Autumn school co-organized by LOS
Institute for Logic and Data Science, Faculty of Mathematics
and Computer Science, University of Bucharest and LOS
organize the ILDS-FMI Coq and Lean Autumn School 2023 in the period September 20-23, 2023.
Institute for Logic and Data Science, Faculty of Mathematics
and Computer Science, University of Bucharest and LOS
organize the ILDS-FMI Coq and Lean Autumn School 2023 in the period September 20-23, 2023.
Horațiu Cheval (invited in the Applied Proof Theory Special Session), Laurențiu Leuștean and Andrei Sipoș have all presented their recent research at Logic Colloquium 2023, the annual European meeting of the Association for Symbolic Logic.
Andrei Sipoş published (in collaboration with Liviu Păunescu)
the paper
A proof-theoretic metatheorem for tracial von Neumann algebras
in Mathematical Logic Quarterly.
The Research Center for Logic, Optimization and Security (LOS) was founded in October 2020 by
Laurenţiu Leuştean (head), Paul Irofti and Andrei Pătraşcu.
Our main objective is to stimulate interdisciplinary research in the fields of logic,
optimization and security. We are interested both in fundamental research as well as in
industrial applications. We focus on proof mining and applications to optimization, ergodic theory
and nonlinear analysis, convex optimization for machine learning, signal processing and
matrix factorization (dictionary learning), security, anomaly detection and anti-money laundry.
Contact
Hall 317, Faculty of Mathematics and Computer Science,
Academiei 14, 010014 Bucharest, Romania
Email: los@fmi.unibuc.ro
NetAlert aims to create a hardware-software sensor solution for detecting anomalies in computer networks
based on the monitoring and analysis of data packets.
The network-mounted sensor will provide real-time alerts on abnormal traffic behaviors
using two complementary approaches:
(i) static analysis based on rules and behavioral patterns;
(ii) machine learning (ML) analysis without prior expert knowledge.
The main goal of this project, called DDNET, is to adapt and propose
new dictionary learning methods for solving untractable
fault detection and isolation problems found in distribution networks.
Given a large dataset of sensor measurements from the distribution network,
the dictionary learning algorithms should be able to produce the subset
of network nodes where faults exist.
The proposed project, called Graphomaly, aims to create a
Python software package for anomaly detection in graphs
that model financial transactions,
with the purpose of discovering fraudulent behavior like money laundering,
illegal networks, tax evasion, scams, etc.
Such a toolbox is necessary in banks, where fraud detection departments
still use mostly human experts.
StOpAnomaly aims to create, analyze and implement numerical optimization algorithms for large-scale optimization focusing on robust anomaly detection models based on decomposition and one-class classification. The research will be directed towards development of a toolbox containing scalable stochastic algorithms that can be used to detect several classes of anomalies in noisy large datasets.
Scientific seminars organized by LOS members
The working seminar of the LOS research center.
The seminar features talks on mathematical logic, philosophical logic and logical aspects of computer science.
The seminar presents recent results on proof mining.
