Pawel has a back ground in theoretical physicist and is interested in application of statistical physics and stochastic processes to biological and ecological problems. He has worked on mathematical models of collective motion in biology, as well as related problems of pattern formation and self-propelled motion. Among other things he is currently interested in the question how collective behavior is shaped and constrained by locally available sensory information, how to determine causal information flows between individuals in animal groups, and how do animal and human groups process information in a collective way. Personal homepage: http://romanczuk.de/
Winnie is currently studying fundamental properties of visual networks and complex contagion with applications to collective behavior of locusts and fish. She has a background in theoretical physics and has previously studied the formation of patterns on networks of nonlinear oscillators. More specifically, she focused on the influence of the network topology on pattern formation. She has a broad interest in dynamic processes on networks, such as the spreading of information or diseases, and was already engaged in research of social influence in human groups as a student assistant.
Pascal plans to work on modeling collective response to threats, and analysis of causal information flows in fish schools. He has a background in data analysis, anticipation or prediction of tipping points and in opinion dynamics on time evolving networks. The latter involved studies in which zealots where introduced, whose opinion can not be altered by other agents. Specifically the spreading of their opinion in dependence on their degree was of interest.
Parisa works on her PhD at the Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran. Her broad interests are soft condensed matter physics, biophysics, statistical physics, computational physics,
and complex systems. He is visiting our lab since June 2017, where she works with Pawel Romanczuk on the role of different interaction networks on collective behavior of self-propelled agents in heterogeneous environments and in the presence of informed individuals.
Yinong did his undergraduate has a background in mathematics and applied mathematics and did his Master in System Theory at the Beijing Normal University. He is interested in fundamental aspects of complex systems with a particular focus on emergence and stability of collective states. Currently, he explores the ability to distinguish two fundamental types of (social) interaction, both leading to emergence of collective motion at large scales, through observations of the collective dynamics of corresponding agent-based models.
Andrej has a background in computer science and computational neuroscience. Currently he is working on his Master project within the Computation Neuroscience Master program at the Bernstein Center for Computational Neuroscience. In his thesis he develops a neuronal model for startle response in schooling fish.
Former Members & Visitors
Rachana visited us in June/July 2016 just after her first year in computer science at Princeton University. During her internship she worked for 6 weeks on analysis of video data of fright waves in dense fish schools, recorded during our 2016 field trip to Mexico in collaboration with colleagues from Berlin, Princeton and Konstanz.
Ishan worked in our group in May/June 2016 just after finishing his first year studying Mathematics at Princeton University. He worked with Pawel Romanczuk for 6 weeks on collective information processing in agent-based models of flocking. More specifically he studied the response of a flock to a predator agent using computer simulations.
Conor, a physics undergraduate from University of St Andrews (Scotland), spent three months in our lab during summer 2017 as a research intern (RISE program / DAAD) working with Pascal Klamser and Pawel Romanczuk. Conor worked on a project involving using a convolutional neural network to detect fish in complex video data from our field research in Mexico. In particular, he wrote a program that takes the fish object detected by the neural network and connects individuals across frames in the video. Conor was able to achieve a significant improvement of the fish detection by reducing false positive detection via filtering the neural network output based on the length of trajectories obtained from this chaining process.
Clark visited our lab in June/July 2017 as an architecture undergraduate at Princeton University. He explored during his internship the modeling of complex systems with Python. In particular, he analyzed an cellular automata model of an excitable system as a coarse-grained model for spreading of startling response cascades and sustained startling waves in large-scale, high-density fish populations.