ERC FUNDED PROJECTS

In this project I propose to take advantage of the enormous potential created by the recent material science revolution based on two-dimensional (2D) layered materials, by bringing it to the arena of nanoscale heat transport, where heat transport occurs on ultrafast timescales. This opens up a new research field of controllable ultrafast heat transport in layered materials. In particular, I will take advantage of the myriad of possibilities for miniature material and device design, with unprecedented controllability and versatility, offered by Van der Waals (VdW) heterostructures – stacks of different layered materials assembled on top of each other – and 1D systems of layered materials. Specifically, I will introduce novel device geometries based on VdW heterostructures for passively and actively controlling phonon modes and thermal transport. This will be measured mainly using time-domain thermoreflectance measurements. I will also develop novel time-resolved measurement techniques to follow heat spreading and coupling between different heat carriers: light, phonons, and electrons. These techniques will be mainly based on time-resolved infrared/Raman spectroscopy and photocurrent scanning microscopy. Moreover, I will study one-dimensional layered materials and assess their thermoelectric properties using electrical measurements. And finally, I will combine these results into hybrid devices with a photoactive layer, in order to demonstrate how phonon control allows for tuning of electrical and optoelectronic properties. The results of this project will have an impact on the major research fields of phononics, electronics and photonics, revealing novel physical phenomena. Additionally, the results are likely to be useful towards applications such as thermal management, thermoelectrics, photovoltaics and photodetection.

1 475 000 €

Start date: 2018-12-01, End date: 2023-11-30

Mobile health is becoming the holy grail for affordable medical diagnostics. It has the potential of associating human behaviour with medical symptoms automatically and at early disease stage; it also offers cheap deployment, reaching populations generally not able to afford diagnosis and delivering a level of monitoring so fine which will likely improve diagnostic theory itself. The advancements of technology offer new ranges of sensing and computation capability with the potential of further improving the reach of mobile health. Audio sensing through microphones of mobile devices has recently being recognized as a powerful and yet underutilized source of medical information: sounds from the human body (e.g., sighs, breathing sounds and voice) are indicators of disease or disease onsets. The current pilots, while generally medically grounded, are potentially ad-hoc from the perspective of key areas of computer science; specifically, in their approaches to computational models and how the system resource demands are optimized to fit within the limits of the mobile devices, as well as in terms of robustness needed for tracking people in their daily lives. Audio sensing also comes with challenges which threaten its use in clinical context: its power hungry nature and the fact that audio data is very sensitive and the collection of this sort of data for analytics violates obvious ethical rules. This work proposes models to link sounds to disease diagnosis and to deal with the inherent issues raised by in-the-wild sensing: noise and privacy concerns. We exploit these audio models in wearable systems maximizing the use of local hardware resources with power optimization and accuracy in both near real time and sparse audio sampling. Privacy will arise as a by-product taking away the need of cloud analytics. Moreover, the framework will embed the ability to quantify the diagnostic uncertainty and consider patient context as confounding factors via additional sensors.

2 493 724 €

Start date: 2019-10-01, End date: 2024-09-30

Geometric frustration, namely the impossibility of satisfying competing interactions on a lattice, has recently become a topic of considerable interest as it engenders emergent, fundamentally new phenomena and holds the exciting promise of delivering a new class of nanoscale devices based on the motion of magnetic charges. With ENFORCE, I propose to realize two and three dimensional artificial colloidal ices and investigate the fascinating manybody physics of geometric frustration in these mesoscopic structures. I will use these soft matter systems to engineer novel frustrated states through independent control of the single particle positions, lattice topology and collective magnetic coupling. The three project work packages (WPs) will present increasing levels of complexity, challenge and ambition: (i) In WP1, I will demonstrate a way to restore the residual entropy in the square ice, a fundamental longstanding problem in the field. Furthermore, I will miniaturize the square and the honeycomb geometries and investigate the dynamics of thermally excited topological defects and the formation of grain boundaries. (ii) In WP2, I will decimate both lattices and realize mixed coordination geometries, where the similarity between the colloidal and spin ice systems breaks down. I will then develop a novel annealing protocol based on the simultaneous system visualization and magnetic actuation control. (iii) In WP3, I will realize a three dimensional artificial colloidal ice, in which interacting ferromagnetic inclusions will be located in the voids of an inverse opal, and arranged to form the FCC or the pyrochlore lattices. External fields will be used to align, bias and stir these magnetic inclusions while monitoring in situ their orientation and dynamics via laser scanning confocal microscopy. ENFORCE will exploit the accessible time and length scales of the colloidal ice to shed new light on the exciting and interdisciplinary field of geometric frustration.

1 850 298 €

Start date: 2020-01-01, End date: 2024-12-31

Machine learning is revolutionising computer science and AI. Much of its success is due to deep neural networks, which have demonstrated outstanding performance in perception tasks such as image classification. Solutions based on deep learning are now being deployed in real-world systems, from virtual personal assistants to self-driving cars. Unfortunately, the black-box nature and instability of deep neural networks is raising concerns about the readiness of this technology. Efforts to address robustness of deep learning are emerging, but are limited to simple properties and function-based perception tasks that learn data associations. While perception is an essential feature of an artificial agent, achieving beneficial collaboration between human and artificial agents requires models of autonomy, inference, decision making, control and coordination that significantly go beyond perception. To address this challenge, this project will capitalise on recent breakthroughs by the PI and develop a model-based, probabilistic reasoning framework for autonomous agents with cognitive aspects, which supports reasoning about their decisions, agent interactions and inferences that capture cognitive information, in presence of uncertainty and partial observability. The objectives are to develop novel probabilistic verification and synthesis techniques to guarantee safety, robustness and fairness for complex decisions based on machine learning, formulate a comprehensive, compositional game-based modelling framework for reasoning about systems of autonomous agents and their interactions, and evaluate the techniques on a variety of case studies. Addressing these challenges will require a fundamental shift towards Bayesian methods, and development of new, scalable, techniques, which differ from conventional probabilistic verification. If successful, the project will result in major advances in the quest towards provably robust and beneficial AI.

2 417 890 €

Start date: 2019-10-01, End date: 2024-09-30