In 2017, he was awarded the Leverhulme Prize for having "solved very hard problems and opened new directions in areas of great impact in applied analysis [...] Notably, by introducing the Solvability Complexity Index he has made a major contribution to the advancement of Smale’s programme on the foundation of computational mathematics".[15][16] In 2018, he was awarded the IMA Prize in Mathematics and its Applications[17] for having "made a transformative impact on the mathematical sciences and their applications [...] in particular, for his development of the Solvability Complexity Index and its corresponding classification hierarchy".[18] In 2019, he was awarded the Whitehead Prize of the London Mathematical Society for having "contributed fundamentally to the mathematics of data, sampling theory, computational harmonic analysis and compressed sensing" and "especially his development of the Solvability Complexity Index and its corresponding classification hierarchy
".[19]
A. Bastounis, A. C. Hansen, D. Higham, I. Tyukin and V. Vlacic: "Deep Learning: What Could Go Wrong?", SIAM News (October 2021).
V. Antun, N. Gottschling, A. C. Hansen and B. Adcock, "Deep Learning in Scientific Computing: Understanding the Instability Mystery", SIAM News (March 2021).
A. Bastounis, B. Adcock and A. C. Hansen, "From Global to Local: Getting More from Compressed Sensing", SIAM News (October 2017).
Books
Adcock, Ben; Hansen, Anders C. (2021). Compressive imaging : structure, sampling, learning. Cambridge, United Kingdom. ISBN978-1-108-37744-7. OCLC1260468467.{{cite book}}: CS1 maint: location missing publisher (link)
^Ben-Artzi, J.; Colbrook, M.; Hansen, A. C.; Nevanlinna, O.; Seidel, M. C. (2020). "Computing Spectra -- On the Solvability Complexity Index Hierarchy and Towers of Algorithms". arXiv:1508.03280v5 [cs.CC].
^Bastounis, A.; Hansen, A. C.; Vlacic, V. (2021). "The extended Smale's 9th problem -- On computational barriers and paradoxes in estimation, regularisation, computer-assisted proofs and learning". arXiv:2110.15734v1 [math.OC].