Event Date: Thursday 15th December 5-6 pm CET
Alexander Belikov :
Quantifying Scientific Discovery to Improve the Knowledge of Facts
Abstract: The ever-increasing amount of published academic results poses a challenge in interpretation and validation of these publications and rendering them to scientific facts. Despite the apparent lack of alignment between published claims and established facts, accounting for network structure enables predictive models that can assess the validity of published claims. Using pre-trained models on simulated alternative attention and local clustering distributions (which translates to modifications of funding policies) of academic publication we show that the overall knowledge of facts may be dramatically improved. We conclude by a discussion of applications of our methodology to other domains.
Bio: Sasha (Alexander) Belikov started his career as a physicist with contributions in condensed matter and dark matter physics. After switching gears to become a quantitative researcher in finance for 2 years, he then did a postdoc in computational sociology at the Knowledge Lab at the University of Chicago. In the past 3 years, while leading the data science team at a Parisian start-up Hello Watt, he has remained involved in modeling scientific processes and the development of tools thereof.
