About me

Chief Scientific Officer 

Visiting Research Scientist  
Laboratory for Neural Computation and Adaptation

Principal Scientist & Head of Research*
LeapMind, Inc. Japan *
Bernstein Center for Computational Neuroscience *                      
* previous affiliations 
E-mail: sakya [at] ascent [the usual] ai

Recent News Updates

My  paper on "Transfer learning from synthetic to real images using variational autoencoders for robotic applications" is now available on Arxiv https://arxiv.org/abs/1709.06762
Robot experiment video link: https://goo.gl/3TZP38

Our paper on "Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions" will be presented in the Time-series workshop at NIPS 2017

Our paper on "Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA" will be presented in the MLPCD workshop at NIPS 2017

Associate editor - "deep learning for robotics & automation" for top robotics conference ICRA 2018

Our paper on "Text To Image Generative Model Using Constrained Embedding Space Mapping" appears in MLSP 2017

Our paper on "
Conditional generation of multi-modal data using constrained embedding space mapping" presented at the Implicit Models workshop, ICML 2017

Delivered a half-day tutorial on "Energy-based machine learning" at IJCAI 2017 (jointly with Takayuki
Osogami ) Link: https://researcher.watson.ibm.com/researcher/view_group.php?id=7834

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.

Previously I was 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.

At present, I am a Principal Scientist and the Head of Research, for the Tokyo based startup LeapMind, Inc. My research team and I are building novel technology to compress massive scale deep learning models for efficient computation on edge devices, with applications to robotics and the Internet of Things. We are pursuing both deep learning algorithm research as well as research on custom deep learning hardware using our own FPGA device. 

Research Interests: 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 :))

Subpages (2): Publications Resume