ERC FUNDED PROJECTS

The field of electronics has been a veritable powerhouse of the economy, driving technological breakthroughs that affect all aspects of everyday life. Aside from silicon, there has been growing interest in developing a novel generation of electronic devices based on pi-conjugated polymers and oligomers. While their goal is not to exceed the performance of silicon technologies, they could enable far reduced fabrication costs as well as completely new functionalities (e.g. mechanical flexibility, transparency, impact resistance). The performance of these organic devices is greatly dependent on the organization and electronic structures of π-conjugated polymer chains at the molecular level. To achieve full potential, technological developments require fine-tuning of the relative orientation/position of the pi-conjugated moieties, which provide a practical means to enhance electronic properties. The discovery pace of novel materials can be accelerated considerably by the development of efficient computational schemes. This requires an integrated approach, based on which the structural, electronic, and charge transport properties of novel molecular candidates are evaluated computationally and predictions benchmarked by proof of principle experiments. This research program aims at developing a threefold computational screening strategy enabling the design of an emerging class of molecular precursors based on the insertion of π-conjugated molecules into self-assembled hydrogen bond aggregator segments (e.g. oligopeptide, nucleotide and carbohydrate motifs). These bioinspired functionalized pi-conjugated systems offer the highly desirable prospect of achieving ordered suprastructures abundant in nature with the enhanced functionalities only observed in synthetic polymers. A more holistic objective is to definitively establish the relationship between highly ordered architectures and the nature of the electronic interactions and charge transfer properties in the assemblies.

1 482 240 €

Start date: 2012-12-01, End date: 2017-11-30

Digital 3D content creation and modeling has become an indispensable part of our technology-driven society. Any modern design and manufacturing process involves manipulation of digital 3D shapes. Many industries have been long expecting ubiquitous 3D as the next revolution in multimedia. Yet, contrary to “traditional” media such as digital music and video, 3D content creation and editing is not accessible to the general public, and 3D geometric data is not nearly as wide-spread as it has been anticipated. Despite extensive geometric modeling research in the past two decades, 3D modeling is still a restricted domain and demands tedious, time consuming and expensive work effort even from trained professionals, namely engineers, designers, and digital artists. Geometric modeling is reported to constitute one of the lowest-productivity components of product life cycle. The major reason for 3D shape modeling remaining inaccessible and tedious is that our current geometry representation and modeling algorithms focus on low-level mathematical properties of the shapes, entirely missing structural, contextual or semantic information. As a consequence, current modeling systems are unintuitive, inefficient and difficult for humans to work with. We believe that instead of continuing on the current incremental research path, a concentrated effort is required to fundamentally rethink the shape modeling process and re-align research agendas, putting high-level shape structure and function at the core. We propose a research plan that will lead to intelligent digital 3D modeling tools that integrate semantic knowledge about the objects being modeled and provide the user an intuitive and logical response, fostering creativity and eliminating unnecessary low-level manual modeling tasks. Achieving these goals will represent a fundamental change to our current notion of 3D modeling, and will finally enable us to leverage the true potential of digital 3D content for society.

1 497 442 €

Start date: 2012-09-01, End date: 2017-08-31

"We address one of the fundamental challenges of our time: Acting effectively while facing a deluge of data. Massive volumes of data are generated from corporate and public sources every second, in social, scientific and commercial applications. In addition, more and more low level sensor devices are becoming available and accessible, potentially to the benefit of myriads of applications. However, access to the data is limited, due to computational, bandwidth, power and other limitations. Crucially, simply gathering data is not enough: we need to make decisions based on the information we obtain. Thus, one of the key problems is: How can we obtain most decision-relevant information at minimum cost? Most existing techniques are either heuristics with no guarantees, or do not scale to large problems. We recently showed that many information gathering problems satisfy submodularity, an intuitive diminishing returns condition. Its exploitation allowed us to develop algorithms with strong guarantees and empirical performance. However, existing algorithms are limited: they cannot cope with dynamic phenomena that change over time, are inherently centralized and thus do not scale with modern, distributed computing paradigms. Perhaps most crucially, they have been designed with the focus of gathering data, but not for making decisions based on this data. We seek to substantially advance large-scale adaptive decision making under partial observability, by grounding it in the novel computational framework of adaptive submodular optimization. We will develop fundamentally new scalable techniques bridging statistical learning, combinatorial optimization, probabilistic inference and decision theory to overcome the limitations of existing methods. In addition to developing novel theory and algorithms, we will demonstrate the performance of our methods on challenging real world interdisciplinary problems in community sensing, information retrieval and computational sustainability."

1 499 900 €

Start date: 2012-11-01, End date: 2017-10-31