18 December 2020 - 18 December 2020

Econophysics and economics. Two scientific disciplines that have carved a long way into the subject of financial markets in the last 30 years, providing new theoretical models, methods and results. Nevertheless, despite sharing the same element of scientific investigation, they seem to proceed on strictly separated ways with an absolute lack of dialogue. By considering a crucial problem on a financial market (the early detection of “abnormal behaviors” such as financial crashes or bubbles), aim of my research is to bring in the best of both worlds: the trends and explanations via rational behaviours from economics and the apparent extreme behaviours from econophysics. The conceptual bridge is provided by the introduction of the concept of time asymmetry (i.e. irreversibility) as a fundamental component of economic behaviour. The asymmetry can be easily seen by direct inspection of most time series data for financial instruments in which it is clear that an equilibrium process is not generating the signal. We can model this disruption of equilibrium using concepts from Prigogine’s thermodynamics (the dissipative structures) and in so doing can explain the general dynamics of financial observables, rather than either the trend-like behaviour or the formation of bubbles and crashes. According to the dissipative structures conceptual paradigm, I have identified the news on a financial market (that is the complex system) as the crucial parameter explaining its changes of phase. The role for the AI inside this conceptual model is to detect possible way for demonstrating this role of the news, by measuring the level of entropy implied by the signal conveyed. The fundamental hypothesis is that a high level of entropy of the message inside the news allows for a stable market, whereas the opposite for the case of financial crashes. Therefore, aim of my research will be to develop possible algorithms to make a computer able to classify the financial news as information with low or high entropy, helping therefore the financial operator to identify the trend in the market.

10 December 2020 - 10 December 2020

Multi-period planning problems have experienced an increasing industry interest reflected within IDSIA's project pipeline. On the research side, this demand is complemented by the emergence of strong Monte Carlo search techniques over the recent years. The resulting decision-making agents have demonstrated super-human performance in complicated toy scenarios, such as game-play of Hex, Go, Chess, StarCraft, ... This talk introduces Monte Carlo planning theory and applies it to two planning problems to serve industry demand: The hedging of financial derivative contracts (with UBS) and military decision making (with ArmaSuisse).

29 October 2020

Metabolism is central to all processes of life and the metabolome -- large-scale measurement of the quantities of small molecular entities in cells and tissues -- gives a readout of cellular functioning at a point in time. Harnessing metabolomic information together with transcriptomic information about gene expression allows for multi-level insights into genetic dysregulation and its cellular effects. I will describe a multi-omics approach based on genome-scale modelling that is able to integrate the two levels and provide insights into the systems-level deregulation of cellular function due to ageing by transforming the cellular reaction space into a constraint-based linear optimisation problem. Metabolic models such as these and their interpretation depends on publicly available data about small molecular metabolites. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts including to annotate metabolites in genome-scale models. However, ChEBI is manually maintained and as such does not scale to the full range of metabolites in all organisms. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem, based on chemical structures, which are represented as connected graphs of atoms and bonds. I will discuss recent efforts to evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and LSTMs, and different encoding approaches for the chemical structures, including cheminformatics 'fingerprints' (feature vectors) and character-based encodings from chemical line notation structural representations.

15 October 2020 - 15 October 2020

Clinical decision support systems, or CDSSs, represent a paradigm shift in healthcare today as they are expected to aid clinicians in their complex decision-making processes, encompassing data of different type and source. In oncology, most of the information used for devising optimal and personalized prognostic profiling is biological, which is time- and resource-demanding to retrive, thus unfeasible to enter a CDSS workflow. Here comes the hypothesis that imaging-derived information, namely radiomics, can be a surrogate for biological characterization of tissues, performing the so-called "virtual biopsy" and informing the entire treatment planning. Given this rationale, some example of application as well as open issues and future directions will be further discussed.

14 October 2020

The field of applied algebra encompasses a wide range of subjects in pure mathematics with applications in areas such as data analysis, biology, optimization and coding theory, to name a few. A hallmark of this research area is the mutual interaction between the development of abstract mathematical tools and the problems coming from real-world applications. In the talk I will illustrate this fascinating subject by means of a few examples that are especially related to my own research, including applications to computational biology, consensus methods in data analysis and neural networks. The exposition will not assume any preliminary knowledge, and everybody is welcome to attend.
Manno, Galleria 1, 2nd floor @12h00 Room 222

30 September 2020

Due to the current situation this event will be postponed to September/October 20202 Recent striking success in Artificial Intelligence have made the public believe that in a not so far future machines could be even more intelligent than human beings. The actual and possible developments of Artificial Intelligence open up a series of striking, deep and pressing questions such as: – Can a computer ever think in the way a human being does? – Can a computer have a mind and conscious experiences, such as thoughts, desires, and emotions? – What is artificial intelligence? Is it the same as human intelligence? Are they even comparable or are they something essentially different? – Can a machine be morally responsible for its actions? Can a machine be good or evil? What other moral considerations are related to AI? With the goal of enhancing their scientific and educational collaborations around those important topics, the Swiss AI Lab IDSIA USI-SUPSI and the USI Master in Philosophy Program are organising in Lugano on May 29-30 2020 an international meeting on current trends and perspectives in the Philosophy of AI.
Auditorium USI

23 September 2020

Salamander inspired platforms have been used to study robotic/biological locomotion in the Biorobotics Laboratory at EPFL for over a decade, drawing parallels between humanmade and natural systems to improve our understanding of both. Our most recent iteration, Krock 2, is targeted towards search and rescue applications within the NCCR Robotics program. Previously demonstrated in terrestrial applications, it is now being introduced into the water through the use of a dry suit and undulatory swimming gait. Multimodal locomotion carries the inherent challenge that the morphology and actuation for one mode may not be useful, or even present a hindrance, for another. On the other hand, unique synergies between modes of locomotion can present themselves. Its sprawling-posture gait and capability to swim along the water's surface manage to avoid the persistent challenge of balance within legged locomotion. However, this comes with inherent challenges in the perception of the environment, which we are addressing in collaborations.
Manno, Galleria 1, 2nd floor @11h30 Room 222

The recent advances in Deep Learning made many tasks in Computer Vision much easier to tackle. However, working with a small amount of data, and highly imbalanced real-world datasets can still be very challenging. In this talk, I will present two of my recent projects, where modelling and training occur under those circumstances. Firstly, I will introduce a novel 3D UNet-like model for fast volumetric segmentation of lung cancer nodules in Computed Tomography (CT) imagery. This model highly relied on kernel factorisation and other architectural improvements to reduce the number of parameters and computational load, allowing its successful use in production. Secondly, I will discuss the use of representation learning or similarity metric learning for few-shot classification tasks, and more specifically its use in a competition at NeurIPS 2019 and Kaggle. This competition aimed to detect the effects of over 1000 different genetic treatments to 4 types of human cells, and published a dataset composed of 6-channel fluorescent microscopy images with only a handful of samples per target class.
Manno, Galleria 1, 2nd floor @12h00