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Pokrovsky boulevard, 11, room S938, Moscow, Russia, 109028
Phone: +7 (495) 772-95-90*27319
The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects.
Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.
Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.
Vasilii A. Gromov, Yury N. Beschastnov, Korney K. Tomashchuk.
PeerJ Computer Science. 2023. Vol. 9. No. .
Makhalova T., Kuznetsov S., Napoli A.
Data Mining and Knowledge Discovery. 2022. P. 108-145.
Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.
Plos One. 2022. Vol. 17. No. 10.
Zhirayr Hayrapetyan, Nascimento S., Trevor F. et al.
In bk.: Information Systems and Technologies: WorldCIST 2022, Volume 2. Iss. 469. Springer, 2022. P. 141-147.
Dudyrev F., Neznanov A., Anisimova K.
In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.
Egurnov D., Точилкин Д. С., Ignatov D. I.
In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.
Egurnov D., Ignatov D. I.
Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.
Kudriavtseva P., Kashkinov M., Kertész-Farkas A.
Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.
Kanovich M., Kuznetsov S., Scedrov A.
Information and Computation. 2022. Vol. 287.
Series of Lectures on Collaborative Systems
The lectures were delivered by Max Kanovich, Professor of Computer Science, University College London, and Andre Scedrov, Chair of the Mathematics Department, University of Pennsylvania.
The following topics were covered during the lectures:
- Analysis of Network Security Protocols
- Kerberos Authentication Protocol
- Bounded Adversary
- Timed Collaborative Systems
- Resource reasoning in timed collaborative systems and program analysis
—Basics, where the fundamental ideas of the resource logics (linear logic, separation logic) are presented in a semi-formal style.
— Foundations, where, for general resource models, on one hand, and for concrete heap-like models of practical interest, on the other hand, we get into the formalities, including the semantics of the assertion language and axioms and inference rules. Surprisingly, as for the assertion language of separation logic, even purely propositional separation logic turns out to be undecidable. What is more, whatever concrete heap-like model Hwe take, it is undecidable whether a purely propositional formula A is valid in this model H.
— Horn linear logic (a.k.a. multiset rewriting systems), where we research into the computational complexity of planning within real-time collaborative systems. A particular focus is on planning under uncertainty caused by actions with non-deterministic effects and/or quantitatively non-deterministic and continuous delayed effects.
— `New' logical principles for program analysis. Abduction, the problem of discovering hypotheses that support a conclusion, has mainly been studied in the context of philosophical logic and AI. Recently, the abduction principle — given A and B, find a non-trivial X such that A * X entails B, is one of the powerful practical tools for iterated deduction and hypothesis formation to `dig information out of bare code'. We study the complexity of abduction for a relevant fragment of separation logic over `symbolic heaps' which include a basic `points-to' predicate, and an inductive predicate for describing linked-list segments.