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Rishabh Joshi

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Rishabh gravitated towards the field of analytics when he learned that cricket teams around the world began using large-scale historical data to predict the pitch conditions and winning combinations. He graduated from the Indian Institute of Technology, Guwahati, with a Bachelor of Technology in Mathematics and Computing. He has completed two summer internships to gain hands on experience working with huge datasets.

Rishabh was awarded the IAS Summer Research Fellowship after his sophomore year. His work involved detecting communities in dynamic social networks. He created a time-based dynamic co-authorship network from the DBLP Computer Science Bibliography and implemented an algorithm, which outperformed the state-of-the-art in community detection, Louvain method, in terms of modularity. Rishabh was one of the 160 students throughout India to secure the DAAD WISE (Working Internships in Science and Engineering) Scholarship after his junior year. He completed an internship with the Machine Learning Group at Saarland University in Germany. He constructed an author citation network from arXiv in Python to distinguish between authors sharing names. The network contained more than 1.35 million nodes and 172 million edges. This project exposed him to a massive heterogeneous data set, which needed to be cleaned and organized into a computationally utilizable format.

Curious about the statistical side of data science, in his senior year, Rishabh worked on his Bachelor’s Thesis titled “Data Analysis in Cancer Research”. He performed Bayesian analysis of progressively censored data and Bayesian estimation of parameters using Hamiltonian Monte Carlo algorithm and the No-U-Turn Sampler in R. Rishabh utilizes his free time to participate in online courses such as the Data Science Specialization on Coursera.

Through the MSiA's cohort-style approach, Rishabh is eager to acquire skills to perform in a diverse team-based environment. His keenness to assimilate knowledge drives him to expand his technical skillset and analytical mindset by working on massive real-world datasets through the practicum, capstone and several course projects. Further, with the quantitative undergraduate courses under his belt and the comprehensive nature of MSiA, he aims to develop a deep conceptual understanding so as to thrive in the dynamic world of data science.