LOS SEMINAR
The working seminar of the LOS research center.
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
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.
The project PROFET aims at developing and implementing a better solution compared to the existing ones regarding the precise positioning of a satellite onto its orbit. The efficiency of our approach comes from the low frequency of interaction between the space object and the GNSS (Global Navigation Satellite System) network. To achieve this, the definition of mission requirements, altogether with data retrieval from the orbital satellite is needed. The successful design of the estimation algorithms will be followed by the definition of the hardware requirements and the design of an electronic module, the estimation software being implemented at the firmware level, afterwards.
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.