“In naming … lenacapavir as the 2024 #BOTY, Science acknowledges the next, but by no means final, step in the drive to fight HIV/AIDS, where the rigors of the laboratory and the needs of humanity are inseparable,” writes @holdenthorp.bsky.social in this week’s #ScienceEditorial. scim.ag/4g9kjG4
Science’s 2024 Breakthrough of the Year is lenacapavir, an injectable drug that demonstrated remarkable success at preventing HIV infection with one shot every 6 months. Learn more about this year’s #BOTY and other big advances in science: scim.ag/3BrCtUn
Mary’s long, luxurious baths have drawn so much attention that an envious elephant at the Berlin Zoo has figured out how to shut the water off on her supersoaking rival—a type of sabotage rarely seen among animals. Learn more: scim.ag/49C1s3S
Recent research helps explain why people who are incorrect are so confident in their correctness. scim.ag/3ZNqKcd https://scim.ag/3ZNqKcd
Although tricky to target, #ProstateCancer cells that lack p53 still depend on the integrated stress response and can be killed with inhibitors of the enzyme GCN2 that disrupt purine synthesis. Read more in #ScienceSignaling: scim.ag/3D6Lyme https://scim.ag/3D6Lyme
The Indian government has announced a giant deal with multiple publishers that will allow an estimated 18 million students, faculty, and researchers free access to nearly 13,000 journals through a single portal. scim.ag/49vrG8c https://scim.ag/49vrG8c
Newly discovered footprints show that at least two hominid species were walking through the muddy submerged edge of a lake in Kenya’s Turkana Basin at the same time, about 1.5 million years ago. Learn more in Science: scim.ag/49x1s5g https://scim.ag/49x1s5g
From carbon dioxide to starch—no plants required. In Science, researchers developed a cell-free method of synthesizing starch from CO2 and hydrogen using a combination of chemical catalysts and a carefully selected set of enzymes. Learn more: scim.ag/3ZycORU #ScienceMagArchives https://scim.ag/3ZycORU
Macrophages deliver polarizing messages to promote immune suppression in tumors. Learn more in a new #SciencePerspective: scim.ag/4gs0vO1 https://scim.ag/4gs0vO1
For the second time in 2 years, #mpox poses a global threat. Can the long-overlooked virus be contained? scim.ag/3VwwvIW https://scim.ag/3VwwvIW
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.