Assoc. Prof. Battista Biggio

University of Cagliari, Italy

Battista Biggio (MSc 2006, PhD 2010) is Associate Professor at the University of Cagliari, Italy, and co-founder of the cybersecurity company Pluribus One. He has provided pioneering contributions in machine-learning security, playing a leading role in this field. His seminal paper on “Poisoning Attacks against Support Vector Machines” won the 2022 ICML Test of Time Award. His work on “Wild Patterns” won the 2021 Best Paper Award and Pattern Recognition Medal from Elsevier Pattern Recognition. He has managed several research projects, and regularly serves as a PC member for ICML, NeurIPS, and USENIX Security. He chaired IAPR TC1 (2016-2020), co-organized S+SSPR, AISec and DLS, and served as Associate Editor for IEEE TNNLS, IEEE CIM, and Pattern Recognition. He is a senior member of IEEE and ACM, and a member of IAPR and ELLIS.

Speech Title: "Machine Learning Security: Lessons Learned and Future Challenges"

Abstract: In this talk, I will briefly review some recent advancements in machine learning security with a critical focus on the main factors which are hindering progress in this field. These include the lack of an underlying, systematic, and scalable framework to properly evaluate machine-learning models under adversarial and out-of-distribution scenarios, along with suitable tools for easing their debugging. The latter may be helpful to unveil flaws in the evaluation process, as well as the presence of potential dataset biases and spurious features learned during training. I will finally report concrete examples of what our laboratory has been recently working on to enable a first step towards overcoming these limitations, in the context of Android and Windows malware detection.

Assoc. Prof. Miguel Bordallo López

University of Oulu, Finland

Miguel Bordallo López, is Associate Professor of Vision Systems Engineering at the Center for Machine Vision and Signal Analysis (CMVS). He received his doctoral degree in 2014 from the University of Oulu, Finland. Currently, he leads the Multimodal Sensing team, that focuses on using real time computer vision and radio technologies to sense humans. He also works as Senior Scientist (AI) at VTT Technical Research Centre of Finland. Assoc Prof. Bordallo actively participate in two Academy of Finland Flagships, the Finnish Center for Artificial Intelligence and 6G-Flagship, where he coordinates the research area in Distributed Intelligence.

Speech Title: "Transforming Primary Healthcare using Multimodal Computer Vision"

Abstract: Camera-based automated medical diagnosis assistance is an emerging topic of interest as it provides a remote alternative to traditional primary healthcare, since it does not necessarily require personal visits to the health centers.  Computer vision and AI can leverage remote and mobile video data, and they can assist in providing unobtrusive and objective information in a patient's condition. Up to 30 medically relevant symptoms or conditions can be detected or at least assessed objectively using computer vision methods and facial images. Although many advanced computer vision based healthcare and medical diagnosis methods have been demonstrated, their actual implementations as embedded or remote solutions, if existing, are still far from being useful. The problem derives from the implementation challenges arising from explainability, real time computations, communications and cost issues. This talk shows how we could enable the use of computer vision for medical diagnosis using camera-based devices (such as mobile phones) or remote video connections (e.g. video-conference services), embracing the challenges and particularities that derive from real world scenarios.

Prof. Wangmeng Zuo (H-index: 78)

Harbin Institute of Technology, China

Wangmeng Zuo is currently a Professor in the School of Computer Science and Technology, Harbin Institute of Technology. He received the Ph.D. degree in computer application technology from the Harbin Institute of Technology, Harbin, China, in 2007. His current research interests include image enhancement and restoration, image and face editing, object detection, visual tracking, and image classification. He has published over 100 papers in top tier academic journals and conferences. His publications have received 30,000+ citations in terms of Google Scholar. He has served as Area Chairs for ICCV 2019, CVPR 2020/2021, ECCV 2022, and is currently an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, and IEEE Trans. Image Processing.

Speech Title: "Learning Deep Image Restoration Network: From Full- and Self-supervision to Non-ideal Supervision"

Abstract: Deep learning has made unprecedented success in many computer vision tasks, which largely depends on massive well annotated data. However, the annotation cost is expensive and laborious, making unsupervised/self-supervised learning as feasible alternatives for training deep networks. In this talk, we first introduce a novel self-supervised method by learning both noise model and deep denoising network. Subsequently, we present a self-supervised adaptation model for adapting a denoising network to new domains and devices. Then, we show that the ideal ground-truth images are not available for deep ISP and Raw super-resolution, and present our alternative solution for learning deep networks with non-ideal supervision (e.g., color inconsistency and spatial deformation). Finally, we discuss the potential to incorporate self-supervised learning and multiple sensors for learning deep image restoration models.