Previous Next
Scroll down to
explore more

LION lab:

Machine Learning and Intelligent Optimization

The LION (machine Learning and Intelligent OptimizationN) laboratory at the University of Trento (Italy) fosters research and development in intelligent optimization and reactive search techniques for solving relevant problems arising in different application areas, including marketing automation and e-commerce, telecommunication networks, ICT, mobile services, big data, cost management, social networks, clustering and pattern recognition in bio-informatics.

These challenges require the integration of different theoretical and practical tools in a creative environment that cuts across rigid borders between disciplines. This is the spirit of the LION activities, which include advanced research and educational opportunities ranging from the basic Computer Science degree, to the Master Degree in Computer Science, to the international PhD program.

Welcome to LIONlab!
Roberto Battiti , LIONlab director

Machine learning plus otimization.

Realizing the vision of extreme automation.

Leaning from data.
 

On the shoulders of Galileo Galilei and Leonardo Da Vinci.

Bioinformatics and healthcare.
 

For more effective and affordable healthcare.

Industrial research and development.

From consultancy to turnkey applications.

Latest projects

A sample of our research projects, conferences and startups.

  • LION 8 Conference

    Gainesville, Florida - USA, Feb 16-21, 2014
  • Reactive Search srl

    the Learning and Intelligent Optimization Company
  • LION 6 Conference

    Paris, Jan 16-20, 2012
  • Clinicoptimizer.com

    Trading off cost versus quality for picking your favorite hospital.
  • lionmode.com

    Self-service big data analytics.

The LION Team

Roberto Battiti

Research program leader

Mauro Brunato

Assistant professor

Andrea Passerini

Researcher

Dinara Mukhlisullina

PhD Student

Danil Mirylenka

PhD Student

Gianluca Corrado

PhD Student

Giovanni Pellegrini

Junior Research Assistant

Matteo Presutto

Junior Research Assistant

bg

LIONlab

We are in Trentino
Home of the Winter Universiade 2013

Projects

Big data, predictive analytics and optimization

Huge amounts of data are produced during business operations. The LIONlab develop methods and tools to mine this treasure and extract actionable insight. Applications are far-reaching, ranging from marketing and e-commerce to bioinformatics, healthcare and social networks. After models through "learning from data" methods are avaliable, automated improvement motors (optimization) can be run to obtain better and better solutions.

Reactive Search and Intelligent Optimization

Reactive Search advocates the integration of sub-symbolic machine learning techniques into local search heuristics for solving complex optimization problems. The word reactive hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Methodologies of interest for Reactive Search include machine learning and statistics, in particular reinforcement learning, active or query learning, neural networks, and meta-heuristics (although the boundary signalled by the "meta" prefix is not always clear).

Machine learning

The increasing availability of huge amounts of data in machine readable format from sources as diverse as databases of chemical compounds, DNA and protein sequences and structures, tagged bookmarks, digital libraries, images, web pages and blogs represents an unprecedented opportunity as well as a formidable challenge for machine learning systems. Such a complex body of information calls for the most recent advances in machine learning research in order to scale to large datasets, deal with complex structured data both in input and output, and jointly solve multiple related tasks, as well as learn models able to transfer knowledge among similar tasks. Models able to provide interpretable explanations for their decisions are especially appealing for the domain experts. Our research is mainly focused on kernel machine algorithms for structured data, multitask learning and statistical relational methods.

Machine learning and optimization for bioinformatics

Computational molecular biology is a hot research area and a continuous source of relevant and challenging problems for machine learning. Structural bioinformatics aims at predicting the three-dimensional structure of macromolecules such as proteins and RNA, given their sequence of residues or nucleotides. Given its intrinsic complexity, the problem has been addressed by tackling a number of related sub-tasks, such as secondary structure, contact map or disulphide bridge prediction. Being able to effectively solve such sub-tasks and combine their outputs into a reliable 3D structure predictor is one of the greatest challenges in bioinformatics. The activity of living cells involves a huge number of interactions among their components, which can be represented as regulatory, metabolic and interaction networks and whose structure is mostly unknown. Machine learning techniques need to be able to combine heterogeneous and noisy sources of information from evolutionary, similarity and experimental data in order to contribute in discovering such relational structures.

PRIN project 2011

Page1

PRIN project 2011

Learning Techniques in Relational Domains and Their Applications

ScienScan

Page1

ScienScan

ScienScan: an efficient visualization and browsing tool for academic search

Triton

Page1

Triton

Trentino Research and Innovation for Tunnel Monitoring

Damasco

Page1

Damasco

Data Acquisition and MAnagement in a Sensing and COmmunicating environment

CASCADAS

Page1

CASCADAS

Component-ware for Autonomic Situation-aware Communications, and Dynamically Adaptable Services

TET

Page1

TET

Type Extension Trees for feature construction and learning in relational domains

BIONETS

Page1

BIONETS

BioNets - mating in the computer world

BC-EMO

Page1

BC-EMO

Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker

GRID.it

Page1

GRID.it

An Italian National Research Council Project on Grid Computing

WILMA

Page1

WILMA

Wireless Internet and Location Management Architecture

E-NEXT

Page1

E-NEXT

EU FP6 Network of Excellence on Internet protocols and services

CatANalyst

Page1

CatANalyst

A web server for predicting catalytic residues in proteins from sequence and structure

QuaSAR

Page1

QuaSAR

Qualita' e Controllabilita' dei Servizi di Comunicazione su Reti Eterogenee

ADONIS

Page2

ADONIS

Algorithms for Dynamic Optical Networks based on Internet Solutions
Web

Prin project

Learning Techniques in Relational Domains and Their Applications

Start date: Oct 2011 - end date Oct 2013

Research units:

  • I GORI Marco Professore Ordinario Università degli Studi di SIENA
  • II FRASCONI Paolo Professore Associato confermato Università degli Studi di FIRENZE
  • III MALERBA Donato Professore Straordinario Università degli Studi di BARI
  • IV BATTITI Roberto Professore Ordinario Università degli Studi di TRENTO
  • V SPERDUTI Alessandro Professore Ordinario Università degli Studi di PADOVA

The field of machine learning is in the midst of a "relational revolution." After many decades of focusing on independent and identically-distributed (iid) examples, some researchers are now studying problems in which the examples are linked together into a complex network. The world wide web, research papers with their citation and relational data bases are noticeable examples of these networks, but any learning problem can in principle be formulated in the framework of networked data, since the relationships amongst the examples can be induced by unsupervised learning. Whereas it is quite straightforward to formulate learning in networked data when symbolic variables are involved, most approaches to learning in the continuum setting do not consider relationships amongst the examples. Our basic assumption is that problems like the classification of a pattern can benefit from a collective computation that takes into account either explicit or implicit relationships with related patterns. The overall process turns out to take place on networked data more than on single instances, thus opening the doors to new computational schemes.

The different methodological contributions from the partners of the project will lead to a unified mathematical formalism for understanding functions and learning protocols on networked data. This study is supposed to introduce a framework for understanding generalization issues in semi-supervised and transductive frameworks, so as to develop a solid methodology to evaluate the results. Functions on networked data assume values that depend on both the information within the vertexes and the edges and, therefore, it turns out to be important to understand how machine learning models can effectively induce concepts over such domains. The study will also focus on active learning schemes that propose vertexes to be labeled mainly on the basis of the links amongst the examples. We will adress semi-supervised and transductive learning using different models, including probalistic inductive logic programming (PILP), neural networks, kernel machines, and we will investigate the extension of classic PCA techniques to this new graphical framework. We expect to shed light also on related models that are unified by the common mechanism of propagation throughout networked data, also in the case of dynamically changing data.

The application of the proposed theory will be mainly investigated in the fields of pattern recognition and data mining. In particular, we will carry out a systematic study on appropriate representations of pattern recognition problems within the setting of learning in networked data. Whereas the machine learning community has already gained a significant experience in the process of formulating appropriate problems in the framework of inductive logic programming by expressing relationships amongst the examples, in most cases the learning process involves collections of examples where neither relationships are made explicit nor are they used implicitly during the learning. A special attention will be devoted to the case in which the strength of the relationships amongst the pattern are learned by unsupervised learning and, subsequently, are refined by exploiting a few labelled examples. This project is expected to open a new direction in the whole area of pattern recognition by proposing a general framework to carry out collective classification. However, in order to show the impact in real-world applications, we will also focus on a specific problem of document analysis and recognition, namely the recognition of chemical drawings in low-quality documents. In addition, we plan to extend multirelational data mining methods to improve scalability on spatial data and to introduce relational approaches to transductive learning to face predictive tasks where only a small portion of available data is labelled.

Interestingly, the research carried out in this project is expected to have an impact in the field of the science of networks, where the graph dynamics is captured by explicit modeling of the growth process. The adoption of learning schemes is expected to yield a better basis for dealing with complex evolution processes, where simple assumptions, like the preferential attachment, might not yield the desired approximation. Another significant impact is expected in the field of optimization, where we plan to apply the methods developed in relational learning to stochastic local search techniques based on reactive search principles. The idea is to regard the search space as a graph, where edges represent neighborhood relationships between solutions to be explored.

Web

ScienScan

ScienScan: an efficient visualization and browsing tool for academic search

ScienScan
Web

Triton

Exploratory project for road tunnels monitoring and tele-controlling. see DISI TRITON web site

TRITon is a research and innovation project funded by the project members and the Autonomous Province of Trento (Provincia Autonoma di Trento, PAT) aimed at advancing the state of the art in the management of road tunnels, specifically to improve safety and reduce energy costs. To achieve these goals, TRITon will merge research on state-of-the-art technology into the established practices of road tunnel infrastructures, supported by project members that include local research centers and companies working in the field.

An example application, central in TRITon, is adaptive lighting. In current deployments, the light intensity inside the tunnel is typically regulated based on design parameters and the current date and time, and regardless of the actual environmental conditions. As it can be experienced when driving through a road tunnel too bright or too dark, this potentially determines a waste of energy, as well as a potential safety hazard. In TRITon, the light intensity inside the tunnel will instead be regulated through a wireless sensor network (WSN). This will relay sensed light information to the control station, which will exploit such information for fine-grained adaptation to environmental condition, significantly reducing costs and improving safety. A dedicated laboratory has been established to support TRITon's research and development activities.

However, to bring state-of-the-art research and technology like WSN into road tunnel management, the traditional lab-centered research is not sufficient. Indeed, TRITon will transfer its results in real test-sites, four operational tunnels on road SS 45bis near Trento. This will provide not only the ultimate test for the project outcomes, but also a direct and measurable benefit to the local population.

Web

Damasco

Project for scientific and technological cooperation founded by the Italian Ministry of Education, University and Research in the framework of international collaboration between Italy and USA

Short Summary of the Scientific Statement

DAMASCO aims at studying and improving the emerging technology for the collection, elaboration, transmission and use of context information gathered through sensors which are able to communicate. The focus is on the collection and elaboration of context information.

The research activities of the DAMASCO project are centered on "Intelligent Transport Systems" (ITS). The internetworking and communication frameworks are based on solutions suitable for ad hoc (multi-hop or peer-to-peer) and sensor networks so that new solutions to monitor and collect context information can be easily deployed. These data are elaborated to provide services for:

  • Accurate traffic and environment information
  • Distributed monitoring of vehicles and roads
  • Improvement of road safety
  • Infotainment

The deployment of ITS services for car networking is limited by the small number of vehicles equipped with sufficient hardware and software to create a network node. Thus, in the initial phase of the project, an infrastructure will be used to provide services to networking cars.

Partners

  • UCLA (University of California at Los Angeles)
  • Universita' di Trento
  • Universita' di Bologna
  • ST Microelectronics
  • Istituto Superiore Mario Boella (Torino)

Short Summary of the Local Activities

The research activity of the University of Trento is centered on security and application aspects:
  • Ad hoc and peer-to-peer networking for vehicular communication, in particular, node localization and security (node and user authentication, confidentiality and data availability)
  • Location aware collaborative recommendation.
  • Infotainment for the application of new communication paradigms to enable context aware services
  • Reputation and Trust Management for ad-hoc virtual communities.

Members of the local unit

  • Prof. Roberto Battiti (Scientific Coordinator)
  • Dr. Mauro Brunato
  • Dr. Roberto G. Cascella (Young Researcher)

Publications

ROBERTO G. CASCELLA
The "Value" of Reputation in Peer-to-Peer Networks.
In Proceedings of the Fifth IEEE Consumer Communications & Networking Conference (CCNC 2008) Las Vegas, Nevada, USA, January 10-12, 2008. [doi]

ROBERTO G. CASCELLA
Costs and Benefits of Reputation Management Systems.
In Proceedings of the 9th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2008), Newport Beach, CA, USA, June 23-27, 2008. [doi]

MARIO GERLA, ROBERTO G. CASCELLA, ZHEN CAO, BRUNO CRISPO, ROBERTO BATTITI
An efficient weak secrecy scheme for network coding data dissemination in VANET - Invited Paper.
In Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2008), Cannes, France, September 15-18 2008.

ROBERTO G. CASCELLA, ZHEN CAO, MARIO GERLA, BRUNO CRISPO, ROBERTO BATTITI
Weak Data Secrecy via Obfuscation in Network Coding Based Content Distribution.
Accepted for publication to the IFIP Wireless Days Conference, Dubai, United Arab Emirates, November 24-28 2008.

Related News

Web

Cascadas Project

Component-ware for Autonomic Situation-aware Communications, and Dynamically Adaptable Services


As European citizens, we are witnessing an age of computing ubiquity where our work and home environments are enveloped by computing resources. This comes at a cost, which is the significant problem of configuration and complexity of these resources. If computing power is to serve us, and the converse is to be denied, then these resources and their rich panoply of services must be able to carry out their increasingly complex functions without significant intrusion into our lives. CASCADAS is a three-year integrated project driven by a clear research vision, which is to define a new generation of composite, highly-distributed, pervasive services, with underlying technology, that addresses these configuration and complexity problems.
Our central objective with CASCADAS is to identify, develop, and evaluate a general-purpose abstraction for autonomic communication services, in which components autonomously achieve self-organisation and self-adaptation towards the provision of adaptive and situated communication-intensive services. We will achieve this objective by developing a common abstraction, called an ACE (Autonomic Communication Element), which represents the cornerstone of our component model. We will also use four key, underpinning scientific principles in CASCADAS, which are situation awareness, semantic self-organisation, self-similarity, and autonomic componentware to help guide the project.
The ACE concept, together with the use of the four principles ensures that CASCADAS overlaps strongly with the goals of the "Situated and Autonomic Communications" call. CASCADAS is highly relevant to the two main objectives of this call, in that we precisely define a self-organising communication network concept, and we also study how strategic needs impact on future communication paradigms. CASCADAS is proactive in seeking to carry out research encompassing security, resilience, and the interaction of new paradigms on society, which are all key call focuses.


CASCADAS in the local newspaper (click to enlarge)
Web

TET

Type Extension Trees for feature construction and learning in relational domains

Type Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. TETs can be used as a feature discovery instrument in relational domains, and a metric on TET features can be constructed, in order to effectively exploit their expressive power in terms of "counts-of-counts". Experiments on bibliographic data (e.g., for the prediction of the future h-index of an author) show the potentiality of such features.

TET
Web

BC-EMO

Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker

BC-EMO paper, BC-EMO software
Web

The GRID Research Project

GRID COMPUTING - FIRB-CNIT Project "Enabling platforms for high-performance computational grids oriented scalable virtual organizations (GRID.IT)"

The specific objectives of the entire FIRB research project are Grid Oriented optical switching paradigms and High Performance Photonic Tests.

Web

The WILMA Research Project

The project is sponsored by the Province of Trento (Provincia Autonoma di Trento) and participated by ITC/irst (Istituto per la Ricerca Scientifica e Tecnologica), the Department of Computer Science and Telecommunications of the University of Trento and Alpikom S.p.A..

Web

E-NEXT

The general objective of E-NEXT is to reinforce European scientific and technological excellence in the networking area through a progressive and lasting integration of research capacities existing in the European Research Area (ERA). The detailed objectives of the Network are divided into two dimensions: the Research dimension and the Integration dimension. This reflects the two complementary lines of action of the Network of Excellence.

Web

CatANalyst

A web server for predicting catalytic residues in proteins from sequence and structure

CatANalyst
Web

QuaSAR Project

"Qualità e Controllabilità dei Servizi di Comunicazione su Reti Eterogenee"

"Quality and Controllability of Communication Services over Heterogeneous Networks"

Project Statement
Networking technologies and telecommunication services are experiencing fast and significant developments, both methodological and applicative. In the field of networking technologies, for instance, both backbone networks and access networks are subject to important innovations. As for the former, the advent of optical technologies not only provides unimaginable transport capacities in the backbone, but also opens up the opportunity for optical packet switching, with enormous gains in terms of reliability and efficiency. As for access networks, on the other hand, the successful deployment of xDSL technologies, the ever-increasing popularity of 802.11x technologies, and the availability of 3G infrastructures based on UMTS, are creating new interoperability problems but also new opportunities.

Analogous considerations hold for the technologies used in network terminals. In parallel with the natural progress of PC-class systems, we have faced the evolution in terms of processing power of Personal Digital Assistant systems (PDAs) on one side, and the production of new mobile phonesets with moderate processing capabilities, equipped with open operating systems and capable of running novel applications and services, on the other side.

The availability of such a great variety of technologies seems to speed up the diffusion of innovative telematic applications and services, such as those requiring multimedia content delivery. And, in fact, this is happening for a large number of innovative services in technically homogeneous systems and networks, as, for instance, the video-phone service and the video-delivery service over UMTS networks or IP Telephony services over wired IP networks.

Unfortunately, in more complex but realistic scenarios, such as those of large-scale hybrid wired and wireless networks, the heterogeneity in terms of networking technologies and of computing systems makes it hard, if not impossible, both QoS management and the interoperability of applications, unless complex interworking solutions are devised.

Therefore, the main focus of this proposal will be the study and the development of technologies and methodologies for the provision of communication services with controllable quality in higly heterogenous distributed systems, in terms of available networking infrastrutures, user terminal characteristics and typology of services and applications.

In particular, the project activity will aim at pursuing the following macro objectives:

  • definition and evaluation of mechanisms and policies to support Quality of Service in heterogeneous networks, both wireline and wireless;
  • integration of those mechanisms in a global architecture that provides advanced communication services to applications, and definition of network-application interactions for the actual provisioning of communication services defined by Service Level Agreements;
  • definition and implementation of mechanisms for traffic monitoring, to be used for the validation and the enforcement of the negotiated policies;
  • definition of advanced communication services with guaranteeed and controllable quality that can be provided in heterogeneous networking scenarios to multimedia applications running on multi-homed terminals.

While devising proper solutions for the above objectives, particular attention will be devoted to the scalability of solutions and to manageability of infrastructures, in order to allow dynamic control capabilities in scenarios that are extremely dynamic, due to both users mobility and system status variations.


Institutions Involved in QuaSAR Project

  • Università di Pisa (UNIPI)
  • Università degli Studi di Napoli Federico II (UNINA)
  • Politecnico di Torino (POLITO)
  • Roma Campus Biomedico (UNICBM)
  • Università degli Studi di Trento (UNITN)
  • Università degli Studi di Catania (UNICT)



Responsible from UNITN: Mikalai Sabel.

Web

The ADONIS Research Project

The project aims at proposing an evolutionary methodology for the design and management of optical networks in order to support the migration from the current static assignment and routing techniques to adaptive, dynamic techniques based on IP-centric control as soon as new generation equipment becomes available.

Simplicity is the ultimate sophistication.

Leonardo Da Vinci

The archetype of the Renaissance Man

Measure what is measurable, and make measurable what is not so.

Galileo Galilei

Father of modern science

It is a good morning exercise for a research scientist to discard a pet hypothesis every day before breakfast: it keeps him young.

Konrad Lorenz

Nobel Prize in Physiology or Medicine

contact us

WE LOOK FORWARD TO HEARING FROM YOU

Send us a message

Contact Info

Address: DISI - Università of Trento,
Via Sommarive 5, 38123, Trento

Email: roberto.battiti ((AT)) unitn.it

OUR PLACE (TRENTINO)

img