16 January 2020 - 16 January 2020

Most literature in the Philosophy of Computing stresses the dual, abstract and physical nature of computational systems. Under many respects, this debate reduces to the problem of explaining the relation which has traditionally been expressed in terms of the duality between specification and implementation. When this problem is analysed from the point of view of the notion of information though, computational systems require to be described at several levels of abstraction, and at each an appropriate notion of information is required. With such a conceptual tool in place, correctness of computational artifacts is adequately defined at functional, procedural and executional levels. A correct physical computational system is one which satisfies all such layers. This tripartite notion of correctness based on information is in turn essential to provide the basic elements of an appropriate logical analysis of efficiency, correctness, explanation and resilience for computational systems.
Manno, Galleria 1, 2nd floor, room G1-201 @12:00

18 November 2019

Cancer is a disease characterized by the accumulation of alterations to the genome, which selectively make cancer cells fitter to survive, proliferate and move. The understanding of progression and evolution models that underlie this processes, i.e., the characterization of sequences of alterations that lead to the emergence of the disease, is a topic attracting much attention. Of course, the problem of reconstructing such models is not new; in fact, several methods for inferring progression models (and phylogenies) from cross-sectional samples have been developed since the late 90s. Recently, we have proposed a number of algorithms to reconstruct cancer clonal evolutionary models from a variety of cross-sectional data types, either "ensemble", "bulk" or "single cell". We perform our reconstructions using a variety of algorithms based on a “probability raising” score that guarantees statistical dependencies on the inferred precedence relations. Our methods are complementary to traditional phylogeny reconstructions ones. Within this context, we have proven the correctness of our algorithms and characterized their performance. Our algorithms are collected in a R BioConductor package “TRanslational ONCOlogy” (TRONCO) that we have successfully used as part of our "Pipeline for Cancer Inference" (PiCnIc) to analyse Colorectal Cancer (CRC) data from TCGA. The newest addendum to TRONCO is the “Temporal oRder of Individual Tumors” (TRaIT) a new collection of algorithms that can be used for single-cell (and multi-region) progression analisys of cancers.
Room G1-201, Galleria 1, @12h00

14 November 2019

In this talk, we will discuss two different types of optical sensors based on luminescence: one is used for oxygen sensing type sensors, and the other is an «optical nose» for fingerprinting of substances. The problem of these types of sensors is that the measured signal is influenced by many components (like mirrors, lasers, electronics). Unfortunately these dependencies cannot be modeled mathematically in a simple way. So typically, complicated and empirical mathematical models are used, which are then fine tuned for each sensor in what is called calibration. But do we need to model those effects? Or there is another way? We will describe how the use neural networks can dramatically change how to build and use these sensors, without the need for any complicated mathematical model. Introducing neural networks in optical sensors typically does not require deep networks. However, there are several aspects that are very different from classical neural networks models which will be discussed here: one example overall is overfitting, that in this case requires completely different approaches to be dealt with. We will bring at the talk a prototype of a portable, low-cost sensor that we are currently developing at TOELT to demonstrate how a low-cost sensor can be built and used.
Manno, Galleria 1, 2nd floor, room G1-201 @12:00

30 October 2019

When building Bayesian networks with the help of domain experts, often properties of monotonicity arise, such as veterinarians expressing that “under more severe conditions, seeing the more severe symptoms becomes more likely”. It is well known that human decision makers will not use a network in their daily practice if such common properties of monotonicity are clearly violated, not even if the network shows overall high performance. In this talk, I will introduce some properties of monotonicity for Bayesian networks and further focus on the different roles that monotonicity has in engineering networks, both in building them by hand, in learning them from data and in fine-tuning.
Manno, Galleria 1, 2nd floor, room G1-201 @12:00

22 October 2019

The field of Geometric Packing problems has attracted the attention of many researchers in the late years. Generally speaking, in this setting we are given a region in the two-dimensional plane and a set of rectangles and the goal is to pack a subset of them inside the given region in such a way that they do not overlap and some given objective function is optimized. In this talk we will review our recent developments on two of these problems in the framework of Approximation Algorithms: Strip Packing and Geometric Knapsack. In the first problem, the goal is to pack all the rectangles into a strip of fixed width so as to minimize the final height of the packing, while in the second one the goal is to pack a subset of the rectangles of maximum profit into a rectangular region of fixed size. We will also discuss applications and open questions regarding the mentioned problems, the talk is meant to be accessible for non-experts.
Manno, Galleria 1, 2nd floor, room G1-204 @12h00

16 October 2019

Glyco-bioinformatics is an emerging subfield of bioinformatics aimed at expediting research in the field of glycomics. Unfortunately, the development of sugar-based virtual structures is made difficult by some structural features of sugar such as their high charge density, conformational flexibility and the torsional angles between glycosidic bonds. As a consequence, automated prediction of the binding poses of long sugar with proteins (that is a pivotal aspect of many biological processes) has been evaded so far, also due to the solvation/desolvation, weak surface complementarity and large electrostatic interactions of sugar/protein interactions. My PhD activity is aimed at overcoming these limits. To this aim, I have implemented a new computational method based on incremental docking that has been so far successfully applied to two important biological interactions such as that of heparin with the HIV-1 p17 matrix protein in the field of AIDS and VEGF with its VEGF receptor-2 in the field of tumor neovascularization. Perspective developments include the development of an algorithm able to automatize the developed computational methods and their application to other sugar/protein interactions of biological importance.
Manno, Galleria 1, 2nd floor, room G1-201 @12:00

25 September 2019 - 25 September 2019

This is an introductory level crash-course in Financial Mathematics. We review some key concepts of Financial product pricing and show how they can be applied to price Options. We present a Reinforcement Learning approach to replicate the theoretical prices.
Manno, Galleria 1, 2nd floor, room G1-201 @12:00

18 September 2019

In this talk we are going to highlight some major recent breakthroughs in the field of Natural Language Processing (NLP) and their impact in the definition of conversational models. But instead that talking about “chit-chat” conversation like “Alexa play my favorite Italian song” and the competition for passing the Turing test, we will focus on the automation of the conversations in the context of contact centers and we will address specifically the need to define “goal oriented or intent driven” conversational models. The ultimate objective is to identify what is needed to create personalized conversation in a framework where Artificial Intelligence meets Human Interactions.
Room G1-201, Galleria 1, @12h00

17 September 2019 - 17 September 2019

In this talk I will first give a short overview of the research performed at the Robotics and Perception Group. I will present recent research from our lab in the areas of vision-based navigation of micro-aerial vehicles, aggressive flight and machine learning. After this I will focus on autonomous agile flight and present our research on drone racing, where we combined a learned perception system with a classical control pipeline to teach a drone to race through a track. Finally I will describe our current project on autonomous acrobatic flight, explain the challenges that arise when pushing vision-based platforms to aggressive maneuvers and outline our approaches to integrate learning deeper in the control&estimation pipeline.
Galleria 1, @12h00

25 July 2019 - 25 July 2019

Data fusion strategies for precision medicine and drug repurposing. Over the last few years, biomedical research and clinical practice have experienced an incredible growth in terms of both amount and heterogeneity of data being collected and leveraged for different types of analysis. This data explosion represents a great opportunity to increase our knowledge about many biological mechanisms as well as to improve medical processes (i.e., diagnosis, prognosis, therapy). However, not all big data are created equal. The downside of data heterogeneity is it complicates integration analysis. For example, clinical record data is highly heterogeneous, sparsely annotated, and contains several measurement types and unstructured text fields, comprised of ambiguous statements as well as varying levels of certainty, whereas genomic and imaging data are crisp and densely annotated data with a low cardinality of distinct variables. Integrating these data is particularly challenging when the molecular measurements are not conducted on individual subjects. In order to take full advantage of the wide spectrum of biomedical data available, advanced data integration tools need to be applied. In this context, I will present data fusion strategies for precision medicine and drug repositioning from my own research. These methods will include an approach for the prediction of potential multi-target drug repurposing strategies and its performances when applied to triple negative breast cancer. A second method that will be presented computes patient similarities by integrating patient-specific genomic data and public biomedical knowledge through a matrix tri-factorization approach. Finally, I will present a network-based approach integrating genomic and drug data with Gene Ontology-based information theoretic semantic similarities for the suggestion of new drug repurposing candidates. These examples show the potential of developing new research hypotheses and conducting predictive and data interpolation operations.
IDSIA Meeting Room, Galleria 1, @14h30