A longstanding question in the design of programming languages is how to balance safety and control. C-like languages give programmers low-level control over resource management at the expense of safety, whereas Java-like languages give programmers safe high-level abstractions at the expense of control. Rust is a new language developed at Mozilla Research that marries together the low-level flexibility of modern C++ with a strong "ownership-based" type system guaranteeing type safety, memory safety, and data race freedom. As such, Rust has the potential to revolutionize systems programming, making it possible to build software systems that are safe by construction, without having to give up low-level control over performance. Unfortunately, none of Rust's safety claims have been formally investigated, and it is not at all clear that they hold. To rule out data races and other common programming errors, Rust's core type system prohibits the aliasing of mutable state, but this is too restrictive for implementing some low-level data structures. Consequently, Rust's standard libraries make widespread internal use of unsafe blocks, which enable them to opt out of the type system when necessary. The hope is that such unsafe code is properly encapsulated, so that Rust's language-level safety guarantees are preserved. But due to Rust's reliance on a weak memory model of concurrency, along with its bleeding-edge type system, verifying that Rust and its libraries are actually safe will require fundamental advances to the state of the art. In this project, we aim to equip Rust programmers with the first formal tools for verifying safe encapsulation of unsafe code. Any realistic languages targeting this domain in the future will encounter the same problem, so we expect our results to have lasting impact. To achieve this goal, we will build on recent breakthrough developments by the PI and collaborators in concurrent program logics and semantic models of type systems.

The development of a multicellular organism requires the precise control of gene expression in space and time so that cells adopt their correct identity. However, genetic mutations can alter this complex process. Recently, transcriptional adaptation (TA) has been uncovered as one of the mechanisms underlying genetic compensation in zebrafish, mouse cells in culture, and Caenorhabditis elegans. TA refers to the phenomenon by which mutated genes (often with mRNA-destabilizing mutations) trigger the transcriptional modulation of related genes, called adapting genes. However, little is known about the spatial and temporal characteristics of adapting gene regulation and particularly during the zygotic genome activation. This project aims to decipher when and where TA occurs during early zebrafish development. Using genome engineering followed by live imaging, high-resolution microscopy and quantitative analysis, I will test the hypothesis that TA is regulated in a temporal manner during zygotic genome activation and that there is a specific mode of transcription during the modulation of the adapting genes (i.e., linear/discontinuous). Furthermore, I will investigate the subcellular localization of mutant mRNA degradation as well as the heterogeneity of the TA response between embryonic cells. Finally, I will implement the live imaging of translation in zebrafish embryo to decipher whether the dynamics of translation is involved during the TA/genetic compensation process. Until now, TA has been mostly investigated on pooled populations of cells. Therefore, we lack the understanding of this phenomenon at the single cell level. This project aims to fill this gap and obtain a better understanding of the spatio-temporal characteristics of genetic compensation which aid in the robustness of vertebrate development.

Following a recent paradigm shift in neuroscience, the brain is often thought to generate active predictions of its environment. Neural predictions of temporal information, in particular, are a current research frontier. A key challenge is to provide a unifying mechanistic explanation of neural expectations ranging from microscopic electrophysiology, via models of interacting populations of neurons, to macroscopic signals observed non-invasively. Auditory and speech perception are the optimal testing ground for studying temporal dynamics of predictive processing in the brain, as most auditory signals only gain meaning as temporal sequences. This project aims at (1) providing a mechanistic model explaining how temporal expectations influence neural activity in auditory cortex across species (rats and humans), and (2) test whether the mechanisms established for simple auditory stimuli generalise to rich, complex and hierarchical structures characteristic of speech stimuli. These research objectives will be addressed in a coherent programme based on well-controlled psychoacoustic and psycholinguistic experiments, combining extracellular electrophysiology in rats and neuroimaging (magneto- and encephalography) in humans with novel analytic tools. By integrating knowledge from disparate fields such as systems-level neuroscience and psycholinguistics, the project will yield novel and unique evidence testing the generality (across species and stimulus domains) and specificity (regarding the proposed neural mechanisms) of the influential theories construing the brain as an inherently predictive organ. Besides its immediate research objectives, the current project is optimised to provide me with training necessary to reach professional independence, complementing my interdisciplinary expertise in an innovative research field across different species and cognitive domains, and consolidating my project coordination, management and leadership abilities.