By mapping the meanings of the words used to communicate emotions across more than one-third of the planet’s spoken languages, a study in Science found that there is significant variation in how emotions are expressed across cultures. #ScienceMagArchives scim.ag/41X6dDk https://scim.ag/41X6dDk
Move over mitochondria, a new organelle called the nitroplast is here. In a Science study from earlier this year, researchers report that a nitrogen-fixing organelle has been identified in a marine alga. Learn more in this #SciencePerspective: https://scim.ag/49GjClc
A 2015 #ScienceAdvances study finds #Christmas trees once had a second set of genes—a feature that allows one copy to change what it does without affecting the organism’s survival. Learn more: https://scim.ag/3OYI8EK
Climate change is driving global extinction risks, with 1.6% of species threatened at 1.3°C of warming and risks escalating to 29.7% at 5.4°C, according to a new meta-analysis in Science encompassing more than 30 years of research. Read more: https://scim.ag/3OZgGGX
“‘’Twas the night before grant deadline, 7:15. Many creatures were stirring, thanks to caffeine. The comments accepted, the references done, In hopes someone might fund my first R01.” https://scim.ag/3ZYWqeL
A #SciencePerspective from earlier this year highlights the growing body of evidence that hints at the potential of GLP-1–based medications in treating conditions other than diabetes and obesity, including cardiovascular disease and neurodegenerative disorders. Read more: https://scim.ag/4gmyfMY
Women leave academia at higher rates than men at every career stage, and attrition is especially high among three groups: tenured faculty, women in non-STEM fields, and women employed at less prestigious institutions, a #ScienceAdvances analysis finds. https://scim.ag/4iWJNbN
Between 800,000 and 900,000 years ago, the population of human ancestors crashed, a 2023 Science study finds. The results suggest there were only ~1280 breeding individuals during the transition between the early and middle Pleistocene. https://scim.ag/401GZSU #ScienceMagArchives
In the 20 years since the term “microplastics” was first coined, a growing body of research has consistently shown how pervasive and problematic the pollutants have become. A recent #ScienceReview provides an overview and the progress made in understanding #microplastics. https://scim.ag/3Di9seC
In a new Science study, researchers report that twisted carbon nanotube filaments can emit circularly polarized thermal radiation with a high brightness. These materials could be used at high temperatures that are unattainable by existing emitters. Learn more in this issue: https://scim.ag/41A9W9Y
As a physicist, I seek to gain a mechanistic understanding of the functionality of the human brain and the resulting behavior. Depending on the resolution of the examined data, behavior is to be understood as an emergent phenomenon of the interactions of individual neurons, populations of neurons or whole brain regions. With the help of generative neural networks, I reconstruct empirical neuronal activity and use the resulting models as surrogates for the underlying dynamical system. They can then be used for in silico experiments to simulate the response of the brain to artificial conditions and thus draw conclusions about the physiology.
Besides that, I am interested in the development of deep learning models for the prediction of time series and the reconstruction of dynamical systems in general.
I spend my free time doing sports (running, hip hop, cycling) or let my thoughts run free while hiking. You can reach me at niklas.emonds@iwr.uni-heidelberg.de.
My recent research has focused on developing machine learning algorithms for extracting dynamical systems models from empirical time series data. One of my main motivations was to make these approaches work in real world clinical settings, including multi-subject data, very short, noisy, and non-Gaussian time series. A novel research direction I want to pursue is the intersection of creativity and generative AI.
Outside of research, I enjoy many things art-related (making music with my jazz trio triolog, literature, cinema), as well as cooking, playing chess and traveling.
I studied psychology and mathematics. My research interprets psychological processes as dynamical systems that can be analyzed and controlled. I develop and apply recurrent neural networks, control theoretic, and reinforcement learning methods to time series data of human affect and behaviour. My goal is to optimize interventions to improve mental well-being, and to gain a better understanding of these inherently nonlinear processes.
I am a Postdoctoral fellow at the AI Health Innovation Cluster, working with the University of Heidelberg and the ZI Mannheim to develop computational models of social interactions for adaptive gamified treatment approaches. I hold an MSc in Cognitive and Decision Sciences, an MRes in Computer Science (Machine Learning) from University College London, and a PhD in Experimental Psychology, specializing in experimental and computational approaches to strategic social interaction. My research focuses on social learning in mental health disorders, particularly exploring how cognitive interventions from leading psychotherapies can be integrated with computational behavior models to address social learning dysfunction.
Outside office hours, you’ll probably find me hiking around the hills in Heidelberg.
I’m a Data and Computer Science student at the University of Heidelberg, with a strong background in software development and machine learning. I’ve worked on web development, iOS apps, computer games, and full-stack projects. My current research adventures include diving deep into topics like Large Language Models and Retrieval-Augmented Generation—yep, the cool AI stuff!
I’ve studied in Germany, Italy, and India, and I’m always excited to take on new challenges and build scalable solutions.
When I’m not geeking out over code, you’ll find me in the kitchen whipping up some Indian cuisine (seriously, hit me up if you’re hungry). I also love jamming on the piano or violin, swimming, and occasionally crushing it at table tennis!
My work is driven by a deep curiosity to uncover the underlying mechanisms of human behavior, ranging from individual cognitive functions to complex social dynamics. Through machine learning and computational models, we aim to bridge the gap between human cognition and societal interactions, advancing both scientific understanding and practical applications.
I studied mathematics at the University of Mannheim, where I was first introduced to both reinforcement learning and generative time series modelling, sparking my interest in their algorithmic foundations and applications to computational neuroscience.
My current focus is on using reinforcement learning models to better understand the psychological and neuroscientific processes behind human decision-making, particularly in social interactions and psychiatric disorders.
I’m also exploring how reinforcement learning concepts can be integrated with dynamical system reconstruction, especially in the presence of chaos and randomness.
Outside of academics, I enjoy playing and coaching Ultimate Frisbee on an international level.