Prof Dr Stefanie Höhl


Getting attuned to others: Interpersonal synchrony and coordination in early human development

Caregiver-child interactions are characterized by interpersonal rhythms of different scales, from nursery rhymes and affective touch to daily routines. These rhythms make the environment more predictable for young children and enable interpersonal behavioral and physiological synchrony and attunement between caregiver and child. By using simultaneous measures of brain activities from caregiver and child, dual-EEG and dual-fNIRS, we can unravel the neural underpinnings of early interactional dynamics and their rhythmicity. I will present our research addressing factors critical to the establishment of caregiver-child synchrony, such as eye contact and interaction quality, especially behavioral reciprocity and contingency. I will also discuss some of the potential functions of interpersonal neural synchrony in early development, from social learning to effective cooperation and communication.


Stefanie Höhl is Professor of Developmental Psychology at the University of Vienna. Her areas of research interest are neural and behavioral synchrony in social interactions (EEG/ fNIRS-Hyperscanning); functionality of neural rhythms in early development; social learning and communication across development; and development of face, emotion, and gaze perception.

Prof Aude Billard


Towards reproducing humans’ dexterity and reactivity

Our homes, offices and urban surroundings are carefully built to be inhabited by us, humans. Tools and furniture are designed to be easily manipulated by the human hand. Floors and stairs are modeled for human-sized legs. For robots to work seamlessly in our environments they should have bodies that resemble in shape, size and strength to the human body, and use these with the same dexterity and reactivity.

This talk will provide an overview of techniques developed in our group to enable robust, fast and flexible manipulation. Learning is guided by human demonstrations. Robust manipulation is achieved through sampling over distributions of feasible grasps. Smooth exploration leverages on complete tactile sensing coverage and learned variable impedance strategies. Bi-manual coordination offers ways to exploit the entire robot’s workspace. Imprecise positioning and sensing is overcome using active compliant strategies, similar to that displayed by humans when facing situations with high uncertainty. The talk will also present examples in which robots can learn to manipulate objects that deforms as a result of being manipulated, such as cutting fruits and vegetables.


Aude Billard is Professor in the School of Engineering, École Polytechnique Fédérale de Lausanne. Her research interests span the control and design of robotic systems meant to interact with humans. To this goal, she pursue research in three complementary areas: a) the development of control systems for teaching robots through human demonstration; b) the study of the neural and cognitive processes underpinning imitation learning in humans; c) the design of user-friendly human-computer interfaces to facilitate human-robot interaction. She also conducts research on societal aspects of the use of robotics with application to diagnosis and therapy of children with autism. Her expertise lies in robot control, signal processing and machine learning, areas that are fundamental to her research and teaching.

Prof Hod Lipson


Automating discovery: From cognitive robotics to particle physics

Can machines discover scientific laws automatically? Despite the prevalence of big data, the process of distilling data into scientific laws has resisted automation. Particularly challenging are situations with small amounts of data that is difficult or expensive to collect. This talk will outline a series of recent research projects, starting with self-reflecting robotic systems, and ending with machines that can formulate hypotheses, design experiments, and interpret the results, to discover new scientific laws. We will see examples from psychology to cosmology, from classical physics to modern physics, from big science to small science.


Hod Lipsonis a professor of Engineering at Columbia University in New York, and a co-author of the award winning book “Fabricated: The New World of 3D printing”, and “Driverless: Intelligent cars and the road ahead”. His work on self-aware and self-replicating robots challenges conventional views of robotics, and his TED talk on self-aware machines is one of the most viewed presentations on AI. Lipson directs the Creative Machines Lab, which pioneers new ways to make machines that create, and machines that are creative. For more information visit

Prof Michael J. Frank


Clustering and generalization of abstract structures in human reinforcement learning.
Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers.  Previous computational models and data suggest that rather than learning about each individual context, humans build latent abstract structures and learn to link these structures to arbitrary contexts, facilitating generalization. In these models, task structures that are more popular across contexts are more likely to be revisited in new contexts. However, these models predict that structures are either re-used as a whole or created from scratch, prohibiting the ability to generalize constituent parts of learned structures. This contrasts with ecological settings, where some aspects of task structure, such as the transition function, will be shared between context separately from other aspects, such as the reward function. Here, we develop a novel non-parametric Bayesian agent that forms independent latent clusters for transition and reward functions that may have different popularity across contexts. We compare this agent to an agent that jointly clusters both across a range of task domains. We show that relative performance of the two agents depends on the statistics of the task domain, including the mutual information between transition and reward functions in the environment, and the stochasticity of the observations. We formalize our analysis through an information theoretic account and develop a meta learning agent that can dynamically arbitrate between strategies across task domains, and which best fits data from human learning and generalization experiments. We argue that this provides a first step in allowing for compositional structures in reinforcement learners, which should be provide a better model of human learning and additional flexibility for artificial agents.


Michael J. Frank, PhD is Edgar L Marston Professor of Cognitive, Linguistic & Psychological Sciences and Psychiatry and Human Behavior and is affiliated with the Carney Institute for Brain Science. He directs the Brown Initiative for Computation in Brain and Mind and the Laboratory for Neural Computation. He received his PhD in Neuroscience and Psychology in 2004 at the University of Colorado, following undergraduate and master’s degrees in electrical engineering and biomedicine (Queen’s University (Canada) and University of Colorado). 

Dr. Frank’s work focuses primarily on theoretical models of frontostriatal circuits and their modulation by dopamine, especially in terms of their cognitive functions and implications for neurological and psychiatric disorders. The models are tested and refined with experiments involving pharmacological manipulation, deep brain stimulation, EEG, fMRI and genetics. Honors include Kavli Fellow (2016), the Cognitive Neuroscience Society Young Investigator Award (2011), the Janet T Spence Award for early career transformative contributions (Association for Psychological Science, 2010) and the DG Marquis award for best paper published in Behavioral Neuroscience (2006). Dr Frank is a senior editor for eLife, associate editors for Behavioral Neuroscience and the Journal of Neuroscience, and member of Faculty of 1000 (Theoretical Neuroscience section).