NEWS

DDNET (12PD/2020)

DDNET research grant moved to LOS Research Center.

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ABOUT

Research Center for Logic, Optimization and Security (LOS), Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest.

We promote scientific research and development in the Logic, Optimization and Security fields with pragmatic applications in the economic and industrial sectors.

Contact: los@fmi.unibuc.ro.

PEOPLE

Faculty

PROJECTS

DDNET (12PD/2020)

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.

Graphomaly (287PED/2020)

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 (143PD/2020)

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.

SEMINARS

Scientfic seminar organized by LOS members

LOGIC SEMINAR

The logic seminar features talks on mathematical logic, philosophical logic and logical aspects of computer science.

SECURITY SEMINAR

The Cyber-security seminar brings together academic and industry folk to discuss hot topics in the field ranging from operating systems, static and dynamic analysis of executables to fraud detection and security centric machine learning techniques.

PUBLICATIONS