Physics and Astronomy »

Centre for Doctoral Training in Data Intensive Science

26 Sep 2023

Seminars 2020

16-11-2020 : Prof. Ulrich Kerzel, IUBH University of Applied Sciences

Data science & AI in the "real world"

Data science and AI have seen a huge growth and many successes in the past decade. However, using these methods to create value in a commercial setting requires a diverse skill set, most of which is not taught at universities. In this talk, we'll explore what we mean by "data science and AI" in the private sector, how to set a project, why they often fail - and what graduates from engineering and physics find most challenging when they leave academia to join a company in this area.

19-10-2020 @ 12pm: Caterina La Porta and Stefano Zapperi (University of Milan)

Estimating individual susceptibility to Sars-CoV-2 in human subpopulations using artificial neural networks

The response to SARS-CoV-2 infection differs from person to person, with some patients developing more severe symptoms than others. The reasons for the observed differences in the severity of the Covid-19 disease are mostly still unknown. When a cell is infected by a virus, it exposes on its surface fragments of the viral proteins, or peptides, in association with HLA molecules. There are two classes of HLA molecules: class I and class II. HLA class I molecules are exposed on the surface of all the nucleated cells and trigger the activation of T cells which then destroy the infected cell. HLA molecules differ from individual to individual and so does their ability to bind viral fragments and expose them on the cell surface. In this talk we show that artificial neural networks (ANN) can be used to analyze the binding of SARS-CoV-2 peptides with HLA class I molecules. The ANN was first trained with experimentally known binding affinities of peptide-HLA pairs and then used to predict the binding of Sars-CoV-2 peptides. In this way, we identify two sets of HLA molecules present in specific human populations: the first set displays weak binding with SARS-Cov-2 peptides, while the second shows strong binding and T cell propensity.

01-07-2020 : Prof. Mirco Musolesi (UCL), a Turing Fellow at the Alan Turing Institute

Sensing and Modelling Human Behaviour and Emotional States using Mobile Devices

Zoom ID: 346 266 5332

Today's mobile phones are far from mere communication devices they were just ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. Information about users’ behaviour can also be gathered by means of wearables and IoT devices as well as by sensors embedded in the fabric of our cities. Inference is not only limited to physical context and activities, but in the recent years mobile phones have been increasingly used to infer users' emotional states. The applications of these techniques are several, from positive behavioural intervention to more natural and effective human-mobile device interaction. In this talk I will discuss the work of my lab in the area of mobile sensing for modelling and predicting human behaviour and emotional states. I will present our ongoing projects in the area of mobile systems for mood monitoring and mental health. In particular, I will show how mobile phones can be used to collect and analyse mobility patterns of individuals in order to quantitatively understand how mental health problems affect their daily routines and behaviour and how potential changes in mood can be automatically detected from sensor data in a passive way. Finally, I will discuss our research directions in the broader area of anticipatory mobile computing, outlining the open challenges and opportunities.

03-04-2020 : Ciarán Lee (Babylon Health)

Causal Inference in Healthcare

Causal reasoning is vital for effective reasoning in science and medicine. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations---unlike correlations---allow one to reason about the consequences of possible treatments. However, all previous approaches to machine-learning assisted diagnosis, including deep learning and model-based Bayesian approaches, learn by association and do not distinguish correlation from causation. I will show that these approaches systematically lead to incorrect diagnoses. I will outline a new diagnostic algorithm, based on counterfactual inference, which captures the causal aspect of diagnosis overlooked by previous approaches and overcomes these issues. I will additionally describe recent algorithms from my group which can discover causal relations from uncontrolled observational data and show how these can be applied to facilitate effective reasoning in medical settings such as deciding how to treat certain diseases.

13-02-2020 : Elena Cuoco (Pisa U)

Gravitational Wave science and Machine Learning

In the recent years, Machine and Deep learning techniques approaches have been introduced and tested for solving problems in astrophysics. In Gravitational Wave science many teams in the LIGO-Virgo collaboration have experimented, on simulated data or on real data of LIGO and Virgo interferometers, the power and capabilities of machine learning algorithms both for the detector noise characterization and gravitational wave astrophysical signals. The cost action CA17137 (g2net) aims to create an interdisciplinary network of Machine Learning and Gravitational Waves experts and to create collaborating teams to solve some of the problems of gravitational wave science using Machine Learning. In this seminar, I will show some of the results of the application of Machine Learning in the LIGO-Virgo collaboration and in the CA1737 cost action, dedicated to the analysis of data from gravitational wave experiments.

23-01-2020, : Howard Bowman (Kent U and Birmingham U)

Uses (and Abuses) of Machine Learning in Cognitive and Clinical Neuroscience

Machine learning has become extremely popular in cognitive neuroscience, and may be on the verge of impacting clinical applications of neuroimaging. Such methods offer the prospect to greatly increase the statistical and explanatory power available to the field. The plan for this talk is to illustrate how machine learning is being used in cognitive and clinical neuroscience, thereby highlighting its promise, as well as some potential pitfalls. I will illustrate a number of machine learning approaches, including, a time-oriented method, called temporal generalisation, a classic spatial analysis: multivariate lesion-deficit mapping in stroke and, if time allows, decoding methods, which are being applied to determining what is in a subjects mind at a particular point. While important findings are being made with these approaches, they are not always being applied completely robustly. Neuroimaging data sets are characterised by being very high dimensional, e.g. hundreds of thousands or millions of measurement units, such as, voxels, with (often non-stationary) smoothness in space and time. Additionally, one is typically trying to identify regions of this volume that underlie a particular classification or prediction (i.e. localisation of function is important). This means that machine learning methods need to be carefully embedded within statistical inference that controls for multiple comparisons (so called, family wise error correction), with consideration of the possibility of overfitting at the level of parameters and hyper-parameters. In particular, it may be that at the very point when the replication crisis in traditional experimental psychology is being addressed, machine learning is being applied in a fashion that inflates false positive (i.e. type-I error) rates [Skocik et al, 2016]. Additionally, interpretability of classification or prediction is critical for clinical uptake, limiting the applicability of some very powerful learning algorithms that effectively provide black-box solutions. Accordingly, there is also interest in methods that fit Bayesian graphs to data. Reference: L Skocik, M., Collins, J., Callahan-Flintoft, C., Bowman, H., & Wyble, B. (2016). I tried a bunch of things: the dangers of unexpected overfitting in classification. BioRxiv, 078816.