Shubhanshu is a 5th year Ph.D. student at the iSchool, University of Illinois at Urbana-Champaign. His research involves using large scale data mining and machine learning solutions, to study the temporal evolution of science. He ongoing research work is focused on studying topical novelty of scientific articles, measuring relative topical expertise of authors on an article, and studying factors which affect the self-citation practices by authors. He finished his Integrated Bachelor’s and Master’s degree in Mathematics and Computing from the Indian Institute of Technology, Kharagpur in 2012. He was a fellow of Kishor Vaigyanik Protsahan Yojana (KVPY), a scholarship program funded by the Department of Science and Technology of the Government of India, from 2007 to 2012.
… no models are [true]—not even the Newtonian laws. When you construct a model you leave out all the details which you, with the knowledge at your disposal, consider inessential…. Models should not be true, but it is important that they are applicable, and whether they are applicable for any given purpose must of course be investigated. This also means that a model is never accepted finally, only on trial.