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.
Postdoctoral Fellow for Computational Neuroscience
Institute of Computational Neuroscience
Universitätsklinikum Hamburg-Eppendorf (UKE)
Martinistrasse 52 , 20246 Hamburg, Germany