how to balance the demands of your PhD with your personal life – bridging realities of your live and a PhD.

Literature

• The European Competence Framework for Researchers: https://research-and-innovation.ec.europa.eu/document/download/7da29338-37bf-4d51-b5eb-a1571b84c7ad_en?filename=ec_rtd_research-competence-presentation.pdf
• General information on PhD scholarships (by German Government): https://www.bmbf.de/EN/Research/ScienceSystem/AcademicCareers/DoctoralScholarships/doctoralscholarships_node.html
• General information on German academic system & funding for international exchange (DAAD): https://www.daad.de/en/
• Largest scholarship organisation in Germany: https://www.studienstiftung.de/en/doctoral-scholarships/doctoral-scholarships
• Chris Woolston: “Graduate survey: A love-hurt relationship” Nature 550, 549-552 (2017)
• https://www.nature.com/articles/nj7677-549a https://doi.org/10.1038/nj7677-549a (Nature’s survey of more than 5,700 doctoral students worldwide)
• Lars Kiewidt, PhD: “To PhD or not to PhD?” (2019) https://medium.com/age-of-awareness/to-phd-or-not-to-phd-4312cdb862c5 (Evaluation of this survey data set concerning PhD student’s motivation, skills and satisfaction across fields in natural sciences.)
• Chris Woolston: ‘I don’t want this kind of life’: graduate students question career options
• Nature 611, 413-416 (2022) doi: https://doi.org/10.1038/d41586-022-03586-8 (Newest version of nature’s PhD survey, but not open access)
• Katie Mitzelfelt, PhD: “To Be or Not To Be a PhD Candidate, That Is the Question” (Association for Women in Science Magazine, 2021): https://awis.org/to-phd-or-not-phd/ (Individual perspectives of 6 persons on their own decision to be or not to be a PhD.)
• Charlotte King_: “To PhD or not to PhD, that is the question…” https://www.postgrad.com/blog/to-phd-or-not-to-phd/ (Rather old, but still helpful blog post)

Lecturer

Jutta Kretzberg is professor for Computational Neuroscience and head of the MSc program Neuroscience at University of Oldenburg. She studied applied computer science and biology at University of Bielefeld, where she also did her PhD in Biology. After being a postdoc (and having a baby) in San Diego, California, she came back to Germany to be a junior professor and became a professor some years (and another baby) later. Nowadays, while juggling her family, teaching, research and administration duties, her favorite task is mentoring.

Affiliation: University of Oldenburg
Homepage: https://uol.de/en/neurosciences/compneuro

Katja Hellekes, is an experienced academic professional and Coordinator of the Vienna Doctoral School Cognition, Behavior and Neuroscience. She completed her diploma and doctorate at the University of Cologne, specializing in Neurobiology, followed by postdoctoral research at the Institute of Molecular Pathology (IMP) and the University of Freiburg. Alongside her role as Coordinator of the Doctoral Program, Katja Hellekes lectures in cognitive science. With a passion for fostering the growth of early-career researchers, she provides dedicated support to PhD candidates, guiding them through their doctoral journey and helping them transition into independent research roles.

Affiliation: University Vienna
Homepage: https://vds-cobene.univie.ac.at/

Lecturer: Jennifer Fewell
Fields: Biology; Collective Behavior

Content

This course will explore the social organization and collective behavior of social insects from a biological perspective. The social insects are models for coordination and cooperation across small to large scales. Their distributed communication systems have been used extensively as inspiration for applied questions in coordination and collective behavior, from supply chains to robotics and beyond. Is a social insect colony the original collective “AI”? – well probably not, but it will be a fun question to explore!

Lecturer

Jennifer Fewell is a President’s Professor at Arizona State University, where she served as the founding Director of the Center for Social Dynamics and Complexity. She has served also as President of the Animal Behavior Society and the International Union for the Study of Social Insects. She studies social organization and division of labor in social insects, and mechanisms and evolution of social cooperation across a range of species. She received her MS and PhD from the University of Colorado.

Affiliation: Arizona State University