Senior Research Scientist in Embodied Cognition
Laboratory for Neural Computation and Adaptation Bernstein Center for Computational Neuroscience *
* previous affiliations
E-mail: sdasgup [at] jp [the usual] ibm [the usual] com
Recent News Updates
Our tutorial proposal on "Energy-based machine learning" selected for IJCAI 2017 (jointly with Takayuki Osogami )
Dasgupta S. and Osogami T. "Nonlinear Dynamic Boltzmann Machines for Time-series Prediction" accepted at AAAI 2017 (acceptance rate of 24.6% >2500 submissions)
Dasgupta et al. " Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences", ICPR 2016 (oral presentation <14% acceptance)
Abstract selected for Talk at COSYNE 2016 main meeting. <5% acceptance (talks) (http://www.cosyne.org)
Research Theme
My diverse research interests include nonlinear dynamical systems, recurrent neural networks, reinforcement learning, time-series modeling, computational neurodynamics, neurorobotics, optimization & control. Overall, I am interested in statistical machine learning as a field and in searching for novel theoretical frameworks to build adaptive intelligent systems.
I am currently leading an IBM Research-Tokyo Far Reaching Research project on closed-loop experience building embodied robots learning framework (CLEBER). CLEBER is not only applicable to robots, but is a framework for online sequential decision making in an application independent manner.
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Dynamical Systems
Energy-based machine learning
Neuro-robotics / Biorobotics
Recurrent Neural Networks
Learning and Memory
Brain Plasticity
Reinforcement Learning
Information Theory
(not necessarily in this order :))
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