Portfolio Archives: Research Projects

Enfield – European Lighthouse to Manifest Trustworthy and Green AI

ENFIELD will create a unique European Centre of Excellence that excels the fundamental research in the scientific pillars of Adaptive, Green, Human-Centric, and Trustworthy AI that are new, strategic and of paramount importance to successful AI development, deployment, and acceptance in Europe and will further advance the research within verticals of healthcare, energy, manufacturing and space by attracting the best talents, technologies and resources from world-class research and industry players in Europe and by carrying out top-level research activities in synchronisation with industry challenges to reinforce a competitive EU position in AI and create significant socio-economic impact for the benefit of European citizens and businesses.

UPB is leading WP4: Common vision and roadmaps.

Project funded under topic HORIZON-CL4-2022-HUMAN-02-02.

Duration: 2023-2025

MERITT – Maintaining Active and Healthy Living Through Serious Games and Artificial Intelligence

The elderly population worldwide faces medical, psychological, emotional and social limitations that reduce their quality of life and prevent them from actively participating in everyday life. The MERITT project proposes a coherent set of activities (and related modules and applications) that positively influence autonomy, participation in social life, and the accumulation of digital skills and competences for the elderly population.

To this end, the MERITT project exploits gamification to motivate and educate older people to maintain an active life while maintaining social connections. In addition, it proposes a strategy for developing digital skills in a fun and engaging way. Thus, addressing both the health challenges brought by aging, with the needs of relationships and social functioning, the MERITT project offers a comprehensive continuum of support services.

Project number PN-III-P2-2.1-PTE-2021-0255.

Duration: 2023-2025

CNCC – Creation, Operationalization and Development of the National Center of Competence in the field of Cancer

Developing and implementation of a Competence Center in the field of Cancer entitled: Creation, Operational and Development of the National Center of Competence in the field of Cancer (NCCC) is focused on improving the management of cancer patients by developing integrated programs based on the recent advances in personalized medicine, telemedicine, molecular tumor characterization, imaging systems and artificial intelligence.

Project funded under topic Establishment and Operationalization of Competence Centers, PNRR-III-C9-2022 – I5.

Duration: 2023-2025

Virtual Patient – An AI-based System for Training in Cardiovascular Diseases Diagnostic and Treatment

Virtual patients are able to represent patients in realistic clinical scenarios and engage learners in doctor-patient conversations about the patient’s health, interpret laboratory results and medical images, and form a diagnosis. Currently, due to the conditions created by the COVID-19 pandemic, the interaction of medical students with patients is limited, therefore virtual doctor-patient interaction environments become a safe and practical solution for training medical skills, including blended learning. With the current advances in artificial intelligence, virtual patients can be equipped with advanced functions, such as the integration of different types of conversational agents or support for the automatic prediction of the patient’s evolution.

In this context, the main objective of the project is to develop a virtual environment to simulate the interactions between the learner (medical student or doctor/resident) and a virtual clinical patient for the diagnosis and treatment of acute and chronic heart diseases. The virtual environment, supported by a software platform and artificial intelligence technologies, simulates real-life clinical scenarios in which the student emulates the role of the doctor who performs an examination of the virtual patient and obtains a history, performs anamnesis, physical examination, paraclinical investigations, establishes a diagnosis and recommends a therapeutic plan.

Duration: 2022-2024

Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases

In healthcare, the cost of treating brain disorders accounts for a significant percentage of total treatment costs. In Europe, the budget for treating brain disorders alone far surpasses the combined budget for other diseases. Utilising cutting-edge AI, the EU-funded ALAMEDA project could greatly reduce the cost of treating these conditions by enabling personalised care and improved treatments for major brain disorders. The goal is to demonstrate AI-enabled prediction, prevention and intervention, making the treatment of disorders such as Parkinson’s, multiple sclerosis and strokes more affordable and easing the burden on healthcare systems across Europe.

ALAMEDA is a truly pan-European project and brings together a highly skilled and complementary consortium of 15 expert organisations from across 8 countries. University Politehnica of Bucharest is coordinating the WP3.

The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101017558.

Duration: 2021-2023

Visit the ALAMEDA Official website.

Visit the ALAMEDA National Own Funding project website.

CASHMERE – Context-Aware Search and Discovery in Hypermedia-Driven Multi-Agent Environments

In hypermedia-driven Multi-Agent Systems (HyperAgents) the web and its architecture act like a global environment in which different types of agents (whether human or software) perform interactions and compose the functionality of web-enabled things to achieve their goals.

The main objective of this project is the development of a web-based framework facilitating context-aware search and discovery of web-enabled things in hypermedia driven environments. The requirement is to facilitate the detection and understanding of shared context between a producer and a consumer to enable corresponding discovery of and access to relevant web resources to agents operating in the web environment.

To achieve its objective the project aims to:

1) Develop a web-based platform that can expressively model relevant context information of web things and enable a semantic-query and complex-event processing based mechanism to define and detect situations of shared context between web things and agents;

2) Develop a web-based APIs that take the detection of a shared context as input and are able to facilitate the creation of authorization elements that integrate into existing access control and search technologies for web things.

Duration: 2021-2023

AI Folk – Resource Management in Distributed AI

Implementing a vision in which intelligent agents perceive and act in an environment while searching for, exchanging, annotating, and improving machine learning models, forming a culture based on experience and interaction.

Our research goal is to develop a knowledge model and an interaction protocol which allow a system formed of multiple actors — human, software, or organizational — to find, use and share improvements on machine learning resources. Such resources can be datasets, models, or experiences.

We propose the development of the AI Folk framework and methodology, at the intersection of machine learning, knowledge management, and multi-agent systems. It comprises tools and methods that allow the management and discovery of ML-related resources in a distributed system. Federated learning has yet to achieve maturity as a field of study and this is a novel approach which assumes an open system and a variety of resources. We believe that this approach will lead to an advance in the state of the art and will help in the development of standards for open and distributed artificial intelligence.

The project result will be an ontology for describing machine learning resources, a methodology for creating searches for resources, an interaction protocol through which actors can search, transfer and update machine learning resources, the implementation of two applications using this approach, and a general methodology for applying the proposed approach to other application domains.

Duration: 2022-2024

People Detection and Tracking for Social Robots and Autonomous Cars

Detecting, tracking, and recognizing people is a valuable capability for machines. However, these tasks are quite difficult to be achieved autonomously and, although significant results have been obtained, they are still a major technological challenge. People tracking, unlike other recognition and interpretation tasks, is difficult both from the point of view of the recognition and prediction of trajectory, and from the one of the identifications of the ground truth.

The main objective of the PETRA project is the development of a software platform enabling the development of applications requiring people detection and tracking in real environments. The design and implementation of the platform and the set of supported algorithms for people detection and tracking will be such that they can be easily used and integrated in tasks performed by social robots in closed spaces and in tasks in open spaces, such as the case for pedestrian detection and tracking. The scientific and technological challenge of the project is to start from our current developments on people detection and tracking in the contexts of user-robot interaction and autonomous driving to develop and implement novel solutions based on deep learning approaches. One of the project challenges is to extensively test the implementations towards several difficult benchmarks, but also on our own data sets, and strive to achieve results better than current state-of-the-art.

Duration: 2020-2022

Cognitive Systems for Personal Robots and Autonomous Vehicles

ROBIN is a user centered project which develops systems and services for the use of robots in an interconnected digital society. It comprises 5 sub-projects. ROBIN-Social develops integrated and configurable solutions for the personalization of assistive and social autonomous robots. ROBIN-Car develops computer vision methods devoted to autonomous driving and a prototype systems to be tested on an electric car. ROBIN-Context creates a support platform for the semantic representation and management of data that becomes context in scenarios of personalized robotic assistance and AADS. ROBIN-Dialog develops a set of scenarios for micro-worlds and the technology for the Romanian language processing to achieve situational dialogs in these micro-worlds. ROBIN-Cloud builds a support platform for collecting data coming from the sensors of robotic systems and IoT devices that offers Cloud Edge and Cloud Robotics computing.

The ROBIN project is financed by UEFISCDI “Complex Projects” programme, PNIII.

Duration: 2018-2020

Ecosystem for Research, Innovation and Development of IoT Based Products and Services to Sustain an Interconnected Society

NETIO Objectives: development of innovative products and services in the domain of IoT and Big Data, empowering innovation activities of companies in the domains of IoT and Big Data through knowledge transfer and common research activities with UPB, implication of UPB staff in all stages of technological transfer for IoT and Big Data based products and services.

Project co-financed by the European Fond for Regional Development in the framework of the Operational Programme for Competitiveness 2014-2020.

Duration: 2017-2020