Dr.Prabuchandran K.J of IIT Dharwad

Machine learning involves a lot of complex math and coding that, at the end of the day, serves a mechanical function the same way a flashlight, a car, or a computer screen does. When we say something is capable of “machine learning”, it means it’s something that performs a function with the data given to it and gets progressively better over time. A deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning applications use a layered structure of algorithms called an artificial neural network. The design of an artificial neural network is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.

Biography:

Prabuchandran K.J. is an Assistant Professor at IIT Dharwad. He completed Ph.D. from the Department of Computer Science and Automation, IISc in the area of Reinforcement Learning. Post his PhD, Prabuchandran worked as Research Scientist at IBM Research Labs, India for an year and half on change detection algorithms for multivariate compositional data. After that he pursued his postdoctoral research at IISc, Bangalore as an Amazon-IISc Postdoctoral scholar for an year and half on Multi-agent Reinforcement Learning and Stochastic Optimization algorithms. His research lies in the intersection of reinforcement learning, stochastic control & optimization, Machine Learning, Bayesian Optimization and stochastic approximation algorithms. His research interest also focuses on utilizing techniques from these fields in solving problems arising in applications like wireless sensor networks, traffic signal control and social networks.

Address:Assistant Professor, Department of CSE, IIT Dharwad, Dharwad, India