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Complex Systems and Machine Learning in Molecular Biology with Stefano Zapperi and Caterina La Porta

Complex Systems and Machine Learning in Molecular Biology with Stefano Zapperi and Caterina La Porta

FromThe Ensemble Podcast, by CrunchDAO


Complex Systems and Machine Learning in Molecular Biology with Stefano Zapperi and Caterina La Porta

FromThe Ensemble Podcast, by CrunchDAO

ratings:
Length:
62 minutes
Released:
Apr 7, 2023
Format:
Podcast episode

Description

Stefano Zapperi is Professor of Theoretical Condensed Matter Physics and Coordinator of the Center for Complexity and Biosystems at the University of Milan. He graduated in physics at the University of Rome “La Sapienza” and received his Ph. D. in physics from Boston University. After a postdoctoral position at ESPCI in Paris, he became tenured researcher at INFM at the University Rome and then at the University Modena and Reggio Emilia. He has been invited as visiting scientist or visiting professor in many institutions worldwide. Prof. Zapperi is an expert in the statistical physics of complex systems and has fostered computational and data driven approaches to materials science and biophysics. His most notable contributions include the theory of the Barkhausen noise in magnets, the statistical physics of plasticity and fracture, and recent work on the physics of cancer and protein aggregation. Prof. Zapperi is the recipient of numerous awards including the Marie Curie Excellence Award. He was elected fellow of the American Physical Society and named Finland Distinguished Professor by the Academy of Finland. He organized several international workshops, summer schools and symposia on a variety of interdisciplinary research topics, ranging from the “Physics of Cancer” to Statistical Physics of Materials and Complex Systems. He has been elected member of the council and the executive committee of the Complex Systems Society and acted as chair of the steering committee of the Conference on Complex Systems. In 2018 he co-founded Complexdata alongside Caterina La Porta.

Caterina La Porta is professor of General Pathology at the University of Milan where she coordinates the research group Oncolab and is member of the steering committee of the Center for Complexity and Biosystems. In 2018, she co-founded the startup ComplexData where she serves as CEO. During her scientific career, Prof. La Porta published more than 200 papers in international journals mainly on cancer and neurodegenerative diseases, receiving thousands of citations. In the past 15 years, she shifted her interest to quantitative biology and digital health. The focus of her current research activity focuses on understanding cancer heterogeneity using tools from cell biology, biophysics and data science. Her interdisciplinary view of cancer is summarized in a book published by Cambridge University Press in 2017 entitled “The Physics of Cancer”. Prof. La Porta was selected as one of the 100 most important female scientists in Italy (https://100esperte.it/) and was visiting scientist in many universities around the world, including MIT, Cornell University, Aalto University, Rice University, the ENS Paris, the Weizmann Institute of Science and LMU in Germany. Prof. La Porta has a long track record of public outreach activities, including the organization of several editions of the EU Researcher’s night, and is involved in many other science dissemination activities.
Released:
Apr 7, 2023
Format:
Podcast episode

Titles in the series (9)

From Chaos Theory, through mathematical and Crowdsourced research, the Ensemble Podcast addresses the scientific issues related to financial prediction.  The interviews are conducted by our lead quant Matteo Manzi. Market predictions solved by its decentralised network of 2000 data scientists and 200 PHDs positions CrunchDAO’s as a leader in building a symbiotic connection and harmonised relationship between the futurist DAO and institutional investor networks. In addition, with its transparent and decentralised nature, CrunchDAO is at the forefront of social innovation and redefining the future of work for data scientists around the world.