Thanks for visiting this page, I am a fourth year Phd Student at Deptt of Computer Science, Aalto University. My supervisor is Prof. Aki Vehtari. I am also a member of the Probabilistic Machine Learning (PML) group at Aalto University. My research is about the intersection of approximate inference and Bayesian statistics. More specifically, I have worked in Gaussian Process models, Variational Inference (VI) and its properties and application in probabilistic parametric models. My coauthors and I, have applied VI to problems like Extreme Classification with GP models, in Bayesian Optimisation in a pairwise comparison setup. VI has become popular due to its scalability on large datasets and complex non-conjugate models, owing in part to the success of efficient autodiff frameworks and SGD algorithm in finding the optima using simulations in form of MC samples. As a result, I am also interested in finding the theoretical grounding of SGD algorithm in context of VI and its properties as a Markov chain.
I have contributed to multiple open source scientific softwares like GPy(https://github.com/SheffieldML/GPy), viabel. Prior to my studies at Aalto, a while ago, I completed my bachelors degree in Electrical Engineering from IIT Roorkee, India and masters degree in Computer Science from KTH Royal Institute of Technology, Sweden.