Samuele Lo Piano

Bio: Samuele Lo Piano is an academic researcher from University of Reading. The author has contributed to research in topics: Sensitivity (control systems) & Medicine. The author has an hindex of 12, co-authored 29 publications receiving 532 citations. Previous affiliations of Samuele Lo Piano include Open University of Catalonia & University of Pisa.

Five ways to ensure that models serve society: a manifesto.

Abstract: Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Abstract: Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Abstract: Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.

Quantitative Storytelling in the Making of a Composite Indicator

Abstract: The reasons for and against composite indicators are briefly reviewed, as well as the available theories for their construction. After noting the strong normative dimension of these measures—which ultimately aim to ‘tell a story’, e.g. to promote the social discovery of a particular phenomenon, we inquire whether a less partisan use of a composite indicator can be proposed by allowing more latitude in the framing of its construction. We thus explore whether a composite indicator can be built to tell ‘more than one story’ and test this in practical contexts. These include measures used in convergence analysis in the field of cohesion policies and a recent case involving the World Bank’s Doing Business Index. Our experiments are built to imagine different constituencies and stakeholders who agree on the use of evidence and of statistical information while differing on the interpretation of what is relevant and vital.

Measuring statistical dependence with Hilbert-Schmidt norms

Abstract: We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Lanthanide probes for bioresponsive imaging.

Abstract: The chemistry of the less familiar elements is a fascinating topic especially for the inorganic minded. The lanthanides, or rare earths, comprise the 5d block of the periodic table and represent a huge array of applications from catalysis to lasers, and of course, imaging agents.1 Recent advances in luminescence and magnetic resonance microscopy have, in part, been stimulated by extraordinary success in the development of new lanthanide probes. The unique properties of the lanthanides provide for a deep tool chest for the chemist, biologist and the imaging scientist to exploit, and that exploitation is in full swing. In this review we focus on two classes of lanthanide probes that are subsets of the larger area of metalloimaging: luminescent and magnetic lanthanides. In Section 2 we discuss the general design and photophysical properties of lanthanides and how these parameters are tuned to develop bioresponsive probes for optical imaging. In Section 3 we provide a brief description of how MR images are acquired and the how MRI contrast agents are engineered to respond to biological events of interest. These guiding principles have driven research that has produced a truly diverse number of new agents that are target specific and bioresponsive (or bioactivatable). While other imaging modalities utilize lanthanide-based probes, these topics are beyond the scope of this review. We direct the reader to explore some excellent reviews in the important areas of radiometals and multimodal imaging.2–5

Lanthanide complexes as chiral probes exploiting circularly polarized luminescence

Abstract: Optical probes that provide information about local chirality have been developed based on changes to the circular polarisation of emitted light. Highly emissive complexes of lanthanide ions are ideally suited for CPL spectroscopy and the design criteria for developing such probes are defined, based on a rigorous stereochemical analysis. The perturbation of a dynamically racemic complex may occur either by a change in complex constitution or by non-covalent association. With complexes of enantiopure ligands, perturbation may involve either dynamic helicity inversion or a reversible change in the lanthanide coordination environment.