New arXiv preprint
Laurențiu Leuștean and Horațiu Cheval published the preprint
Quadratic rates of asymptotic regularity for the Tikhonov-Mann iteration
on arXiv.
PEOPLE
Associated researchers
Students
Laurențiu Leuștean and Horațiu Cheval published the preprint
Quadratic rates of asymptotic regularity for the Tikhonov-Mann iteration
on arXiv.
Laurențiu Leuștean will give on Wednesday, June 23, 2021 at 14:00 the talk
Proof mining and applications in nonlinear analysis and convex optimization. Part II
in the LOS seminar.
Laurențiu Leuștean will give on Tuesday, June 14, 2021 at 14:00 the talk
Proof mining and applications in nonlinear analysis and convex optimization
in the LOS seminar.
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
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 logic seminar features talks on mathematical logic, philosophical logic and logical aspects of computer science.
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.