In this episode of the IEEE Signal Processing Society Podcast, Hemang Chawla, Solutions Lead at Scantrust, speaks with Justin Picard, Co-founder and CTO of Scantrust. Their conversation explores how modern signal processing, printing physics, and machine learning are being combined to combat the global problem of product counterfeiting through secure QR codes and copy detection technology.

Dr. Justin Picard

Dr. Justin Picard is the Co-founder and Chief Technology Officer of Scantrust, a company specializing in product authentication and traceability solutions. Originally from Canada and now based in Switzerland, Dr. Picard completed his Ph.D. in artificial intelligence before moving into digital watermarking and image security. After working in research and development roles across North America and Europe, Dr. Picard co-founded Scantrust to develop smartphone-based authentication systems that empower consumers and brands to verify product authenticity in real time.

In this episode, Dr. Picard discusses the trillion-dollar global impact of counterfeiting, which now affects not only luxury goods but also everyday products such as food, industrial components, health supplements, and consumer goods—an issue intensified by e-commerce and global supply chains. He explains that traditional anti-counterfeiting methods, including holograms, UV inks, and forensic testing, struggle to scale in today’s digital marketplace because they rely on specialized equipment or human inspection.

Functional Brain Imaging: Signals, Imaging, and Graphs

In this episode of the IEEE Signal Processing Society Podcast, Professor Borbála Hunyadi from the Mental Health and Neuroscience Research Institute, Maastricht University, The Netherlands interviews Dr. Dimitri Van De Ville, Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and the University of Geneva, Switzerland. Their conversation explores how modern neuroimaging modalities, combined with advanced signal processing and computational methods, are transforming our understanding of brain function in health and disorder.

Dr. Dimitri Van De Ville

Dr. Dimitri Van De Ville received his M.S. and Ph.D. degrees from Ghent University, Belgium, in 1998 and 2002, respectively. He was a postdoctoral fellow at EPFL before leading the Signal Processing Unit at the University Hospital of Geneva as part of the CIBM Center for Biomedical Imaging. Since 2024, he has been a Full Professor at EPFL’s Neuro-X Institute with a joint appointment at the University of Geneva. His interdisciplinary research focuses on computational neuroimaging, wavelets, sparsity, and graph signal processing, applied to MRI and M/EEG data.

In this episode, he discusses current and emerging neuroimaging modalities such as intracranial recordings, fMRI, fNIRS, M/EEG, and functional ultrasound (fUS). He highlights how signal processing plays a vital role in data formation, preprocessing, and analysis, enabling researchers to extract meaningful information about brain activity. The discussion also touches on innovations such as independent component analysis, connectomics, and the growing influence of AI and deep learning in neuroimaging. Dr. Van De Ville concludes by reflecting on the field’s future—emphasizing multimodal integration, brain–body connectivity, and targeted neuromodulation as key directions for advancing both neuroscience research and clinical applications.

In this episode of the IEEE Signal Processing Society podcast, Felicia Lim, a staff software engineer at Google, where she works on audio signal processing and machine learning, interviews Dr. Ivan Tashev, Partner Software Architect at Microsoft Research (MSR) – Redmond USA, where he leads the Audio and Acoustics Research Group. Their conversation explores the rapid development of novel algorithms in AI and their impact on the audio processing domain.

Dr. Ivan Tashev

Dr. Ivan Tashev is a Partner Software Architect at MSR in Redmond, WA, USA, where he leads the Audio and Acoustics Research Group and also coordinates the Brain-Computer Interfaces project. He is an Affiliate Professor in the Department of Electrical and Computer Engineering at the University of Washington in Seattle, USA, and an Honorary Professor at the Technical University of Sofia, Bulgaria. He is also an IEEE Fellow and a member of the Audio Engineering Society (AES) and the Acoustical Society of America (ASA).

In this episode, Dr. Tashev discusses the unique challenges of audio signal processing as a specialized domain, examining why traditional statistical methods have limitations and how machine learning and AI approaches offer new solutions. He also talks about the future trajectory of machine learning and AI in transforming audio signal processing capabilities.