The Learning Planet Institute is the new iteration of the Center for Research and Interdisciplinarity, or CRI, started by researchers François Taddei and Ariel Lindner in 2006. As suggested by its name, the Learning Planet Institute is a driver of the learning-society revolution that believes learning, research, collective intelligence, and creativity can help both people and organizations adapt to the increasingly complex challenges of our rapidly changing world.
We are a research-and-development center as well as a graduate and post-graduate school developing programs for kindergarteners and doctoral students alike and every age in between—and even for continuing-education learners. We likewise offer services to government agencies, companies, and other types of organizations as they embark on their learning-society transformation. Our unique position as a digital-innovation hub tapped into a global community of change-makers lets us explore and share knowledge in new ways at an unprecedented scale, all for the purpose of facing head on the great social and environmental challenges of our time.
Thanks to long-standing partners the Bettencourt Schueller Foundation, University Paris Cité, CY Cergy Paris University, the French General Secretariat for Investment, the City of Paris, Inserm (the French Institute of Health and Medical Research), and UNESCO, the Learning Planet Institute can continue in the CRI’s legacy as an agent of change where research and entrepreneurship come together to have a real impact in making the world a better place for live.
We will have a Network Seminar by Sasha (Alexander) Belikov.
The seminar will be hybrid. It will be held at LPI, room 2.05. Please register above to receive a link for online participation
Title: 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.
More information on the network seminar: https://interactiondatalab.com/network-seminar/