BrainMap

Mr. Tudor Corneliu Ionescu

Dr.

Associate Professor - UNIVERSITATEA NAȚIONALĂ DE ȘTIINȚĂ ȘI TEHNOLOGIE POLITEHNICA BUCUREȘTI

Other affiliations

Researcher - INSTITUTUL DE STATISTICA MATEMATICA SI MATEMATICA APLICATA AL ACADEMIEI ROMANE''GHEORGHE MIHOC - CAIUS IACOB' (Romania)

Researcher | Teaching staff | Scientific reviewer

Web of Science ResearcherID: AAG-9196-2020

Curriculum Vitae (01/09/2022)

Expertise & keywords

Learning to optimize: from mathematical foundations to applications in modelling and control Call name: P 4 - Proiecte de cercetare exploratorie - PCE-2021
PN-III-P4-PCE-2021-0720 2022 - 2024

Role in this project:

Coordinating institution: INSTITUTUL DE STATISTICA MATEMATICA SI MATEMATICA APLICATA AL ACADEMIEI ROMANE''GHEORGHE MIHOC - CAIUS IACOB'

Project partners: INSTITUTUL DE STATISTICA MATEMATICA SI MATEMATICA APLICATA AL ACADEMIEI ROMANE''GHEORGHE MIHOC - CAIUS IACOB' (RO)

Affiliation:

Project website: https://sites.google.com/view/l2o-moc/home

Abstract:

Digital technologies are transforming all sectors of our economy and the next generation of smart control systems (SCS) are expected to learn models from data and take optimal decisions in real-time, leading to increased performance, safety, energy efficiency, and ultimately value creation. Since, SCS produce large amounts of data, machine learning (ML) aims at extracting information from them. The key step in any ML technique is training, where an optimization problem is solved to tune the parameters of the ML model. In this project we reverse this paradigm, i.e. we use ML for devising efficient optimization algorithms. Our key observation is that the modelling and control approaches for SCS yield optimization problems that are extremely challenging due to their large dimension, stochasticity, nonconvexity, etc. Optimization algorithms addressing such problems usually involve many parameters that need to be hand-tuned after time-consuming experimentation and are prone to ill-conditioning and slow convergence. To address these challenges, L2O-MOC will develop learning-based techniques for devising efficient tuning-free optimization algorithms. A novel universal framework will be developed, which will serve as a solid theoretical ground for the design of new learning paradigms to train optimization methods with mathematical guarantees for convergence. Modelling and control problems for SCS, e.g. those arising in power networks, will provide the datasets for the training.

Efficient Learning and Optimization Tools for Hyperspectral Imaging Systems Call name: EEA Grants - Proiecte Colaborative de Cercetare
RO-NO-2019-0184 2020 - 2023

Role in this project:

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA NAŢIONALĂ DE ŞTIINŢĂ ŞI TEHNOLOGIE POLITEHNICA BUCUREŞTI (RO); Norwegian University of Science and Technology (NO); UNIVERSITATEA BUCURESTI (RO); SPITALUL CLINIC COLTEA (RO)

Affiliation:

Project website: https://elohyp.wordpress.com

Abstract:

With the increasing amounts of data around us, many modern technologies designed for hyperspectral image processing, computer vision and medical imagining are now routinely using Artificial Intelligence (AI) and Big Data techniques. Hyperspectral imaging collects and processes information from the electromagnetic spectrum using a subset of targeted wavelengths at chosen location that span beyond the usual RGB spectrum. Hyperspectral data are becoming a valuable tool for monitoring the Earth’s surface or human body and are used in a wide array of applications: agriculture, health, environment, mineralogy, surveillance, physics, astronomy and chemical imaging. In the last decades, a large number of methods were proposed to deal with image processing problems. However, most these methods have been designed for application to color and/or grayscale images; therefore, they have limited success when applied to hyperspectral images. This is partially owing to large hyperspectral datasets being difficult to collect, process and analyze, and also to the heavy computational load associated with images captured using many spectral bands.

ELO-Hyp will find practical answers to these scientific challenges. We will push the frontiers of AI and Big Data and take important steps towards making the hyperspectral technologies of tomorrow possible, with direct impact in various parts of environment, health and industry sectors. To address these difficulties, we propose novel learning and optimization techniques for hyperspectral imaging systems. Our first novelty consists in defining appropriate models (loss functions), which include additional data representations, relational information about data (e.g. taking into account not only spectral information, but also spatial information) or ways of removing the influence of noise from predictor performance, boosting performance even when dealing with small datasets. However, high-quality models based on these new features would require hard Big Data (possibly nonconvex) optimization formulations, which further need fast and scalable algorithms with proper convergence guarantees. Therefore, our second novelty consists of developing fast optimization algorithms (e.g. higher order, projection or stochastic splitting methods) having low complexity per iteration (e.g. by combining with coordinate descent framework) and scalability (e.g. using parallel architectures such as GPUs, FPGAs). In conclusion, we aim to create, analyze and implement efficient learning and optimization algorithms for hyperspectral imaging models with applications to ocean monitoring and medical imaging. We will implement the algorithms, benchmark them using real datasets, ensure the algorithms’ interoperability, and produce free software.

Reliable motion planning for nonlinear systems under uncertainties Call name: P 1 - SP 1.1 - Proiecte de cercetare pentru stimularea tinerelor echipe independente
PN-III-P1-1.1-TE-2019-1614 2020 - 2022

Role in this project:

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: http://replan.upb.ro/

Abstract:

The project proposes various procedures for the reliable computation of trajectories in a multi-agent framework (i.e., offline trajectory generation, followed by trajectory tracking and reconfiguration) in the presence of realistic disturbances and model uncertainties.

The main idea is to exploit theoretical tools (flatness, NURBS functions, set-theoretic elements) in order to obtain a collection of trajectories which respect the internal dynamics and the operational constraints (collision avoidance, coverage restrictions, target tracking, etc.) of the agents while in the same time minimizing the given costs (e.g., time to destination, energy consumption, path length, etc.).

Particular emphasis will be put on the continuous validation of constraints and analytic characterization of costs, thus avoiding the pitfalls of discretization procedures and the necessity of over-conservative assumptions.

The trajectories are either pre-computed offline such that they are robust to disturbance and incertitude or are parameterized after the parameter(s) affecting the model. In both cases, the model limitations are taken into account explicitly in the design procedure, leading to a constrained optimization problem with non-convex constraints and non-linear cost. Heuristic methods will look for near-optimal solutions when standard approaches prove exhaustive.

Set invariance notions will certify the performance and stability of the closed-loop dynamics. Order-reduction methods will provide (where needed) simplified flat-derived formulations.

A reconfiguration mechanism (part of an overall fault tolerant control scheme) will switch between trajectories such that disturbances and model variations (i.e., faults) are accommodated.

The theoretical results will be validated in simulation (using ROS/Gazebo) and experimental benchmarks.

Scale-Free modeling and optimization techniques for control of complex networks Call name: P 4 - Proiecte de Cercetare Exploratorie
PN-III-P4-ID-PCE-2016-0731 2017 - 2019

Role in this project:

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI (RO)

Project website: http://acse.pub.ro/person/ion-necoara/#project

Abstract:

Scale-Free Modeling and Optimization Techniques for Control of Complex Networks (ScaleFreeNet) program will develop a theoretical modeling- and optimization-based framework to build on scale-free algorithms tailored for distributed model predictive control (MPC) of complex networks. This will set the foundations for a new optimal control theory dealing with complex physical networks with arbitrary size, which represent the next frontier in systems and control. Applications to a broad range of engineering, urban, social and health problems are expected. ScaleFreeNet has been designed from the observation that networks have become far more complex than the numerical tools available for managing them. Thus, ScaleFreeNet will make significant advances in the state of the art of decision-making for complex network systems by addressing several central points on how the current approach to modeling, optimization and control of networks must change in order to adapt to the large-scale challenges: (i) Scale-free modeling algorithms for complex networks using specific features (e.g. clustering of nodes, dynamical interactions between subsystems) and the new concept of aggregation; (ii) Scale-free optimization algorithms tailored for control of large networks using methods featuring nearly dimension-independent convergence (e.g. first-order methods) and techniques for obtaining near-linear cost per iteration (e.g. coordinate descent). Optimization provides a common paradigm for identification of optimal decisions; (iii) Scalable distributed MPC schemes for networks using scale-free modeling/optimization algorithms. Our flexible controllers will enable the deployments of advanced optimal decisions for the whole network in the large dimension context; (iv) Benchmark and software packages. We will test our algorithms on demand response problem in power distribution networks, while our software will facilitate their use by practitioners.

Efficient Approximation of Complex Systems with Delays Call name:
16/25.09.2017 2017 - 2019

Role in this project: Project coordinator

Coordinating institution: UNIVERSITATEA POLITEHNICA DIN BUCURESTI

Project partners: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Project website:

Abstract:

Timedomain Loewner framework reduction for complex physical systems with delays Call name:
TD1307 - 37646 2017 - 2017

Role in this project: Project coordinator

Coordinating institution: University of Groningen

Project partners: University of Groningen (); UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Affiliation: UNIVERSITATEA POLITEHNICA DIN BUCURESTI ()

Project website:

Abstract:

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects