18 May 2022

On Wednesday 18 May, the prize-giving ceremony of the Wiki-Science Competition 2021 will take place in the Auditorium of the West Campus of Università della Svizzera italiana (USI). A report by the Dalle Molle Institute for Artificial Intelligence USI-SUPSI won the "Image sets" section.
Auditorium West Campus - USI

9 May 2022 - 18 May 2022

AIDD is a European Project which aims at training and preparing a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry. In this context IDSIA USI-SUPSI is proud to host and co-organise the first Summer School on advanced Machine Learning for Drug Discovery.
East Campus USI-SUPSI, Lugano-Viganello

5 May 2022 - 6 May 2022

From May 4th to 6th, the Dalle Molle Institute for Artificial Intelligence (IDSIA), will host at the East Campus of Lugano-Viganello the annual meeting of the European COST action INNOGLY, a European research action aimed at creating research networks to develop knowledge on the function of glycans in different biological contexts.
East Campus, Lugano - Viganello

29 April 2022

In the past few years, Reinforcement Learning (RL) has achieved significant breakthroughs and its intersection with practical applications has become more and more relevant.
East Campus USI-SUPSI, Lugano

30 March 2022

Understanding mental illnesses requires looking at a variety of different factors contributing to the development, persistence, and treatment of mental illness. That is, scientists must take into account the role of, e.g., behavioral, psychological, neurophysiological, genetic, pharmacological and environmental influences on psychopathology. To integrate real-world data regarding such varied factors, complex computational models have been raising high hopes. In this talk, I shall examine the vices and virtues of recent multiplex approaches for grasping psychopathology.
Room D1.06, Sector D, first floor, Campus Est, USI-SUPSI, Lugano-Viganello

10 December 2021 - 10 December 2021

Statistical modelling and analysis for complex biomedical data: challenges and obstacles. One of the major challenges in modern biostatistics derives from the analysis of Real World Data (RWD). Indeed, whenever main features of common control designs cannot be applied, common statistical tools may fail in dealing with unbalanced groups, non-homogenous populations and confoundness derived from large observational data. New statistical perspectives are needed to deal with wide heterogeneity of big biomedical data that might magnify noise rather than improve inference. A deep understanding of the data generating process represents the first step to investigate the complex dependence structure among covariates in RWD and for supporting new findings in biomedical research. This seminar proposes novel statistical settings (classical and Bayesian) for the analysis of observational studies based longitudinal, survival and cross-sectional data. Latent class models, Bayesian Networks, Frailty modelling are illustrated as possible tools for facing obstacles and challenges of complex biomedical data. Examples from different biomedical frameworks, such as COVID-19 data, gene therapy and oncology are illustrated.
Room D1.10 - East Campus

2 December 2021

Artificial intelligence (AI) and the human brain are profoundly different cognitive systems. Nevertheless, they can interact with each other dynamically. One such way of interaction concerns the possibility of using AI to enhance human cognition. This approach, known as 'augmented intelligence', raises important ethical issues. These include implications for agency, autonomy and moral responsibility, and the moral permissibility of cognitive enhancement. This talk will offer an overview and critical analysis of the ethical implications of augmented intelligence and human-computer interaction.
Room D1.01, Sector D, first floor, Campus Est, USI-SUPSI, Lugano-Viganello

AI is growing to play an increasingly important role in the decision environments of human agents. One of the key challenges here is AI influence - an aspect that seems largely unaddressed in current debates around AI Ethics. In this talk, I aim to shed some light on the notion of AI influence and the problems that surround it. In the first part of this talk, I will outline what exactly I mean by AI influence. For this, I will introduce what I call the objectivity-fallacy, and will differentiate between cases of intended and unintended AI influence. The second part of this talk will then focus on the ethical implications of unintended AI influence. I hope to emphasise an important change in the power dynamics in human-AI interaction, and will outline how unintended AI influence changes the roles and thereto related responsibilities in human-AI interaction.
Room B1.12, Sector B, first floor, Campus Est, USI-SUPSI, Lugano-Viganello