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Building a stronger community through weekly appointments on
- newly published papers on Data Science & AI methodologies and applications
- L'EMbeDS researchers' ongoing projects
How do JC and Science Meetings work?
How do JC and Science Meetings work?
Starting from the 18th March 2026, we want to try a new bottom-up format where all members of the L'EMbeDS community can propose and present newly published papers (no more than one year prior to the presentation), so that:
the community stays up-to-date on cutting-edge Data Science & AI methodologies and applications
we stimulate collaborations between researchers from different areas and expertise
This is the Journal Club!
JC - L'EMbeDS.pdfTo complete this initiative, we will experiment also with the Science Meetings: ooccasions for all L'EMbeDS researchers to present one of their works (ideally a not concluded one), get valuable feedbacks and possibly create collaborations.
When and where?
When and where?
The Journal Club will take place once every two weeks on Wednesday at 10 a.m. Italian Time.
The physical location will be communicated via email and also in the event details in the calendar (please, click here subscribe).
The Science Meetings will alternate the Journal Club (once every two weeks on Wednesday at 10 a.m. Italian Time), but it is not a fixed appointment: if nobody books the slot to present its work, there will be no Science Meeting.
If you want to actively participate to Journal Club and/or Science Meetings, please, join us on Slack!
Upcoming Journal Club & Science Meetings
Upcoming Journal Club & Science Meetings
Coming soon...
Past Journal Club & Science Meetings
Past Journal Club & Science Meetings
Journal Club-20260415_100449-Registrazione della riunione.mp4Journal Club - 15th April 2026
Lu, C., Lu, C., Lange, R.T. et al., "Towards end-to-end automation of AI research",
Nature 651, 914–919 (2026).
https://doi.org/10.1038/s41586-026-10265-5
Click here for Teams Meeting recording with transcription
Science Meeting - 8th April 2026
Stefano Blando, "Statistical Model Checking of the Island Model: An Established Economic Agent-Based Model of Endogenous Growth"
Abstract:
Agent-based models (ABMs) are increasingly used to study complex economic phenomena such as endogenous growth, but their analysis typically relies on ad-hoc Monte Carlo exercises without formal statistical guarantees. We show how statistical model checking (SMC), and in particular Multi-VeStA, can automate and enrich the analysis of a seminal ABM: the Island Model of Fagiolo and Dosi, which captures the exploration-exploitation trade-off in technological search. We reproduce key stylized facts from the original model with formal confidence intervals, confirm the optimality of moderate exploration rates, and perform a counterfactual sensitivity analysis across returns to scale, skill transfer, and knowledge locality. Using MultiVeStA's built-in Welch's t-test, 6 out of 7 pairwise parameter comparisons yield statistically different growth trajectories, while the exception reveals a saturation effect in knowledge locality. Our results demonstrate that SMC offers a principled, reproducible methodology for the quantitative analysis of agent-based economic models.
Journal Club - 1st April 2026
Z. Wang, G. Xu, W. Yu and L. Ou-Yang, "LineGRN: A Line Graph Neural Network for Gene Regulatory Network Inference"
in IEEE Journal of Biomedical and Health Informatics, vol. 30, no. 2, pp. 1785-1797, Feb. 2026,
Journal Club-20260318_095909-Registrazione della riunione.mp4Journal Club - 18th March 2026
Asai, A., He, J., Shao, R. et al. Synthesizing scientific literature with retrieval-augmented language models. Nature 650, 857–863 (2026). https://doi.org/10.1038/s41586-025-10072-4
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