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Computational Complexity is concerned with the resources that are required for algorithms to detect properties of combinatorial objects and structures. It has often proven true that the best way to argue about these combinatorial objects is by establishing a connection (perhaps approximate) to a more well-behaved algebraic setting. Indeed, many of the deepest and most powerful results in Computational Complexity rely on algebraic proof techniques. The algebraic theme continues to play a central role in some of the most exciting recent progress in computational complexity in areas like circuit complexity, polynomial identity testing, pseudorandomness and derandomization, or error correcting codes. Beside algebraic methods, analytic methods like Fourier analysis have been used for quite some time in theoretical computer science. These methods have been used recently in some breakthrough results in complexity theory in areas like hardness of approximation, quantum computation, and scaling algorithms. A new theme that has gained importance in the last years is the area of meta-complexity, that is, the complexity of computational problems that are themselves problems about the complexity of computation. Meta-complexity provides a link between a variety of important areas like circuit complexity, proof complexity, cryptography, and learning theory. These new directions were in the focus of the Dagstuhl Seminar and reflect the developments in the field since the previous Dagstuhl Seminar 22371. Taking the recent exciting developments outlined above into account, we also included analytic methods this time. This Dagstuhl Seminar aimed to capitalize on recent progress and brought together researchers who are using a diverse array of algebraic and analytic methods in a variety of settings. Researchers in these areas are relying on ever more sophisticated and specialized mathematics and we hope that this seminar has contributed in educating a diverse community about the latest new techniques.