ERC FUNDED PROJECTS

Discovering the causal mechanisms of a complex system of interacting components is necessary in order to control it. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations under certain conditions from observational data alone or with a limited number of interventions/manipulations. An important, challenging biological problem that may take decades of experimental work is the induction of biological cellular pathways; pathways are informal causal models indispensable in biological research and drug design. Recent exciting advances in flow/mass cytometry biotechnology allow the generation of large-sample datasets containing measurements on single cells, thus setting the problem of pathway learning suitable for CD methods. CAUSALPATH builds upon and further advances recent breakthrough developments in CD methods to enable the induction of biological pathways from cytometry and other omics data. As a testbed problem we focus on the differentiation of human T-cells; these are involved in autoimmune and inflammatory diseases, as well as cancer and thus, are targets of new drug development for a range of chronic diseases. The biological problem acts as our campus for general novel formalisms, practical algorithms, and useful tools development, pointing to fundamental CD problems: presence of feedback cycles, presence of latent confounding variables, CD from time-course data, Integrative Causal Analysis (INCA) of heterogeneous datasets and others. Three features complement CAUSALPATH’s approach: (A) methods development will co-evolve with biological wet-lab experiments periodically testing the algorithmic postulates, (B) Open-source tools will be developed for the non-expert, and (C) Commercial exploitation of the results will be sought out. CAUSALPATH brings together an interdisciplinary team, committed to this vision. It builds upon the PI’s group recent important results on INCA algorithms.

1 724 000 €

Start date: 2015-01-01, End date: 2019-12-31

The recent IPCC report identifies mineral dust and the associated uncertainties in climate projections as key topics for future research. Dust size distribution in climate models controls the dust-radiation-cloud interactions and is a major contributor to these uncertainties. Observations show that the coarse mode of dust can be sustained during long-range transport, while current understanding fails in explaining why the lifetime of large airborne dust particles is longer than expected from gravitational settling theories. This discrepancy between observations and theory suggests that other processes counterbalance the effect of gravity along transport. D-TECT envisages filling this knowledge gap by studying the contribution of the triboelectrification (contact electrification) on particle removal processes. Our hypothesis is that triboelectric charging generates adequate electric fields to hold large dust particles up in the atmosphere. D-TECT aims to (i) parameterize the physical mechanisms responsible for dust triboelectrification; (ii) assess the impact of electrification on dust settling; (iii) quantify the climatic impacts of the process, particularly the effect on the dust size evolution during transport, on dry deposition and on CCN/IN reservoirs, and the effect of the electric field on particle orientation and on radiative transfer. The approach involves the development of a novel specialized high-power lidar system to detect and characterize aerosol particle orientation and a large-scale field experiment in the Mediterranean Basin using unprecedented ground-based remote sensing and airborne in-situ observation synergies. Considering aerosol-electricity interactions, the observations will be used to improve theoretical understanding and simulations of dust lifecycle. The project will provide new fundamental understanding, able to open new horizons for weather and climate science, including biogeochemistry, volcanic ash and extraterrestrial dust research.

1 968 000 €

Start date: 2017-09-01, End date: 2022-08-31

The objective is to develop a robust multifunctional framework/probe for capturing the evolution of deformation and failure in a variety of processes at the micro-nano transition regime. An interdisciplinary approach will be pursued based on fundamental theory and experiment, in conjunction with multiscale simulations for micro/nanotechnology applications. The approach is unconventional as it ventures to extend continuum mechanics down to the micro/nano regime and verify this through nanoindentation and atomic force microscopy techniques. It is also unique as the new phenomenology introduced for establishing this extension (higher order gradients accounting for microscopic processes and interfacial energy terms accounting for nanoscopic phenomena) will be substantiated through hybrid (ab initio-atomistic-defect-finite element) simulations. The framework will be employed to consider fracture and size effects in a number of micro-nano scale transition configurations ranging from nanograined aggregates and nanolayered structures to nanotubes and micropillars, and from Li-ion battery electrodes to bioactive interfaces. Other micro/nano objects such as quantum dots, nanowires and NEMS/MEMS devices, as well as biomolecular microcrystalline membranes leading to living cell division will be considered. In a sense this “scale” transition theory is reminiscent in scope to Landau’s “phase” transition theory where a variety of different physical phenomena can be treated within a common framework. This optimism stems from the PI’s previous success with this approach, as well as Smalley’s remark that the “laws of continuum mechanics are amazingly robust for treating even intrinsically discrete objects only a few atoms in diameter”. A good mix of young researchers and mature scholars will be employed, thus connecting people and ideas through joint publications and scholarly activities in a critical area of fundamental and applied research.

1 128 400 €

Start date: 2008-10-01, End date: 2013-09-30

Advances in sensor networking and the availability of every day, low-cost sensor enabled devices has led to integrating sensors to instrument the physical world in a variety of economically vital sectors of agriculture, transportation, healthcare, critical infrastructures and emergency response. At the same time, social computing is now undergoing a major revolution: social networks, as exemplified by Twitter or Facebook, have significantly changed the way humans interact with one another. We are now entering a new era where people and systems are becoming increasingly integrated and this development is effectively leading us to large-scale mobile human-centered systems. Our goal is to develop a comprehensive framework to simplify the development of mobile human-centered systems, as well as make them predictable and reliable. Our work has the following research thrusts: First, we develop techniques for dealing efficiently with dynamic unpredictable factors that such complex systems face, including dynamic workloads, unpredictable occurrence of events, real-time demands of applications, as well as user changes and urban dynamics. To achieve this, we will investigate the use of mathematical models to control the behavior of the applications in the absence of perfect system models and a priori information on load and human usage patterns. Second, we will develop the foundations needed to meet the end-to-end timeliness and reliability demands for the range of distributed systems that we will consider by developing novel techniques at different layers of the distributed environment and studying the tradeoffs involved. Third, we will develop general techniques to push computation and data storage as much as possible to the mobile devices, and to integrate participatory sensing and crowdsourcing techniques. The outcome of the proposed work is expected to have significant impact on a wide variety of distributed systems application domains.

960 000 €

Start date: 2013-03-01, End date: 2019-02-28