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This plenary presentation, delivered by Emmanuel Candès at ICASSP 2026, addresses the bidirectional synergy between statistical theory and artificial intelligence to construct more reliable, safe, and efficient AI systems. The primary goal is to examine how rigorous statistical frameworks—particularly conformal prediction and controlled inference—can be integrated into machine learning workflows to ensure high-quality, trustworthy outputs when automated models substitute for or guide real-world data collection.

This talk revisits classical generative modeling approaches, with an emphasis on mixture models developed prior to the rise of deep generative learning, and highlights their continued relevance for the physical layer of wireless communications. Owing to their capacity for analytical inference, mixture models form a natural connection between machine learning and classical signal processing, where covariance information is central to many physical-layer tasks.

Author Workshop will offer the following:- An overview of the submission and the peer review process- Preparing your work with research integrity in mind- Proper etiquette and ethics for authors- Proper preparation of a manuscript- Best practices to avoid bibliometric and citation manipulation

Atanas Gotchev, ICIP2026 General Chair, overview of ICIP 2026Heikki Huttunen, Visy Oy, presenting ICIP2026 Best Work in Progress Award, sponsored by VisyPetros T. Boufounos, Mitsubishi Electric Research Laboratories (MERL), presenting ICIP2026 Best Show & Tell Demo Award, sponsored by MERLFrederik Dufaux and Paulo Nunes, ICIP2026 Award Chairs, presenting ICIP2026 Best Industry AwardRay Liu, IEEE SPS Awards Board Chair, presenting the Pierre-Simon Laplace Early Career Technical Achievement Award to Katherine L. Bouman