Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SocialReality (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499465.0 €

Social reality is comprised of numerous social constructs such as institutions, practices, and norms. These constructs are mind-dependent (e.g., paper money is valuable because people believe it is). Research suggests that when people misconstrue social constructs to be mind-independent facts (e.g., viewing inequality as a fact of nature), they are less likely to question their legitimacy. Thus, the attribution of mind-independence to social constructs, or reification, plays a critical role in governing human behavior. Despite the importance of this process, we currently do not have a general scientific theory that explains reification across its varied instantiations (e.g., the reification of nations, economic systems, mental ailments). In the proposed project I aim to develop and test a domain-general, information-processing theory of reification. My pilot data suggest that there is both between-individual and within-individual gradation in the extent to which constructs are reified. I argue that in both cases, reification may be a consequence of (i) de-mentalizing (i.e., adopting a mindset and a discourse that does not consider mental states); (ii) the mere passage of time and increased experience with the construct; (iii) abstraction of knowledge (in which the process of generating facts omits information concerning the human origins of constructs, giving rise to erroneous inferences of naturalness). These three processes may be causally related (i.e., with increased experience, constructs are abstracted, leading to de-mentalizing); however, they also represent three stand-alone hypotheses which will be assessed across five work-packages—using neuroimaging, natural language processing methods, longitudinal big-data analyses, and behavioral paradigms from experimental economics and memory research. A better understanding of reification may help us devise interventions to de-reify maladaptive states of affairs, thereby addressing some of society's greatest ills.

Mots-clés : mentalizing,abstraction,shared reality,social memory
Projet ERC : MagGelGut (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Gastrointestinal (GI) disorders affect over 40% of the world population. Pharmacological agents that modulate cell function and subsequently impact processes involved in such disorders, suffer from limited spatiotemporal specificity and accompanying side effects. Despite the advances in the field of bioelectronic medicine which have enabled localized modulation of electrogenic cells, it still requires invasive hardware, impeding their use in soft and continuously moving organs. We recently showed that magnetic nanoparticles injected into a deep solid organ and exposed to alternating magnetic fields (AMFs), dissipate heat that can trigger localized calcium signaling mediated by endogenously expressed heat-sensitive ion channels. However, penetrating the sensitive and hollow intestine tissue is challenging. This proposal aims to develop a novel approach for remote activation of the intestine via adhesive materials. To achieve this goal, an implantable, biocompatible and stretchable magnetic gel will be designed with controlled adhesiveness, allowing for its effective adherence to tissues and detachment. The magnetic gel can be remotely controlled to dissipate heat under AMFs. Prior to attempting thermal stimulation of the intestine, a proof-of-principle demonstration will be performed in the adrenal gland, a solid organ which expresses heat-sensitive ion channels. Next, the gel properties will be tailored to the intestinal tissue and the activation of heat-sensitive ion channels in the colon will be thoroughly characterized ex vivo. Finally, in vivo entero-modulation will be demonstrated, while addressing barriers associated with use of magnetic nanomaterials in in vivo models and modulating parameters to achieve short latency between stimulation onset and cell response. Ultimately, I propose to achieve wireless on–demand control of calcium influx and neuropeptide release in the colon, to address gastric symptoms of disturbed motility and inflammatory response.

Mots-clés : magnetic nanoparticles,bioadhesive,trpv1,motility symptoms,inflammatory bowel diseases,alternating magnetic fields,hydrogel
Projet ERC : AlgoHumanBoss (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1467455.0 €

In recent years, we have been witnessing a move to algorithmic management, in which supervisory tasks—instructing workers; appraising their performance; and determining their compensation—are being delegated to software applications. Current understanding of such effects is limited, however, as most research on algorithmic management focuses on gig-economy platforms in which traditional supervisory relationships are absent. The proposed research thus aims to explore: How does the introduction of algorithmic management in traditional organizations—in which algorithms operate alongside rather than instead of a human boss—shape human supervisory relationships and practices? To achieve this objective, I will undertake four subprojects, grounded in an empirical, in-depth qualitative approach. Subproject 1 will develop a theory to describe how the introduction of algorithmic management changes supervisory dynamics in traditional organizations. I will collaborate with five organizations spanning different industries and observe workers and supervisors to uncover their experiences of supervisory relationships. Subproject 2 will provide an articulated account of how algorithmic management changes managerial roles and practices, through interviews with a large sample of managers that implement algorithmic management. Subproject 3 will reveal how workers respond to conflicting directions from humans and algorithms. In this study, workers will participate in simulations of actual workplace dilemmas, created using immersive VR technology. Subproject 4 will address the mitigation of detrimental outcomes of algorithmic management through ethnographic action research. The proposed research will provide ground-breaking insights regarding the emerging yet unexplored phenomenon of algorithmic management in traditional organizations, and it will enable practitioners to effectively and ethically update managerial practices for the age of AI oversight.

Mots-clés : algorithmic management,qualitative research,algorithmic control,virtual reality,artificial intelligence,action research
Projet ERC : PhotoTopoCurrent (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2316250.0 €

Topological materials have captured the imagination of scientists with unique electronic dispersions and surface states. While their potential seems huge - from advanced photodetectors to spintronic devices - so far it has not come to fruition, despite two decades of research. In this proposal, my aim is to reveal and control light-matter interactions, electron populations, and currents in topological bands by combining two fields of research: topological materials and nonlinear optical coherent control. Nonlinear quantum coherent control was a major leap in ultrafast science, enabling optical control of chemical reactions and electronic processes in atoms and molecules on femtosecond time scales. In solid-state systems, despite some pioneering experiments, coherent control has not been widely used. This is partially due to the complex band structures and partially because transport research has tended to be more easily applicable to the solid-state realm. Topological materials, however, are especially promising candidates for coherent control, because (a) it has proven hard to access properties related to the topology in 3D materials via transport, and (b) topological bands are associated with unique optical selection rules, and as recently revealed – fascinating nonlinear optical phenomena. In this project I will develop nonlinear coherent control of photocurrents in topological materials, thus building a bridge between nonlinear control to transport measurements of topological bands. I will use time-resolved ARPES – a powerful tool providing band-imaging out of equilibrium – to enable imaging of the photocurrents within the topological bands. PhotoTopoCurrent will establish a new research direction, which will provide a deep understanding of the unique optical couplings and nonlinear optical responses of topological electronic bands, allow us to develop sophisticated optical schemes for tailored control, and finally implement them in transport devices.

Mots-clés : topological materials,photocurrent,ultrafast,trarpes
Projet ERC : LearningNetworks (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2110000.0 €

A central hypothesis in neuroscience is that changes in connectivity patterns between neurons support learning and memory formation. Most methods for examining connectivity between individual neurons rely on ex vivo experiments (e.g., in brain slices). However, in vivo measurements are required to study how neurons causally influence each other's activity ('causal connectivity') in the living brain, and how these causal interactions change over time. Thus, while brain networks are among the most studied biological networks, the cellular-level patterns and dynamics of causal connectivity in vivo remain unknown. Here, I propose to study how causal connectivity between individual neurons and across entire brain areas changes over time during learning of memory-guided behaviors. To this end, we will use novel causal optical methods to longitudinally map causal connectivity and neural activity at cell resolution in vivo, focusing on the motor cortex and related areas. We will combine these methods with a novel goal-directed behavior in mice that does not require pretraining, which will serve as a baseline to study learning mechanisms of more complex behaviors that rely on short-term memory. Specifically, we will map changes in causal connectivity during learning within the motor cortex (Aim 1) and across cortical areas (Aim 2) and relate it to the computational functions of the network. We will also perturb neurons based on their connectivity and coding properties to identify changes in network mechanisms for short-term memory and action selection at various learning stages (Aim 3). Finally, we will work towards identifying constraints on memory formation via optogenetic induction of artificial connectivity patterns. Taken together, this research will enable for the first time to causally study dynamics in network interactions across time on different spatial scales, and to test fundamental mechanisms of memory formation and representation in cortical networks.

Mots-clés : network connectivity,short term memory,optogenetic perturbations,memory formation,learning,effective connectivity,causal connectivity,motor cortex,attractor dynamics,action selection,decision making
Projet ERC : PetrIUS (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Université Adam-Mickiewicz de Poznań — Pays : Pologne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Between the 1460s and 1620s, printed editions of canon and civil law texts witnessed a constant development of marginal paratexts, such as glosses, summaries, cases or commentaries. They were added to authoritative legal sources because the printers were interested in improving the quality and attractiveness of new source editions. They often hired legal experts who offered them new tools, resources, and ideas. These were incorporated into printed books using new printing techniques. In consequence, the dynamic between improved new editions and advances in printing enterprise fueled the ongoing development of printed legal books. Various questions concerning the origin, transmission, and influence of paratexts are embedded in the interplay between sources and marginalia, manuscripts and printing, editors and printers, and books and their readers. PetrIUS has an innovative approach for shifting the focus from source (text) to marginalia (paratexts). PetrIUS aims to examine how print and its evolution helped to petrify – that is, consolidate – landmark achievements of late medieval ius commune. This is done by enabling some doctrinal accounts to be transferred into marginalia of source editions. The novelty brought about by print affected legal science and communication. PetrIUS will acknowledge how legal experts employed novel technical tools to transfer the heritage of medieval law into early modern source editions and how these processes affected patterns of scientific work and the transfer of knowledge in academia. To do so, PetrIUS will implement typical legal and book history tools as well as natural language processing techniques. The latter enables detailed comparisons between paratexts’ printed editions and their reception in the scholarly literature. The research will conclude with a legal theoretical study that assesses the early modern significance of paratexts by treating them as the media of law, that is, the means of legal communication.

Mots-clés : legal history,early modern history of canon and roman law,book history,paratexts,marginalia,natural language processing techniques in humanities
Projet ERC : DynOpt (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1400000.0 €

Dynamic graph algorithms are of increasing critical importance. They are crucial for coping with dynamic networks, which model the ever-changing physical world, and have been instrumental in achieving numerous major breakthroughs in static graph algorithms. The holy grail in the field of dynamic graph algorithms has been to design algorithms with poly-logarithmic (in the input size) update time. However, recent exciting developments, in which the PI has played a central role, aim to push the update time toward an absolute constant independent of the input size – which is qualitatively very different than a poly-log bound. This goal is of fundamental importance not just from a theoretical perspective, but also from a practical viewpoint, due to the rapidly growing size of modern networks. An algorithm is intrinsically optimal if its update time matches the ratio of the problem’s static time complexity to the input size. The main question underlying this research is: Which graph problems admit intrinsically optimal update time? Only few intrinsically optimal graph algorithms are known. The unique goal of this project is to establish a systematic study of intrinsically optimal algorithms. We will also study provably optimal algorithms, aiming to advance our understanding of the thin line that separates these two distinct optimality notions. To achieve this goal, we must go far beyond the current state-of-the-art, and in particular, confront some of the most central problems in the field. Meeting the project’s main goal, even partially, will be groundbreaking. Results of this project will facilitate the use of dynamic algorithms in real-world application domains, and will also be illuminating to other fields, such as distributed computing and fine-grained complexity. Consequently, we believe this research has the potential of revolutionizing the field of dynamic graph algorithms, and impacting related fields, thus enriching the general landscape of computer science.

Mots-clés : dynamic graph,dynamic algorithm,optimal algorithm,distributed computing,lower bound
Projet ERC : PolyAntiTumor (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1812500.0 €

Adoptive T cell therapies are a new class of living drugs, achieving durable results in a subset of patients with aggressive malignancies. These transformative outcomes are not shared with the majority of patients with solid tumors that remain resistant to current T cell therapies. As engineered T cell therapy is usually directed against a single antigen, it is especially vulnerable to antigen loss as a tumor resistance mechanism. Moreover, cancer immunotherapy often leads to severe immune-related adverse events (irAE) by destructive self-reactivity that must be evaluated together with the therapeutic benefit. While T cell therapies with tumor-infiltrating lymphocytes might circumvent these shortcomings, tumor tissue availability is limited and T cells are poorly responsive to ex-vivo perturbation. These therapeutic challenges highlight the gaps in our knowledge of how to engineer curative anti-tumor immunity. We recently developed foundational platforms for CRISPR engineering, TCR repertoire manipulation, and single-cell omics of primary human T cells. We plan to leverage these opportune achievements to address the critical gaps in adoptive T cell therapies. We will focus on three important aspects of engineered anti-tumor immunity: efficacy, safety, and specificity. We will tune TCR sensitivity by perturbing key genes to determine how TCR signaling balances burst-like effector function and long-term persistence. We will also reveal the sequestered self-reactive T cell compartment to control for irAE following immunotherapy. Finally, we will directly uncouple anti-tumor TCR repertoires from their dysfunctional state to mount a polyclonal anti-tumor immune response. This strategy is radically different from current T cell therapies as it is agnostic to specific tumor antigens and leverages pre-existing polyclonal antitumor immunity. These studies will chart novel blueprints for robust, safe, and specific engineered cell therapies targeting solid tumors.

Mots-clés : engineered t cell therapy,car t cells,tcr repertoire,synthetic immunology,self reactive t cells
Projet ERC : DeepSPIM (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Lausanne — Pays : Suisse — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1675683.0 €

Progress in deep learning brings new tools and methods that start reshaping pathology practice and advancing research in oncology. As a postdoctoral researcher in the Mahmood Lab (Harvard Medical School, 2022–), I played a pivotal role in the development and evaluation of “foundation models” for pathology – general-purpose models that can be used for various downstream tasks. Despite advances, foundation models still face several key limitations that restrict their widespread adoption. First, these models are designed to provide “non-human interpretable” data representation, providing little actionable insights to practitioners. Moreover, they lack robust language interfacing capabilities, especially in providing quantitative information. Finally, they are predominantly focused on hematoxylin & eosin (H&E) staining, leaving other histopathology modalities largely untouched. In this context, I hypothesize that foundation models for pathology will remain limited in impact unless they incorporate key additional features: (a) off-the-shelf structure segmentation and phenotyping for downstream analysis, (b) interactive language interfacing capable of answering quantitative queries, and (c) expanded support for other modalities, e.g., immunohistochemistry. To bring these ideas to life, I introduce DeepSPIM. In DeepSPIM, I will semi-automate data labeling to train segmentation models that will be combined with large language modeling to build a language interface. I will evaluate the translational capabilities of the models in assisting pathologists with routine clinical tasks, and, exploring interpretable morphological correlates of treatment response in oncology. The goals of DeepSPIM are closely aligned with the technical skills I developed throughout my PhD and postdoc. Leveraging my mentoring experience, professional network, and support of the University of Lausanne and the ERC Program, I aim to establish an independent research group in computational pathology.

Mots-clés : computational pathology ai for pathology