I'm widely interested in finding out / learning about the world around us, irrespective of discipline. My primary research interests are in the areas of learning in the brain and in artificial neural networks. I work on figuring out how the brain learns, using theoretical and simulational tools from computational neuroscience, control theory, machine learning and statistical physics. My current work uses: adaptive control theory to derive biologically plausible learning rules for connection weights in spiking neural networks especially for motor control; and memory-augmented neural networks to solve cognitive tasks using reinforcement learning. If you have similar (or complementary!) interests, feel free to contact me.

Currently, I'm a post-doctoral researcher in the group of Prof Raoul-Martin Memmesheimer at the University of Bonn, Germany. Earlier I was a post-doctoral researcher at the Laboratory of Computational Neuroscience with Prof Wulfram Gerstner at the École Polytechnique Fédérale de Lausanne, Switzerland.

I have a background in Computational Neuroscience (PhD - National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India with Prof Upinder S. Bhalla), Physics (Master's - Tata Institute of Fundamental Research, Mumbai, India) and Electrical & Electronics Engineering (Bachelor's - Indian Institute of Technology Madras, Chennai, India).

Check out my publications and related code, code for general-purpose neural simulation, and tutorials.