Profile
I am a Reader in Theoretical Physics at the Centre for Theoretical Physics, Department of Physics and Astronomy. My work has focussed on various aspects of supersymmetric/superconformal field theories and their relation to String/M-theory.
I hold an undergraduate diploma from the University of Patras (Greece), an MSc from Durham University and a PhD from Queen Mary University of London. I have previously held postdoctoral positions at the Tata Institute of Fundamental Research (India), King's College London and Rutgers University (USA).
I am a Fellow of the Institute of Physics.
Teaching
I currently teach the undergraduate course "Quantum Mechanics and Symmetry" over the second semester of the 2024-2025 academic year.
I am a Fellow of Advance HE.
Publications
(A complete and up to date list of publications with an accurate citation count can also be found at this link)
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Papageorgakis C, Niarchos V (2024). Learning S-matrix phases with neural operators.
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Niarchos V, Papageorgakis C, Richmond P et al. (2023). Bootstrability in line-defect CFTs with improved truncation methods.
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Kántor G, Niarchos V, Papageorgakis C et al. (2023). 6D (2,0) bootstrap with the soft-actor-critic algorithm.
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Andriolo E, Niarchos V, Papageorgakis C et al. (2023). Covariantly Constant Anomalies on Conformal Manifolds.
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Andriolo E, Lambert N, Orchard T et al. . A path integral for the chiral-form partition function.
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Kántor G, Niarchos V, Papageorgakis C (2022). Conformal bootstrap with reinforcement learning.
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Kántor G, Niarchos V, Papageorgakis C (2022). Solving Conformal Field Theories with Artificial Intelligence.
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Agarwal P, Andriolo E, Kántor G et al. (2021). Macdonald indices for four-dimensional N=3 theories.
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Niarchos V, Papageorgakis C, Pini A et al. (2021). (Mis-)matching type-B anomalies on the Higgs branch.
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Melnikov IV, Papageorgakis C, Royston AB (2020). Accelerating solitons.
View Profile Publication Page Supervision
PhD students:
I am also interested in taking on new students for the following project:
Conformal Field Theories are mathematical descriptions of natural phenomena that look the same at all length scales. They find fundamental applications over a large spectrum of topics ranging from condensed-matter physics, particle physics, string theory and quantum gravity. They are, however, incredibly hard to solve with the exception of a handful of examples.
This project aims to exploit the tremendous recent progress in Artificial Intelligence to solve arbitrary conformal field theories. This will be achieved by utilising machine-learning techniques similar to those used by Google’s DeepMind Technologies when building the AlphaGo programme, which spectacularly beat professional Go champions.
Requirements:
A very good grasp of graduate Quantum Field Theory and supersymmetry, as well as some basic conformal field theory and string theory. Coding experience is desirable.
