Our Digital Life Podcast: A series by IEEE-SPS

In this episode, Prof. Hung-yi Lee, Professor at National Taiwan University, interviews Dr. Jinyu Li, Partner Applied Science Manager at Microsoft. Their conversation traces the evolution of speech translation from traditional cascaded pipelines to today's end-to-end, large language model (LLM)-powered systems. 
Jinyu Li
Dr. Jinyu Li is Partner Applied Science Manager at Microsoft, where he leads a science team advancing speech, translation, and language technologies. He is an IEEE Fellow and an ISCA Fellow for contributions to deep-learning-based speech technology innovation, adoption, and commercialization. He has served on the IEEE Speech and Language Processing Technical Committee and as Vice Chair starting in 2026. He previously served as Associate Editor of IEEE/ACM Transactions on Audio, Speech, and Language Processing and is a Distinguished Industry Speaker for the IEEE Signal Processing Society. He has also been awarded the IEEE SPS Best Paper Award (2025), the APSIPA Industrial Distinguished Leader Award (2021), and the APSIPA Sadaoki Furui Prize Paper Award (2023).
In this episode, Dr. Li discusses the evolution of AI-powered speech translation, from conventional cascaded systems to modern end-to-end architectures, while highlighting the role of large language models in enabling real-time multilingual communication. He also explores the latest applications of speech translation and examines the technical challenges that remain.

In this episode, Nantheera Anantrasirichai (Pui), Professor of Visual Computing at the University of Bristol, interviews Professor Alin Achim, Chair of Computational Imaging at the University of Bristol. Their conversation focuses on synthetic aperture radar (SAR), interferometric synthetic aperture radar (InSAR), and the role of computational imaging, signal processing, and machine learning in earth observation.
Alin Achim
Professor Alin Achim is Chair of Computational Imaging at the University of Bristol. He received his B.Sc. and M.Sc. degrees in electrical engineering from “Politehnica” University of Bucharest, Romania, and his Ph.D. in Biomedical Engineering from the University of Patras, Greece. He subsequently held an ERCIM Postdoctoral Fellowship at ISTI-CNR in Pisa, Italy, and INRIA Sophia Antipolis, France, before joining the University of Bristol in 2004. Since August 2018, he has held the Chair of Computational Imaging. 
His research interests include statistical signal, image, and video processing and machine learning, with applications in biomedical imaging and earth observation. He has coauthored more than 200 scientific publications, including 66 journal articles, and has contributed to several IEEE technical committees and editorial boards. In this episode, Professor Achim highlights computational imaging, statistical signal and image processing, and machine learning in the context of earth observation.

In this episode, Marco Huber speaks with Marija Ivanovska, Assistant at the Faculty of Electrical Engineering, University of Ljubljana, Slovenia. They discuss face morphing attacks and their impact on biometric identity systems, exploring how morphed images can deceive face recognition technologies used in passports and ID documents. The conversation also examines emerging approaches to detecting these attacks, including foundation models, multimodal AI, synthetic data, and other advances in trustworthy biometrics.
Marija Ivanovska
Marija Ivanovska is an Assistant at the Faculty of Electrical Engineering, University of Ljubljana, Slovenia, and a member of the Laboratory for Machine Intelligence. Her research focuses on biometric security, computer vision, and trustworthy artificial intelligence, with particular emphasis on face-based attacks on biometric systems and the use of foundation models and multimodal large language models for robust biometric security. She has held international research appointments as a Visiting Scholar at Johns Hopkins University and as a Visiting Researcher at Queensland University of Technology, fostering collaborations in biometrics and trustworthy AI. She has authored numerous publications in peer-reviewed journals and conferences in computer vision, biometrics, and information forensics.
She is actively involved in the international research community through editorial, organizational, and reviewing roles. Marija is a member of the IEEE Information Forensics and Security Technical Committee and the IAPR Technical Committee on Biometrics and serves on the Webinar Committee of the IEEE Biometrics Council. She is passionate about advancing trustworthy biometric technologies and encouraging collaboration in AI and biometrics to help build more secure, reliable, and resilient digital identity systems.

In this episode of the IEEE Signal Processing Society podcast, Dr. Yan Qiao, an Associate Professor at the School of Computer Science and Information Engineering, Hefei University of Technology, interviews Professor Meng Li from Hefei University of Technology, whose research focuses on applied cryptography, secure and trustworthy data sharing, blockchain, privacy preservation, and Trusted Execution Environments (TEE). 
Professor Meng Li
Professor Meng Li is a Professor at the School of Computer Science and Information Engineering, Hefei University of Technology (HFUT), China. He earned his Ph.D. in Computer Science and Technology from the Beijing Institute of Technology and has held several international research appointments, including postdoctoral positions in Italy supported by the ERCIM "Alain Bensoussan" Fellowship Programme and research collaborations at the University of Waterloo, Wilfrid Laurier University, and the University of Padua. His research focuses on secure and trustworthy data sharing, privacy preservation in the Internet of Vehicles (IoV), applied cryptography, blockchain, and Trusted Execution Environments (TEE). He is a Senior Member of IEEE, CCF, CACR, CIE, and CIC, and has been recognized with the 2024 IEEE HITC Award for Excellence (Early Career Researcher), the 2025 IEEE TCSVC Rising Star Award, and selection among the IEEE Computer Society Computing's Top 30 Early Career Professionals for 2025.
In this episode, Professor Li explains why secure and trustworthy data sharing has become a cornerstone of today's digital world. He discusses how technologies such as cryptography, blockchain, and privacy-preserving techniques help enable secure data sharing while protecting user privacy and explores the role of emerging technologies in addressing modern data-sharing challenges and advancing humanitarian and societal applications.

In this episode of the IEEE Signal Processing Society podcast, Dr. Rogério Augusto Bordini, a Post-doctoral Researcher and Science Journalist at the Artificial Intelligence Lab., Recod.ai, University of Campinas (Unicamp), interviews Dr. Anderson Rocha, Full Professor at the University of Campinas (Unicamp) specializing in Artificial Intelligence, Digital Forensics, and Reasoning for Complex Data. Their conversation explores how modern signal-processing techniques have been explored in various social sectors.
Dr. Anderson Rocha  
Professor Anderson Rocha, Former Director of Unicamp's Institute of Computing and two-time Chair of the IEEE Information Forensics and Security Technical Committee, was named an IEEE Fellow in 2023, an IEEE SPS Distinguished Lecturer in 2025, and an IEEE Biometrics Council Distinguished Lecturer also in 2025. Closing a remarkable year, he was awarded the prestigious Zeferino Vaz Prize—Unicamp's highest recognition. Recognized as one of the world's top scientists by Stanford, PLOS ONE, and Research.com, he holds fellowships from Microsoft and Google and co-founded the Recod.ai AI Lab at Unicamp over 16 years ago.
In this episode, Dr. Rocha discusses applications of signal processing spanning digital forensics, wearable sensors, deepfake detection, and misinformation mitigation, while highlighting core algorithms, real-world healthcare applications, and emerging AI-driven forensic tools, and also provides insights into his research group's key differentiator—a humanistic, expert-in-the-loop approach to solution design.