I am a fourth year Phd Student at Deptt of Computer Science, Aalto University. 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. I am also proficient in probabilistic programming frameworks like Stan, PyMC3, Pyro, TensorFlow Probability. Read more about PPLs here : https://en.wikipedia.org/wiki/Probabilistic_programming I have multiple first author publications in leading ML/Stats conferences like NeurIPS, MLSP. My research was focussed on improving the robustness of current methods in Variational Inference. More recently, I am also interested in Bayesian Deep Learning applications and uncertainty quantification for deep learning models like CNN, RNN etc. The idea being that you get a range over predictions and parameters with probabilities in place of point estimates. Please check my DBLP page for research related work: https://dblp.org/pid/182/2256.html Please find my extended profile here:https://adhaka.github.io/ I have contributed to multiple open source scientific softwares like GPy(https://github.com/SheffieldML/GPy), viabel(https://github.com/jhuggins/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. I also worked as a backend developer for a period of three years, after completing my graduation.