Our Digital Life Podcast: A series by IEEE-SPS – Détails, épisodes et analyse

In this episode of the IEEE Signal Processing Society podcast, Dennis K. Chrogony, Education Board Outreach and Visibility Committee Member interviews Sergios Theodoridis, Professor Emeritus, Signal Processing and Machine Learning, National and Kapodistrian University of Athens, Greece, Aalborg University, Denmark, and Shenzhen Research Institute of Big Data, Chinese University of Hong Kong, China. They delve into the evolution and impact of signal processing and AI and machine learning on technological advancements.

Professor Sergios Theodoridis
Prof. Theodoridis has made countless contributions to the field of signal processing and machine learning. He has authored several books, received multiple awards, and earned prestigious accolades like the EURASIP Athanasios Papoulis Award and the IEEE Signal Processing Society Education Award. His leadership positions, including his role as Vice President of the IEEE Signal Processing Society highlight his significant influence and respect within the academic and professional community.

In this episode, Prof. Theodoridis discusses the advancements in various areas of signal processing, highlighting AI and machine learning as pivotal technologies in modern society. He explains how the seamless integration of machine learning into signal processing has led to significant improvements in areas like speech and audio recognition and natural language processing. 

He also notes that while the core goals of signal processing remain unchanged, new techniques from the machine learning community have greatly enhanced its applications.

In the first podcast sponsored by the IEEE Signal Processing Society, Ervin Sejdić, Professor at University of Toronto’s Edward S. Rogers Sr. Department of Electrical & Computer Engineering interviews April Khademi, Associate Professor of Biomedical Computer and Electrical Engineering at Toronto Metropolitan University and Canada Research Chair in AI for Medical Imaging.

April Khademi

April, an expert in AI-driven medical image analysis, focuses on AI-driven signal processing methods that have revolutionized medical imaging, improving diagnostic and therapeutic accuracy. In this podcast, she discusses how AI advancements continuously push the boundaries of medical imaging, providing clinicians with robust, quantitative disease metrics and enhancing overall healthcare quality.

AI-driven foundation models in medical imaging, like MRI and CT, enhance object detection and segmentation. They mitigate data constraints and enable fine-tuning for smaller datasets. FDA-approved algorithms in image acquisition improve efficiency, allowing faster, lower-dose scans and super-resolution for better image quality, yielding substantial business benefits. AI also enhances interrater agreement in medical image interpretation, reducing subjectivity and varying expertise among clinicians. It ensures consistent, accurate diagnoses, especially in community hospitals without specialized pathologists, ultimately leading to more reliable patient treatment outcomes.

In this episode of the IEEE Signal Processing Society podcast, Prof. Samson Lasaulce, Chief Research Scientist at Khalifa University (KU) interviews Prof. Merouane Debbah, founding Director of the KU 6G Research Center. They talk about how AI is transforming the future of wireless communications, the use of large language models (LLMs) to revolutionize network management, improve communication protocols, and advance automation.

About the speaker 
Mérouane Debbah is a Professor at Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. His research bridges mathematics, algorithms, statistics, and communication sciences, with a focus on random matrix theory and learning algorithms. He has played a pivotal role in advancing small cells (4G), Massive MIMO (5G), and Large Intelligent Surfaces (6G) technologies. He is a frequent keynote speaker at international events in the field of telecommunication and AI.

In this episode, Professor Debbah explores the transformative potential of LLMs in wireless communication. Key applications include dynamic spectrum management, end-to-end system optimization, fault detection, mobility management, and AI-based localization. These techniques enhance data handling and system performance by replacing complex modeling with efficient data-driven approaches. These insights shed light on how 6G, powered by pervasive AI, will revolutionize wireless networks and redefine communication frameworks for the future.

In this episode of the IEEE Signal Processing Society podcast, Dr. Sanjeev Khudanpur, Director of the Center for Language and Speech Processing, Johns Hopkins University interviews Associate Prof. Shinji Watanabe, Language Technologies Institute, Carnegie Mellon University. They talk about the latest research and innovations in speech recognition technologies and their impact across various industries.

Shinji Watanabe

Shinji Watanabe is an Associate Professor at Carnegie Mellon University in Pittsburgh and a leading researcher in speech and language processing. His work spans automatic speech recognition, speech enhancement, spoken language understanding, and machine learning for speech and language processing. He has contributed more than 500 publications to peer-reviewed journals and received several awards, including the best paper award from ISCA Interspeech 2024. 

In this episode, Associate Prof. Watanabe reflects on the transformative progress in speech recognition over the past decade, highlighting milestones from the adoption of deep neural networks to the rise of large-scale models like OpenAI Whisper. He discusses the ongoing challenges in achieving human-level understanding in complex scenarios such as multi-speaker conversations, accented and multilingual speech, and child or disordered speech. He concludes with thoughts on academia’s enduring role in shaping the field, and how his inspiration is often drawn from science fiction and Japanese animation.

In this episode of the IEEE Signal Processing Society podcast, Dr. Lav Varshney, Associate Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign interviews Dr. Kush Varshney, an IBM Fellow and globally recognized expert in trustworthy machine learning. Their conversation explores the multifaceted landscape of trustworthy AI.

Kush Varshney

Kush R. Varshney is an IBM Fellow at IBM Research and a leading authority on trustworthy AI. His work focuses on making AI systems not only accurate but also fair, robust, explainable, transparent, inclusive, and beneficial. He is the author of a book entitled “Trustworthy Machine Learning” and creator of widely used toolkits like AI Fairness 360 and AI Explainability 360.

In this episode, Dr. Varshney outlines the core principles of trustworthy AI and distinguishes it from related concepts such as AI ethics, AI safety, and responsible AI. He shares how signal processing techniques—like Boolean compressed sensing and continued fraction representations, and short-time Fourier transforms—inform his approach. The conversation covers the societal impact of AI, the shift toward generative and agentic models, the importance of governance and policy, and new research directions aimed at building more empowering and accountable AI systems.

In this episode of the IEEE Signal Processing Society podcast, Nir Shlezinger from Ben-Gurion University and Yonina C. Eldar from the Weizmann Institute of Science discuss the design of machine learning systems that are inherently efficient. 

Nir Shlezinger and Yonina C. Eldar

Nir Shlezinger is an Assistant Professor in the School of Electrical and Computer Engineering at Ben-Gurion University of the Negev, Israel. His research spans signal processing, machine learning, and communications. He has been recognized with several prestigious awards, including the IEEE Communications Society Fred W. Ellersick Prize and the 2024 Krill Award.

Yonina C. Eldar is a Professor at the Weizmann Institute of Science, where she heads the Center for Biomedical Engineering and Signal Processing. She is also a member of the Israel Academy of Sciences and Humanities and an IEEE Fellow.

In this episode, Dr. Shlezinger and Dr. Eldar engage in a rich discussion on model-based deep learning—an approach that combines classical signal processing principles with modern data-driven techniques. This framework promotes efficiency not only through computational improvements, but by designing learning algorithms that naturally align with physical models and mathematical structures. They explore the key principles behind this methodology, its practical advantages, and its growing impact across a range of signal processing 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.