ERC FUNDED PROJECTS

"Understanding structure-property relationships across lengthscales is key to the design of functional and structural materials and devices. Moreover, the complexity of modern devices extends to three dimensions and as such 3D characterization is required across those lengthscales to provide a complete understanding and enable improvement in the material’s physical and chemical behaviour. 3D imaging and analysis from the atomic scale through to granular microstructure is proposed through the development of electron tomography using (S)TEM, and ‘dual beam’ SEM-FIB, techniques offering complementary approaches to 3D imaging across lengthscales stretching over 5 orders of magnitude. We propose to extend tomography to include novel methods to determine atom positions in 3D with approaches incorporating new reconstruction algorithms, image processing and complementary nano-diffraction techniques. At the nanoscale, true 3D nano-metrology of morphology and composition is a key objective of the project, minimizing reconstruction and visualization artefacts. Mapping strain and optical properties in 3D are ambitious and exciting challenges that will yield new information at the nanoscale. Using the SEM-FIB, 3D ‘mesoscale’ structures will be revealed: morphology, crystallography and composition can be mapped simultaneously, with ~5nm resolution and over volumes too large to tackle by (S)TEM and too small for most x-ray techniques. In parallel, we will apply 3D imaging to a wide variety of key materials including heterogeneous catalysts, aerospace alloys, biomaterials, photovoltaic materials, and novel semiconductors. We will collaborate with many departments in Cambridge and institutes worldwide. The personnel on the proposal will cover all aspects of the tomography proposed using high-end TEMs, including an aberration-corrected Titan, and a Helios dual beam. Importantly, a postdoc is dedicated to developing new algorithms for reconstruction, image and spectral processing."

2 337 330 €

Start date: 2012-01-01, End date: 2017-12-31

Algorithms for Complex Collective Decisions on Structured Domains. The aim of this proposal is to substantially advance the field of Computational Social Choice, by developing new tools and methodologies that can be used for making complex group decisions in rich and structured environments. We consider settings where each member of a decision-making body has preferences over a finite set of alternatives, and the goal is to synthesise a collective preference over these alternatives, which may take the form of a partial order over the set of alternatives with a predefined structure: examples include selecting a fixed-size set of alternatives, a ranking of the alternatives, a winner and up to two runner-ups, etc. We will formulate desiderata that apply to such preference aggregation procedures, design specific procedures that satisfy as many of these desiderata as possible, and develop efficient algorithms for computing them. As the latter step may be infeasible on general preference domains, we will focus on identifying the least restrictive domains that enable efficient computation, and use real-life preference data to verify whether the associated restrictions are likely to be satisfied in realistic preference aggregation scenarios. Also, we will determine whether our preference aggregation procedures are computationally resistant to malicious behavior. To lower the cognitive burden on the decision-makers, we will extend our procedures to accept partial rankings as inputs. Finally, to further contribute towards bridging the gap between theory and practice of collective decision making, we will provide open-source software implementations of our procedures, and reach out to the potential users to obtain feedback on their practical applicability.

1 395 933 €

Start date: 2015-07-01, End date: 2020-06-30

The epidemiology of alcohol use and related health consequences plays a vital role by monitoring populations’ alcohol consumption patterns and problems associated with drinking. Such studies seek to explain mechanisms linking consumption to harm and ultimately to reduce the health burden. Research needs to consider changes in drinking behaviour over the life-course. The current evidence base lacks the consideration of the complexity of lifetime consumption patterns, the predictors of change and subsequent health risks. Aims of the study 1. To describe age-related trajectories of drinking in different settings and to determine the extent to which individual and social contextual factors, including socioeconomic position, social networks and life events influence drinking pattern trajectories. 2. To estimate the impact of drinking trajectories on physical functioning and disease and to disentangle the exposure-outcome associations in terms of a) timing, i.e. health effect of drinking patterns in early, mid and late life; and b) duration, i.e. whether the impact of drinking accumulates over time. 3. To test the bidirectional associations between health and changes in consumption over the life-course in order to estimate the relative importance of these effects and to determine the dominant temporal direction. 4. To explore mechanisms and pathways through which drinking trajectories affect health and functioning in later life and to examine the role played by potential effect modifiers of the association between drinking and poor health. Several large, longitudinal cohort studies from European countries with repeated measures of alcohol consumption will be combined and analysed to address the aims. A new team will be formed consisting of the PI, a Research Associate and two PhD students. Dissemination will be through journals, conferences, and culminating in a one-day workshop for academics, practitioners and policy makers in the alcohol field.

1 032 815 €

Start date: 2013-01-01, End date: 2017-12-31

The objective of this proposal is to bring together a local team of young researchers who will work closely with international collaborators to advance the state of the art of Algorithmic Game Theory and open new venues of research at the interface of Computer Science, Game Theory, and Economics. The proposal consists mainly of three intertwined research strands: algorithmic mechanism design, price of anarchy, and online algorithms. Specifically, we will attempt to resolve some outstanding open problems in algorithmic mechanism design: characterizing the incentive compatible mechanisms for important domains, such as the domain of combinatorial auctions, and resolving the approximation ratio of mechanisms for scheduling unrelated machines. More generally, we will study centralized and distributed algorithms whose inputs are controlled by selfish agents that are interested in the outcome of the computation. We will investigate new notions of mechanisms with strong truthfulness and limited susceptibility to externalities that can facilitate modular design of mechanisms of complex domains. We will expand the current research on the price of anarchy to time-dependent games where the players can select not only how to act but also when to act. We also plan to resolve outstanding questions on the price of stability and to build a robust approach to these questions, similar to smooth analysis. For repeated games, we will investigate convergence of simple strategies (e.g., fictitious play), online fairness, and strategic considerations (e.g., metagames). More generally, our aim is to find a productive formulation of playing unknown games by drawing on the fields of online algorithms and machine learning.

2 461 000 €

Start date: 2013-04-01, End date: 2019-03-31