“As global norms are set and scientific standards issued, the absence of the US [from WHO] will undermine scientific research, health security, and foreign policy,” write Lawrence O. Gostin and Benjamin Mason Meier in a new #ScienceEditorial. https://scim.ag/3E74dP9
Seven Australopithecus specimens uncovered at the Sterkfontein fossil site in South Africa were herbivorous hominins who did not eat substantial amounts of meat, finds a new Science study that analyzed organic nitrogen and carbonate carbon isotopes extracted from tooth enamel. scim.ag/40uupuN https://scim.ag/40uupuN
New materials with interlocking parts can flow like liquid or contract like muscles. Learn more: scim.ag/4ga4spS
“… US President Donald Trump’s misguided announcement that the US will withdraw from the Paris Agreement on climate continues a long history of confusion … over the intersection of climate science and US climate policy,” writes @holdenthorp.bsky.social in a #ScienceEditorial. scim.ag/4gZHWRS
Scientists have identified the characteristics of #macrophages in the dura mater, revealing their distinct markers and behaviors during homeostasis and autoimmune neuroinflammation in mice. Read more in #ScienceImmunology: https://scim.ag/4g0UqaE
On #SquirrelAppreciationDay, check out a 2021 Science study that revealed how decision-making and learning capabilities complement the biomechanical adaptations that enable “squirrel parkour.” https://scim.ag/3WtCCOp
“The legacy of COVID-19 must not be defined solely by pain, loss, and disruption. Rather, the experience is fraught with lessons that the world should not waste,” writes Maria D. Van Kerkhove in a new #ScienceEditorial. https://scim.ag/3DWFL2V
The Gans’s egg-eater—a slender, meter-long snake native to West Africa—can swallow prey nearly four to five times its head width; think of a squirrel scarfing down a house cat. It’s secret? Superstretchy neck skin—90% more elastic than the skin along the rest of its body. https://scim.ag/3CqpeUm
“My memories of Lebanon and the resilience of its people strengthened my resolve.” This week’s #ScienceWorkingLife: https://scim.ag/3C2dtnd
“One email at a time, one informational interview after another, I became comfortable, confident, and strategic in building my network of mentors.” One mentor isn’t enough, wrote Erika Moore in this 2021 #ScienceWorkingLife essay. https://scim.ag/3DWR4YV #NationalMentoringMonth
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.