Latest News
Marco Grangetto special guest on the RBL channel EULERO show, talking about Computer Vision and prejudice.
EIDOS is very proud to contribute to DCC 2024:
- Domain Adaptation for Learned Image Compression with Supervised Adapters by Presta, Alberto; Spadaro, Gabriele; Tartaglione, Enzo; Fiandrotti, Attilio; Grangetto, Marco;
Congratulations to Alberto Presta for presenting his work with an amazing oral talk.
The Data Compression Conference (DCC) is an international forum for current work on data compression and related applications.
Congratulations to Carlo Alberto Barbano for successfully defending his PhD thesis, titled “Collateral-Free Learning of Deep Representations: From Natural Images to Biomedical Applications”. The committee awarded his defense with honors.
The committee was composed by:
- Prof. Daniel RUECKERT – Dept. Computing - Imperial College London;
- Prof. Hervé LOMBAERT - ETS Montreal;
- Prof. Rosa MEO – Dip. Informatica – Università di Torino;
- Prof. Enrico MAGLI – Dip. Elettronica e Telecomunicazioni – Politecnico di Torino;
- Prof. Isabelle BLOCH - Télécom Paris – Institut Polytechnique de Paris;
- Prof. Marco GRANGETTO - Dip. Informatica – Università di Torino;
- Prof. Pietro GORI - Télécom Paris – Institut Polytechnique de Paris …
PhD Supervisor: Marco Grangetto (University of Turin), Pietro GORI (Télécom Paris – Institut Polytechnique de Paris), Isabelle BLOCH (Télécom Paris – Institut Polytechnique de Paris)
Congratulations to Riccardo Renzulli for successfully defending his PhD thesis, titled “Hierarchical Object-centric Learning with Capsule Networks”. The committee awarded his defense with honors.
The committee was composed by:
- Prof. Peter Sincak, TU Kosice
- Prof. Georgios Leontidis, University of Aberdeen
- Prof. Lia Morra, Polytechnic University of Turin
- Prof. Attilio Fiandrotti, University of Turin
PhD Supervisor: Marco Grangetto (University of Turin)
One postdoc/postgrad position is available at the University of Turin on the topic AI for cardiovascular imaging. The initial appointment is for 12 months with the possibility of renewal.
The relevance of Machine Learning (ML) in cardiology has grown over the years. While the field applied to risk prediction on tabular variables has been extensively explored, it remains highly relevant to delve into the field of cardiovascular imaging evaluation.
In particular, this grant will be aimed at the following objectives:
- Develop image analysis in biomedical field (non-invasive images such as echocardiograms, or invasive images like intracoronary or intravascular imaging).
- Implement clinical event prediction from these types of images using machine learning.
- Implement a comparison between invasive and non-invasive methods using machine learning.
The research activities will be carried out within an interdisciplinary team including Cardiologists and EIDOS group.
For inquiries or further information, please contact prof. Marco Grangetto, prof. Fabrizio D’Ascenzo
The selection process will open in November.
We are inviting applications for a postgrad/postdoc position in the field of AI for surgery, with a specific focus on the following areas:
- Minimally Invasive Surgery: Endoscopy, Laparoscopic Surgery, and Robotics
- Prostate, Renal, and Adrenal Neoplasms
This position offers an exciting opportunity for young researchers interested in advancing the application of artificial intelligence in surgical procedures. The successful candidate will have the chance to work on cutting-edge projects and contribute to the development of AI-driven solutions in the context of minimally invasive surgery and the treatment of prostate, renal, and adrenal neoplasms.
Key Responsibilities:
- Conduct research in the field of AI and its applications in surgery. Collaborate with surgeons and medical professionals to develop AI-based tools and technologies.
- Analyze medical data, including imaging and diagnostic information.
- Contribute to the development of AI algorithms and models for surgical planning and decision support.
- Publish research findings in reputable scientific journals and conferences.
For inquiries or further information, please contact prof. Marco Grangetto, prof. Cristian Fiori.
The selection process will open in December.
EIDOS is very proud to contribute to ICCV 2023:
- Shannon Strikes Again! Entropy-Based Pruning in Deep Neural Networks for Transfer Learning Under Extreme Memory and Computation Budgets by Gabriele Spadaro; Riccardo Renzulli; Andrea Bragagnolo; Jhony H. Giraldo; Attilio Fiandrotti; Marco Grangetto; Enzo Tartaglione
Congratulations to Gabriele Spadaro for presenting his work during the poster session.
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.
EIDOS is very proud to contribute to ICIAP 2023:
- A differentiable entropy model for learned image compression by Presta, Alberto; Grangetto, Marco; Fiandrotti, Attilio; Tartaglione, Enzo
- Two is Better than One: Achieving High-Quality 3D Scene Modeling with a NeRF Ensemble by Di Sario, Francesco; Renzulli, Riccardo; Tartaglione, Enzo; Grangetto, Marco
- A Learnable EVC Intra Predictor using Masked Convolutions by Spadaro, Gabriele; Iacoviello, Roberto; Mosca, Alessandra; Valenzise, Giuseppe; Fiandrotti, Attilio
- Towards One-Shot PCB Component Detection with YOLO by Spadaro, Gabriele; Vetrano, Gaspare; Penna, Barbara; Serena, Antonio; Fiandrotti, Attilio
Congrats to Andrea Bragagnolo for the successful defense of his PhD thesis: “Exploring the Design and Implementation of Pruning Techniques for Deep Neural Networks”. The committee awarded his defense with honors.
The committee was composed by:
- Rossella Cancelliere, University of Turin
- Prof. Alasdair Newson, Télécom Paris, IP Paris
- Prof. Nicu Sebe, University of Trento
PhD Supervisors: Marco Grangetto (University of Turin), Andrea Basso (Synesthesia).
Congrats to Mirko Zaffaroni for his successful PhD defense. His thesis was title “Multimodal Learning and Feature Fusion Methodologies for Real Case Scenarios”.
The committee was composed by:
- Francesco Verdoja, Aalto University
- Prof. Laura Toni, UCL
- Prof. Attilio Fiandrotti, University of Turin
PhD Supervisor: Marco Grangetto (University of Turin)
