How good is your prediction? In risk-sensitive applications, it is crucial to be able to assess the quality of a prediction, however, traditional classification and regression models don't provide their users with any information regarding prediction trustworthiness. In contrast, conformal classification and regression models associate each of their multi-valued predictions with a measure of statistically valid confidence, and let their users specify a maximal threshold of the model's error rate - the price to be paid is that predictions made with a higher confidence cover a larger area of the possible output space. This tutorial aims to provide its attendees with the knowledge necessary to implement conformal prediction in their daily data science work, be it research or practice oriented, as well as highlight current research topics on the subject.

Since its development the framework has been combined with many popular techniques, such as Support Vector Machines, k-Nearest Neighbours, Neural Networks, Ridge Regression etc., and has been successfully applied to many challenging real world problems, such as the early detection of ovarian cancer, the classification of leukaemia subtypes, the diagnosis of acute abdominal pain, the assessment of stroke risk, the recognition of hypoxia in electroencephalograms (EEGs), the prediction of plant promoters, the prediction of network traffic demand, the estimation of effort for software projects and the back calculation of non-linear pavement layer moduli. The framework has also been extended to additional problem settings such as semi-supervised learning, anomaly detection, feature selection, outlier detection, change detection in streams and active learning. The aim of this symposium is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between researchers on any aspect of Conformal Prediction and its applications.

Lars is mainly engaged in two newly started companies, Universal Prediction AB, involved in consulting services around AI and advanced analytics, and  RTHS AB, which is developing a product to measure various properties related to blood pulse waves e.g. blood pressure. Part of the product is heavily reliant on machine-learning methods. Other than this, Lars is also a visiting professor in computer science at Royal Holloway. In the period 2018-2021, Lars held a position as Head of AI within Stena Line and was leading all advanced initiatives within the Stena conglomerate.  Before that, Lars spent almost 15 years in AstraZeneca and participated both in strategic, scientific and management initiatives. Most of his efforts were spent on developing scientifically sound methods, such as conformal prediction, within the drug discovery phases. Lars holds a PhD in Naval Architecture and Ocean Engineering and Scientific Computing and he is a graduate of the Swedish National Graduate School in Scientific Computing. He also did part of his PhD at Lawrence Livermore National Laboratory. The main research objective was to enable fast solutions of the incompressible Navier-Stokes equations on massively parallel computers. Lars has authored or co-authored more than 60 peer reviewed articles. He has also been contributing to book chapters and done editorial and chair work for COPA.

With the rise of wearables, we now have smart devices that can seamlessly collect an extensive range of physiological measures. The availability of these new data sources brings opportunities in several areas, such as sleep medicine. This talk aims to introduce the students to some of the latest developments in sleep staging classification while learning practical Python data processing tips.

Joao Palotti obtained his Ph.D. in Information Retrieval at TU Wien. His thesis was part of the European projects Khresmoi and Kconnect, in which he worked on learning-to-rank methods to address the knowledge gap between medical documents and human expertise. He later worked as a post-doc at MIT on digital health,  as a visiting professor at CMU Qatar, and as an applied scientist at QCRI. In addition, he develops several open-source projects, such as TrecTools, pySocialWatcher and HypnosPy, aiming to either facilitate the life of fellow researchers or bring research insights into practice. After working as a data scientist consultant for several companies, such as the World Bank, Joao joined the ML team at Earkick, a swiss start-up on mental health.