Optimalisatie is een veelvoorkomend proces is een groot aantal disciplines.
Het doel van deze serendipity sessie is om onderzoekers uit verschillende vakgebieden met elkaar in gesprek te laten gaan over verschillende methodes, en daarmee interdisciplinaire samenwerking te stimuleren.
Wanneer: Dinsdag 24 maart 2026
Waar: De Salon, Huize Heyendael
Tijdschema:
10:00 - 10:10 Welkom en introductie
10:10 - 10:30 Eerste presentatie
10:30 - 10:40 Discussie
11:40 - 11:00 Tweede presentatie
11:00 - 11:10 Discussie
11:10 - 11:30 Pauze
11:30 - 11:50 Derde presentatie
11:50 - 12:00 Discussie
12:00 - 13:00 Lunch
Registratie: Registratie via het onderstaande formulier is verplicht en is mogelijk tot 17 maart. Het aantal plaatsen is beperkt, schrijf je daarom snel in, zodat je verzekerd bent van een plek.
Programma: (session will be in English)
Speaker: Marc Slors (Professor Philosophy of Mind and Language)
Title: Stylistic cultural conventions: a synthetic philosophical approach
Abstract: For a long time critical analysis of concepts was supposed to be the core business of philosophy. Philosophers such as Eric Schliesser and Catharina Dutilh-Novaes have recently pleaded for another, complementary function of philosophy: integrating knowledge from various sciences to create a comprehensive, unified understanding of complex systems. They call this 'synthetic philosophy.' In this presentation I will present a project I have been working on for the past 6 years as an example of this approach. I will argue that sociological insights into what Bourdieu called our 'habitus', anthropological insights into culture shock, and psychological insights into 'behaviour settings' can be combined with analytical philosophy of conventions and with theorizing about cultural evolution to explain our thorough dependence on what I will call 'stylistic cultural conventions': etiquette, dress-codes, behavioural codes, architecture and the shape and design of public space. This explanation, I will argue, sheds new light on cultural frictions, for example in multicultural societies. If correct, it shows that our current way of dealing with such frictions is seriously flawed.
Speaker: Alan Sanfey (Professor Decision Neuroscience Laboratory)
Title: A decision neuroscience approach to studying human choice
Abstract: Our lives consist of a constant stream of decisions, from the mundane to the highly consequential. These decisions range from those with clearly defined probabilities and outcomes, such as how much risk to take with an investment, to those where one's outcomes depend on the choices of others, for example when playing a competitive game with an opponent. Here, I will outline our group's approach in exploring these questions, where we utilize a novel methodology to study both individual and interactive decision-making by combining the methods of behavioural experiments, functional brain imaging, and formal economic models. Examining sophisticated high-level decision behaviour at neural and computational levels can provide important clues as to the fundamental mechanisms by which decision-making operates. For example, we demonstrate that this approach reveals key motivations, other than simple economic gain, that guide our decisions in a systematic fashion. A further goal of this research program is to use the knowledge gleaned from these studies to inform public policy debates, for example in understanding how expectations play a role in financial, environmental, and health-care decisions.
Speaker: Martijn Huijnen (Professor Bioinformatics, Radboudumc)
Title: Optimization to predict disease mutations, analyze cell deformability and cluster proteins
Abstract: I will discuss three projects involving optimization approaches. The first concerns predicting whether mutations cause disease based on their location in the 3D structure in a protein. Here we used a 3D convolutional neural network to predict disease mutations and analyzed which features, besides sequence conservation, contributed to the performance of the model. I will also highlight how predictions are impacted by the underlying disease mechanisms of missense mutations. In the 2nd project we used deep learning methods to quantify the deformability of red blood cells that were infected with malaria parasites based on image data. Deep learning requires a significant amount of labeled data to train. By creating images of cells and mimicking noise and plasticity in those images, we generate synthetic data to train a network to detect and segment red blood cells from video-recordings, skipping the need for manual annotation. We uncover significant differences between the deformability of RBCs infected with different strains of Plasmodium falciparum, providing clues to the variation in virulence of these strains. In a third project we analyze migration profiles of proteins on non-denaturing gels. Such migration profiles in principle allow the detection of protein complexes, as proteins from the same complex have similar migration profiles. Nevertheless, variation between gels has to be accounted for to allow comparison of multiple experiments. We introduce using multi dimensional dynamic programming to align the gels and facilitate large scale comparisons.
