Nino Antulov-Fantulin is a senior researcher at the ETH Zurich (COSS group) and a head of research at Aisot Technologies AG.
At ETH Zurich, he works as a lecturer on the following courses: Data Science in Techno-Socio-Economic Systems; Complex Social Systems: Modeling Agents, Learning, and Games; Complexity and Global System Science; Machine Learning and Modelling for Social Networks.

His main interests are at the intersection of complexity science and (financial) data science.
In particular predictive analytics for finance, dynamical processes on networks, machine learning, social network analysis, network dismantling, Monte-Carlo algorithms.
His interdisciplinary research contributions include: Nature Commun. 13, 333 (2022), Phys. Rev. Research 2, 033121 (2020), Proc. of the national academy of sciences 116.14 (2019), Pattern Recognition 82 (2018): 40-55., Phys. review letters 114.24 (2015), and different conferences: ICDM 2018, ICML 2019, ICLR 2020.
Results from his research were covered by New Scientist, Popular Science magazine and different online media Pacific Standard, American Physical Society, ETH News, ACM TechNews and others

Beside ETH Zurich, he worked at the Rudjer Boskovic Institute and the Faculty of Electrical Engineering and Computing in Croatia and as a visiting scientist at the Robert Koch Institute (Berlin) & Courant Institute of Mathematical Sciences (New York). He worked on several EU projects: SoBigData - “Social Mining & Big Data Ecosystem”, Multiplex−“Foundational Research on MULTI-level comPLEX networks and systems”, FOC−“Forecasting Financial Crisis” and e-Lico− “An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data-Intensive Science”. He also works as Supervisor & Panel member of PhD Program in Data Science, Scuola Normale Superiore, Pisa.
He acts as reviewer for IEEE, ACM, Nature Communications, Nature Scientific Reports, Applied Network Science, ICML, ICLR, ECML-PKDD, NeurIPS and is in the program committee of “International Conference on Complex Networks and Their Applications” and “ECML-PKDD Applied Data Science Track”.