ICAT 2025

Short Biography

Darko Zibar is currently Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark and the group leader of Machine Learning in Photonics Systems (MLiPS) group. He received M.Sc. degree in telecommunication and the Ph.D. degree in optical communications from the Technical University of Denmark, in 2004 and 2007, respectively. He has been a Visiting Professor at Politecnico di Torino, Friedreich Alexander University of Erlangen, University of California Santa Barbara and University of Colorado, Boulder. His research efforts are currently focused on the application of digital signal processing and machine learning techniques to advance classical and quantum optical communication and measurement systems. Some of his major scientific contributions include: record capacity hybrid optical-wireless link (2011), record sensitive optical phase noise measurement technique that approaches the quantum limit (2021) and record-bandwidth (S+C+L band) programmable gain Raman amplifier (2019). He is the recipient of Young Researcher Award by University of Erlangen-Nurnberg (2016), European Research Council (ERC) Consolidator Grant (2017), Alexander von Humboldt Foundation Bessel Research Award, (2021), and Villum Investigator Award (2023). Finally, he was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers (2016).

Abstract

The 2024 Nobel Prize in Physics underscores the growing influence of machine learning in diverse areas of physical science. In the field of photonics, machine learning is proving invaluable for tasks such as optimizing and designing fiber-optical communication systems, optical amplifiers, noise characterization of frequency combs, inverse design of photonic components, and quantum-noise-limited signal detection. In this talk, I will review notable applications of machine learning in photonics and explore future directions in this emerging field. Specifically, I will highlight its role in phase noise characterization of optical frequency combs, end-to-end learning for fiber-optic communication, and realization of programmable ultra-wideband Raman amplifiers. Lastly, I will introduce an exciting new application of machine learning: controlling nonlinear interactions in highly nonlinear waveguides.

Short Biography

Bruno Siciliano is professor of robotics and control at the University of Naples Federico II. He is also Honorary Professor at the University of Óbuda where he holds the Kálmán Chair. His research interests include manipulation and control, human–robot cooperation, and service robotics. Fellow of the scientific societies IEEE, ASME, IFAC, AAIA, AIIA, he received numerous international prizes and awards, including the recent 2024 IEEE Robotics and Automation Pioneer Award. He was President of the IEEE Robotics and Automation Society from 2008 to 2009. He has delivered more than 150 keynotes and has published more than 300 papers and 7 books. His book “Robotics” is among the most adopted academic texts worldwide, while his edited volume “Springer Handbook of Robotics” received the highest recognition for scientific publishing: the 2008 PROSE Award for Excellence in Physical Sciences & Mathematics. His team has received more than 25 million Euro funding in the last 15 years from competitive European research projects, including two ERC grants.

Abstract

This talk will present the underlying concepts of EndoTheranostics, a novel ERC Synergy Grant project aiming at revolutionizing the diagnosis and therapy (theranostics) of colorectal cancer (CRC), impacting the quality of life of millions of individuals. CRC represents a significant proportion of malignant diseases. Interventions are often carried out during the latter stages of development, leading to low patient survival rates and poor quality of life. In 2022 a European Commission report stated that “colonoscopy-based screening has higher sensitivity than testing for blood in stool, but it is less acceptable to participants”. At the same time, effective methods to treat polyps in the colon are limited. Current approaches are often associated with unsafe oncological margins and high complication rates, requiring life-changing surgery. EndoTheranostics will usher in a new era for screening colonoscopy, advancing the frontiers of medical imaging and robotics. A tip-growing or eversion robot with a sleeve-like structure will be created to extend deep into hollow spaces while perceiving the environment through multimodal imaging and sensing. It will also act as a conduit to transfer miniaturized instruments to the remote site within the colon for theranostics. With these capabilities, the system will be able to offer: (i) painless colon cleansing in preparation for endoscopy, (ii) real-time polyp detection and tissue characterization through AI-assisted multimodal imaging, (iii) effective removal of polyps by conveying a “miniature mobile operating chamber” equipped with microsurgical tools to the target through the lumen of the eversion robot.

Short Biography

Francesco is senior professor in Computer Science of Free University of Bozen-Bolzano (UNIBZ), Italy. He is member of the UNIBZ Competence Center on Sustainability and member of the Steering Committee of the Digital Humanism Initiative, TUW, Vienna. His current research interests are multidisciplinary and focus on the usage of Artificial Intelligence techniques, such as Recommender Systems, in supporting human collaboration with AI, for improving users' decision making processes and wellbeing. He is interested in building systems that can help groups and individuals to make more rational and fair decisions in complex and context dependent scenarios, such as, what information to consume and how, or what places to visit together in a group. He is the author of more than 250, highly cited, refereed publications. He coedited the Recommender Systems Handbook, a reference publication, and served as president of the steering committee of the ACM conference on RSs.

Abstract

Recommender systems (RSs) are personalised AI tools used online platforms to promote algorithmically identified news, posts, music, and videos. Their core component is a machine learning algorithm exploiting very sparse users' online behaviour data to estimate what items, in a predefined catalogue, should be presented, to each user in a personalised way, to achieve a desired platform goal (e.g., increase revenues and user satisfaction). After having briefly discussed RSs value, and the risks that user faces in interacting with RSs, I will focus on the role and importance of a proper evaluation of these artifacts, which is also a mandatory request formulated in the European legislation (Digital Service Act). Classical machine learning evaluation approaches, which are based on offline testing the learned predictive models on holdout past data, are insufficient to asses the effect of new algorithms (interventions). A new type of simulation-based evaluation approach will be therefore introduced: it enables to estimate the potential effects (positive and negative) of an RS on the choices made by their users when they are influenced by the received recommendations. The application of this evaluation approach will be exemplified in a particular case: sustainable recommendations in tourism, namely, how to tame over tourism and respect local communities.