"...an excellence PhD program, in collaboration with top universities world wide"
FBK offers a unique stimulating and exciting environment to its PhD students. PhD students supervised by FBK researchers conduct their work in the lab of the research units, in strict collaboration with the units’ teams and under the supervision of top-ranked researchers, targeting the highest scientific and publication standards.
The PhD program is carried out in collaboration with prestigious national and international affiliated universities. Over 90 PhD students are allocated to FBK's research units and are actively involved in international research projects and mobility programs.
Most alumni of the PhD program have an exceptionally strong publication and citation record, and have been enrolled in prestigious institutions, as IBM T. J. Watson and University of Colorado at Boulder. During the FBK PhD Day, the best PhD student who graduated in the previous year is granted a monetary award.
FBK's research includes information and communication technologies, materials and microsystems, religious studies.
Abstract: The goal is generating input values (referred to as test payloads), intended to exercise vulnerabilities and	evaluating whether such payloads manage to expose an actual vulnerability, i.e., the security oracle.
FBK hosts lectures for PhD students given by renowned international lecturers as well as recognized FBK researchers. Attendance to the courses is open to any FBK PhD student or researcher.
Process mining provides a new means to improve processes in a variety of application domains. There are two main drivers for this new technology. On the one hand, more and more events are being recorded thus providing detailed information about the history of processes. On the other hand, in most organizations there is a need to improve process performance (e.g., reduce costs and flow time) and compliance (e.g., avoid deviations or risks). This introductory course on process mining teaches students the theoretical foundations of process mining and exposes students to real-life data sets to understand challenges related to process discovery, conformance checking, and model extension.
Modeling opinion, culture and knowledge dynamics using big data.
Alfio Gliozzo has 16 years of research and development experience in the field of Artificial Intelligence, with strong focus on Natural Language Processing and Semantic Web. He is currently managing the Knowledge Induction department at IBM T.J. Watson Research and teaching Cognitive Computing at Columbia University. He was part of the Deep QA team that developed Jeopardy! system.
Dr. Leo Huf Campos Braga worked on his PhD at FBK within IRIS team from October 2010 to April 2014. He got his PhD from University of Trento in April 2014 and joined NXP Semiconductors (Eindhoven, The Netherlands) as principal Scientist in August 2014. In January 2016 he moved to EFFECT Photonics where is now Module Engineering Manager of the team in charge of designing the optical communications module, including mechanics, electronics, firmware and optical sub-assembly.
Specialties: Supervised learning, kernel methods, statistical machine translation, natural language processing, software architectures for text processing, summarization, information extraction, relation extraction, event learning.
Research Interests: Formal methods, verification of hybrid systems, verification of infinite-state systems, abstraction refinement techniques, parameter synthesis, satisfiability modulo theories.
Research scientist, data scientist, research project manager and software engineer with more than 10 years experience in a variety of machine learning, ranking, personalization, text mining and natural language processing applications, including information extraction, summarization, topic labeling, text understanding, machine translation and semantics, in monolingual and cross-lingual environments. Interested primarily in bringing various creative ideas to real world applications.
Research scientist in the areas of service-oriented computing and artificial intelligence. His research interests include service composition, dynamic process adaptation, user-centric services, collective adaptive systems, AI planning. Heorhi obtained his Ph.D. in computer science in 2012, with the thesis: Service Composition in Dynamic Environments: From Theory to Practice.
October 2014 bachelor degree in Philosophy. Thesis "Knowledge Representation and Formal Ontologies". July 2016 master degree in Philosophy "Ontologies, Concepts and Mental Disorders DSM 5 and Schizophrenia"
My area of research is IoT security - area of endeavor concerned with safeguarding connected device and networks in the Internet of things (IoT).
My PhD project aims to propose new methods to virtually segment white matter tracts of interest in the human brain. We apply machine learning techniques to diffusion MRI data in order to obtain automatic segmentations from examples. My research interests include brain connectivity, neural networks, neuroimage analysis and signal processing.
My interest in computer science was born attending classes in the university. From the beginning I wanted to explore many aspects of this topic, motivated by willing classmates and professors. During several university projects I explored in particular embedded systems enhanced with distributed artificial intelligence techniques. Recently I became curious about the development of web and mobile applications and I decided to start a phd in web application testing. I like the exciting, dynamic and challenging environment offered by research, which lets me find out and learn something new every day. I am open to meeting new people with my same passion and thirst for knowledge; I like teamwork, collaboration and sharing of ideas and time.
I am attending the 1st year of PhD in Psychological Sciences and Education in a joint collaboration between FBK and Department of Psychology and Cognitive Science. I graduated in Neuroscience after a two-months internship at the Neurobiology and Behavior Laboratory of Nagasaki University. My current project is about the relationships between genes, psychological traits, and behaviors using smartphones and social media sites as assessment tools.
Software systems are composed of multiple components. Components hold special privileges to perform different tasks. Controlled access to system resources is achieved through permission based security model. In order to gain privileges, a malicious component usually has to abuse a privileged component. This phenomenon is commonly known as the confused deputy attack. Confused deputy attack occurs when a privileged component performs an activity that needs special permission on behalf of other component that does not have the required permission. Static analysis is often used to detect existence of this vulnerability. However, reports of static analysis are vulnerability points rather than conditions that cause the vulnerability. Therefore, a developer that wishes to fix this vulnerability has to manually analyze the code in order to understand the conditions that cause the vulnerability. Similar to other permission based systems, the Android system also suffers from the confused deputy attack called permission redelegation. In our work, we investigate different approaches on how to automatically generate test cases that reveal permission redelegation vulnerabilities in Android apps. Test cases help developers easily trace, identify and fix the vulnerabilities. The test cases are also used to verify if the applied fix has resolved the vulnerability or not.
I joint FBK as a PhD student to pursue my interests in electrochemical energy conversion, solar fuel production and storage, and more generally exploitation of solar energy. My academic route maintains a strong link to the world of industry and engineering applications, with the aim of facilitating and encouraging the knowledge transfer between university and enterprises. In the ARES unit, I am currently working on the development of a Redox Flow Battery (RFB) based on the use of organic compounds.
My research is mainly focused on the study of opposition relations at lexical level as a source of contradiction at textual level. Detecting oppositions, both among words and among portions of text, is a fundamental requirement for text analysis. Indeed, textual opposition plays a crucial role in applications such as machine translation, discourse understanding, and information retrieval. In the first year, I have mainly focused the research on the study of opposition relation among verbs. I have studied opposition relation taking in consideration that i) opposition relation is among senses of verbs, and ii) there exist different types of opposition relation with different characteristics. The future steps of my research project aims at studying cases in which an opposition relation between two verbs lead to a contradiction among pairs of sentences. The study of contradiction also requires the analysis of the conditions in which this relation occurs, both at sentence and at text level. In fact, the presence of a lexical opposition alone is not a sufficient condition for contradiction to exist: the opposite items need to be referred to the same entity/event. Consequently, for a complete study on contradiction, also co-reference need to be study from both a theoretical /linguistic and a computational point of view.
Wireless embedded systems are shaping a new era of smart everything everywhere. New devices are entering in our daily lives contributing to novel scenarios where their co-existence opens opportunities for users' quality of life. Still old and novel challenges lies ahead. Wearables for Health and professional sport encounters the trade-off between energy efficiency, form factor at one side and the QoS and the human factors at the other. Moreover, their massive distribution pose challenges on their scalability, co-existence and cooperation. In this research, these challenges will be considered under the energy efficiency umbrella.
The aim of the thesis is a general and scalable distributed machine learning algorithm and its application in multi-agent systems to increase their ability of collective adaptation in dynamic environments.
Undergraduate institution: Alma Mater Studiorum di Bologna – Facoltà di Ingegneria dei Sistemi Edilizi e Urbani – Ravenna Research fields: Automatic segmentation of Point Cloud to facilitate the BIM approach. Working in 3DOM research unit, FBK Trento, Italy.
Forests are an extremely important natural resource that need to be preserved for obvious environmental and economic reasons. An efficient forest management is essential for effective forest preservation. However such management demands an accurate inventory of biophysical parameters, such as the tree/crown height, the crown width, the biomass, which are costly to estimate by conducting on-site visits. In addition, the tree species information is also very essential for improving the accuracy of estimation. With the advancements in the airborne Light Detection and Ranging (LiDAR) remote sensing technology, it has become possible to capture three dimensional (3D) information of forests at the individual tree level in a much reliable and economical way. The high density multi-return airborne LiDAR data can provide a huge amount of spatial details about forest components, and hence can be used for accurately extracting the tree crowns and species information, which can facilitate the ultimate objective of accurate biophysical parameter estimation. Accurate modelling of the internal crown structure of the tree is necessary for extracting species information. Also, the information in the LiDAR data can be better exploited for accurate biophysical parameter estimation, by developing novel methodologies which incorporate the ability of Sparse methods in finding the prominent patterns in the data. Thus, the overall objectives of this proposal are to: 1) develop methods for identifying the species of trees belonging to the same tree-genera using high density airborne LiDAR data, 2) develop novel methods for 3D tree crown delineation in high density multi-return airborne LiDAR data, and 3) develop fully automatic biophysical parameter estimation techniques, by giving special emphasis on exploiting the capabilities of Sparse methods.
Various health risks are related to unhealthy lifestyles and poor dietary habits. A number of pathologies due to these health risk behaviors represent a serious challenge, resulting in disabling or life-threatening repercussions. People struggle to adhere to dietary requirements, exercise, and lifestyle recommendations. Additionally, decreased motivation results in a probability of negative health implications. This study evaluates existing technologies used to have users adhere to sustained healthy behavior. We investigate persuasive technology and gamification strategies to reinforce users in exercising and healthy food substitution. We intend to develop a model to be used in creating-producing widgets to persuade people change their lifestyle. Subsequently, our model facilitates a sustained diet management plan supported by healthcare experts to intervene in the activity, only when necessary.
I am a Ph.D. candidate in the Department of Information Engineering and Computer Science at the University of Trento. By joining the ICT doctoral school program in 2014, I became a research fellow member of ICT4G research unit inside Fondazione Bruno Kessler FBK, Trento - Italy. At the same time, I was partially funded by CRG - Centro Ricerche GPI. I acquired my Master degree in 2014 from the university of Trento, and with a focus on semantic web and NLP. My Master of science in Computer science was titled "A semantic matching via web". Before all these, I earned my Bachelor (with high honours) in Information Technology in 2012 from Eastern Mediterranean University EMU - Cyprus. I earned a scholarship grand offered by the Cypriot government. My research aims to identify the correlation between diet and chronic diseases, and applying multidisciplinary research to promote healthy diet and lifestyle. I am researching motivational techniques, such as gamification and persuasive technologies to engage users in self-monitoring their condition. Additionally, I am also conducting research on AI and applying conversational UIs as a tool in preventive healthcare and quantified-self. My PhD research is specifically focused on diet management, quantified-self and prevention from chronic disease.
My research is on Modeling opinion, culture and knowledge dynamics using big data.
A theoretical physicist from the University of Milano-Bicocca with a background in statistical mechanics and complex systems dynamics. Currently he is working with the Mobile-and-Social-computing lab (MobS group) at FBK and towards a PhD at the University of Trento, Italy.
WHO? I am PhD student of University of Bologna, Department of Electronics and Information(DEI). My academic supervisor is Prof. Roberto Verdone. However, I have been doing my whole PhD program at FBK/CREATE-NET research center in Povo, Trento Under supervision of Dr. Leonardo goratti.WHAT? The title of my PhD is “Centralized versus distributed Self-Organizing features in 5G small cell networks”. Currently, I have been working on one of the next generations of cellular networks, LTE-LAA, and its co-existence possibilities with WIFI.WHY? The deployment of modern mobile systems has faced severe challenges due to the dramatically increase of users. The situation could be even worse by development of different wireless technologies in the same frequency band. Moreover, the usage of smaller cells (e.g. Micro, femto and wireless LAN), coexistence among heterogeneous networks (such as LTE and Wi-Fi deployed in the same frequency band) has been a big field of research in the academy and industry.
Convolutional Neural Networks (CNN) have shown to be very effective for addressing many computer vision tasks being the current state-of-the-art in object and scene recognition and semantic segmentation. In addition, their layers are able to create abstract images representation very effective as features, beating standard handcrafted approaches. However, there is still evidence that CNNs are not strong enough to tackle local variations and their training algorithms are not able to generalize enough to correctly address problems such as domain shift. My PhD goal is to exploit the CNNs power, together with the combination of local features, to clearly leverage the information coming from each part of the image, addressing tasks such as Scene Recognition and Understanding. The main idea is that not focusing on a single image representation helps building classifiers with enhanced generalization capabilities and increased power.
I come from Monte Santa Maria Tiberina, a small village between Umbria and Tuscany. I received my Bachelor degree in Computer and Electronic Engineering from University of Perugia in 2014, continuing my studies at University of Rome "La Sapienza", where I received my Master degree in Artificial Intelligence and Robotics in 2016.
The Silicon Photo-multiplier (SiPM) is replacing the photomultiplier tube in several applications because of its solid-state nature, its better performance and customization level. Different position-sensitive SiPM (PS-SiPM) architectures have been proposed during the past few years. They allow the position of the light impinging on the SiPM active area to be identified using a limited number of readout channels, with an ultimate precision in the order of tens of microns and a sensitivity down to the single photon. The most important applications of PS-SiPMs are currently in the field of medical imaging, in which a scintillator is used for the detection of gamma-ray or X-ray photons. The PS-SiPM is used for the readout of the pixelated or monolithic scintillator with 2-dimensional position sensitivity and very good timing resolution. In this context, FBK has recently developed a new type of PS-SiPM, called Lineraly-Graded SiPM (LG-SiPM), showing beyond stateof-the-art performance during preliminary measurements. The PhD activity will focus on the research and development optimization of this technology, aiming at improving its performance and at extending its potential applications, within the current research programs of TIFPA and FBK.
Public user profiles are valuable source of data for a variety of promotional and marketing activities run by companies and governments. I aim to design a deep learning-based solution that can gather and infer user attributes by efficiently utilising all available user-related information from multiple social media accounts of the same person.
In the remote sensing, modern Synthetic Aperture Radar (SAR) systems have capabilities of acquiring data raising spatial resolution to sub-meter level and/or controlling the wave polarization of both transmitted and received waves. These represent a powerful tool for the multi-temporal analysis of changes in natural and urban areas. If we consider urban areas, SAR image potential in the analysis of small changes (i.e., smaller than the building size) is not fully exploited yet. These changes are associated to specific patterns of amplitude backscattering variation, if we consider same polarization for the incident and the scattered wave; or other parameter, if we consider data with multiple polarization. Thus, my research activity aims at developing backscattering models for changes affecting parts of the buildings (i.e., the roof or one of the façades) It also aims at designing novel advanced methodologies for the detection of changes in multi-temporal SAR images, which exploits the information from the higher level of detail and the different combinations of wave polarization.
Duty cycling Medium Access Control (MAC) protocols have been extensively applied in wireless sensor networks to reduce energy consumption by periodically putting the main radio into sleep mode. However, these protocols suffer from overhearing and idle listening causing considerable energy waste. This energy wastage can be greatly reduced by using a secondary ultra-low power wake-up radio to completely switch off the main radio and only wake it up when there is communication. To date, most effort has been devoted to the hardware level, but no consideration has been dedicated to the design of communication protocols based on wake-up radios. To exploit the full potential of wake-up radios, cross-layer integration at the higher level is required. My research work focuses on the design of novel wake-up radio MAC and Routing protocols with evaluation using test-beds and real-life deployments.
Currently, I am working with some of the great minds in the area of Wireless Sensor Networks in Dynamic Distributed Decentralized Systems (D3S) group, a cross-institutional research group of University of Trento and Bruno Kessler Foundation. My research involves design and optimization of novel cross-layer MAC and Routing protocols for Ultra Low-Power Wake-up Radios to enable Low Energy Internet of Things (LE-IoT).
I received my M.Tech. degree in Electrical Engineering from Indian Institute of Technology Bombay, India in 2014 where I researched on medical image processing. Following that, I worked as a computational nano mask technology engineer at Taiwan Semiconductor Manufacturing Company Limited, Taiwan (2015-2016). Currently I am a PhD student at Remote Sensing for Digital Earth (RSDE) group of FBK working under supervision of Francesca Bovolo and Lorenzo Bruzzone (University of Trento). I am researching on detection of changes from high resolution optical satellite images. Recently deep learning methods have exhibited state-of-the-art performance in many computer vision tasks. Taking that cue, I am investigating whether deep learning techniques can be exploited for remote sensing change detection.
Production plants are relevant examples of Cyber-Physical Systems where a complex engineered system is built from the seamless integration of computational algorithms and physical processes. Production plants present different challenges and needs. However, they share the need for technology that lowers the costs of design and verification, and optimizes the efficiency of production processes. This scenario provides the opportunity to satisfy such need through the development of a new generation of Planning Systems. The present research proposal aims to foster the joined application of Formal Verification (FV) and Temporal Planning in the Plant Automation domain leveraging the model-based approach.
As the number of security-critical, online applications grows, the protection of the digital identities of the users is becoming a growing concern. Strong authentication protocols provide additional security by requiring the user to provide at least two independent proofs of identity for the authentication to succeed. With this work, we want to provide some tools for the analysis and the testing of a specific kind of security protocols: the Strong Authentication Protocols involving mobile devices. These security protocols are widely used for restricting access to critical resources, while their analysis and testing procedure is not fully developed. The aim of this research work is to help security expert to validate and improve the Strong Authentication Protocols design by providing testing tools and guidelines, in order to reach a higher security level for sensitive data access.
Multimedia content analysis more and more relies on advanced machine learning to capture the enormous richness of multi-modal sources (commented videos, images with captions, etc.). At the other side, domain specific knowledge is often available to leverage the content analysis task, but effectively encoding it into machine learning (down to the development of task-specific feature representations) is still an open research issue. The goal of this PhD is to progress on the computer vision side of the problem, to go beyond a mono-modal approach where supervisions for learning are provided explicitly. Instead, I will investigate how structured (background knowledge) and semi-structured data (e.g. text captions and descriptions) can be used to provide implicit supervision to enrich the task-specific visual learning capabilities.
I am a PhD student at the department of Information Engineering and Computer Science of the University of Trento, Italy, in collaboration with Fondazione Bruno Kessler. My research focuses on deep learning techniques for computer vision applications.
Smart camera networks benefit from technological advances in sensing, processing and networking capabilities while offering novel opportunities in distributed computer vision research. Especially when people moving in a structured environment are to be monitored using wearable cameras and/or robotic platforms, the synergic collaboration among such devices exposes challenges to be further explored. This research will investigate opportunities of cooperative sensing and processing for real-time mapping, localisation and tracking in such contexts. The problem will be tackled with in a probabilistic framework with resource allocation capabilities to opportunistically manage the scalability-robustness trade-off. Performance assessment will itself require the definition of novel evaluation criteria.
When engineering secure software systems and services, software testing is one of the prominent practices to detect faults as well as security vulnerabilities. Security testing (also called penetration testing) is a branch of software testing devoted to stress programs with respect to their security features, with the aim of identifying vulnerabilities. The aspects of security testing that will be investigated during the internship include: (1) generating input values (referred to as test payloads), intended to exercise vulnerabilities; (2) evaluating whether such payloads manage to expose an actual vulnerability, i.e., the security oracle. Security testing is highly expensive given the complexity of modern systems, typically providing a wide range of services, and the sophistication of attacks and exploitations. To reduce effort and cost, the focus will be on achieving a high level of automation in security testing.