About me

Hi! I am a postdoctoral research fellow at the Institute of Computational Neuroscience in Universitätsklinikum Hamburg-Eppendorf (UKE). In collaboration with both computational neuroscience and image processing research groups, I am involved in two research and development projects:

1) SFB 1328 (A02 project) where I develop machine-learning methods for data annotation, cell segmentation (and classification), and motion compensation for high-resolution live-cell Ca2+ fluorescence microscopy.

2) Crossmodal Learning (CML, A02 project) where I study the long-short-term-memory and the computational capacity of brain-spired recurrent neural networks.

Earlier, I spent three years of amazing postdoctoral research in MINDS research group headed by professor Herbert Jaeger, one of the pioneers of reservoir computing. As a part of an EU project (NeuRAM3), our group worked on spiking reservoir computing and its implementation on neuromorphic hardwares. Specifically, within a setting at the crossroads between machine learning, theory of computing, computational neuroscience, and medical applications, I set up a machine learning benchmark in the domain of online biosignal processing, optimized a reservoir computing based neural learning algorithm, in a standard full-precision implementation (Matlab) on a digital computer and succeeded to significantly surpass the documented SoA on the chosen benchmark. For the same task, I also designed a spiking reservoir computer simulated on BRIAN.

I received my PhD in biomedical engineering from Amirkabir University of Technology (Tehran Polytechnic), Iran. In my PhD thesis, to model the dynamics of mood swings in bipolar disorders, I have proposed a novel complex model based on the notion of competition between recurrent maps, which mathematically represent the dynamics of neural activation in excitatory (Glutamatergic) and inhibitory (GABAergic) pathways in the brain.

I am strongly interested in biomedical signal and image processing algorithms, applications, and hardware implementation. Among the conventional and machine-learning processing methods, I am more involved in research on recurrent neural networks for spatiotemporal modeling and analysis where I found it very relevant to study dynamical aspects of information processing in the brain.

Here is my Google Scholar profile and I'm also active in LinkedIn, Research gate and Twitter.

Fatemeh Hadaeghi

Postdoctoral Fellow for Computational Neuroscience

Institute of Computational Neuroscience

Universitätsklinikum Hamburg-Eppendorf (UKE)

Martinistrasse 52 , 20246 Hamburg, Germany

Email: f.hadaeghi@uke.de