What is emergence, after all?
https://arxiv.org/abs/2507.04951

We hear the word #emergence a lot—in science, philosophy, even everyday conversation—but what does it really mean? In this perspective paper, we take a clear-eyed look at emergence as it manifests in real systems, ranging from flocking birds to magnets to herd immunity in social networks. We explain how complex behaviors and patterns can emerge from simple parts interacting locally, and why these large-scale phenomena often can’t be easily understood just by looking at the pieces alone. Instead of getting lost in buzzwords, we break down the idea using concrete examples, showing that emergence isn’t magic—it’s measurable, physical, and beneficial for making sense of the multi-layered complex world we live in.

Springer Nature book on machine learning is full of made-up citations

https://retractionwatch.com/2025/06/30/springer-nature-book-on-machine-learning-is-full-of-made-up-citations/

two #PhD studentships in real-time infectious disease modelling
https://www.lshtm.ac.uk/study/fees-and-funding/funding-scholarships/research-degree-funding/phd-studentships-real-time-infectious-disease-modelling

#Postdoc Researcher on alignment of MMFM with ethical, legal, and social values. Specifically, Oxford Internet Institute

https://my.corehr.com/pls/uoxrecruit/erq_jobspec_version_4.display_form?p_company=10&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=180757

Strength and weakness of disease-induced herd immunity in networks
https://www.pnas.org/doi/10.1073/pnas.2421460122

What if #herd_immunity isn’t just about how many people are immune, but how they’re 'spatially' connected? Our new PNAS paper explores this concept. We show how the topology and geometry of social networks influence the dynamics of herd immunity, whether it arises from infection or #vaccination.

Here is a less technical blog post for a more general reader: abbas.sitpor.org/2025/07/10/the-spatial-puzzle-of-herd-immunity

Strength and Weakness of Disease-induced Herd Immunity in Networks

During the COVID-19 pandemic, several studies suggested that the spread of infection might induce herd immunity more easily than previously thought due to population heterogeneity. However, these studies relied on differential equation-based epidemic models, which cannot account for correlations between individuals. We reexamine the effect of disease-induced herd immunity using individual-based contact network models. We find that herd immunity is weaker when such correlations are taken into account, so much so that the conclusions of the previous studies may be overturned. This effect is especially pronounced when the contact network is spatially embedded. Our results highlight the importance of considering network effects in policy decisions that affect the lives and well-being of millions in future pandemics.

Generated by Google NotebookLM

Introduction to correlation networks: Interdisciplinary approaches beyond thresholding
https://arxiv.org/abs/2311.09536

Opinion dynamics: Statistical physics and beyond
https://arxiv.org/abs/2507.11521

Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton

https://youtu.be/IkdziSLYzHw

Why AI chatbots lie to us | Science
https://www.science.org/doi/10.1126/science.aea3922

This playlist contains all keynotes from IC2S2'25 in Norrköping, Sweden.

https://youtube.com/playlist?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We&si=JfZHBovx27npBd5t

Integrating explanation and prediction in computational social science

https://youtu.be/c7BB5Svd8aw?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

https://www.nature.com/articles/s41586-021-03659-0

#PhD studentships in real-time infectious disease modelling
https://www.lshtm.ac.uk/study/fees-and-funding/funding-scholarships/research-degree-funding/phd-studentships-real-time-infectious-disease-modelling

Human Mobility in Epidemic Modeling

Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.

https://www.arxiv.org/abs/2507.22799

Estimated fraction of LLM-modified sentences across research paper venues over time.

https://www.nature.com/articles/s41562-025-02273-8

Optimistic people are all alike: Shared neural representations supporting episodic future thinking among optimistic individuals
https://www.pnas.org/doi/10.1073/pnas.2511101122

Neural processing of cognitive function is similar among individuals with positive traits but more dissimilar among those with negative traits. Applying the cross-subject neural representational analytical approach, we found that optimistic individuals display similar neural processing when imagining the future, whereas less optimistic individuals show idiosyncratic differences. Additionally, we found that optimistic individuals imagined positive events as more distinct from negative events than less optimistic individuals.

Findings derived from a combination of IS-RSA and INDSCAL, suggest the existence of shared neurocognitive representations based on the emotional dimension among optimistic individuals, despite the fact that different individuals may perceive the same future event differently.

Elements of successful NIH grant applications

https://www.pnas.org/doi/10.1073/pnas.2315735121

5 postulates for Successful Applications 101:

1. The application is for the reviewer, not you, the applicant—remember that.
2. Learn from the Greek—communicate in stories.
3. Your Specific Aims story needs to be cohesive—leave no puzzling gaps.
4. Motivate the reviewer to keep reading—make your story resonate.
5. There is serendipity and noise in the peer-review system—accept that.

The Scales of Human Mobility - Laura Alessandretti

https://youtu.be/AwKlne2VAME

#Postdoc (Bioinformatics/Data Science) in data-driven protein-protein interaction research at Department of Drug Design and Pharmacology

https://jobportal.ku.dk/videnskabelige-stillinger/?show=164645