Research

• Sustainability (Sustain): My research considers two types of sustainability: Sustainability OF the network, where the aim is to reduce the network's carbon footprint without sacrificing performance, and sustainability FOR the network, which relates to making the designed solutions adaptable to changes that may appear without human interaction. 

• Trustworthiness & Transparency (T & T): Due to my math background, I take very seriously the behavior of the solutions I design, and aim at being as much rigorous as possible. I opt to design solutions that are able to identify failures, i.e. trustworthy, as well as explainable and interpretable, i.e. transparent. 

• Reliability & Robustness (R & R): Following the same logic as for "T & T", I opt to design solutions that are provably robust, accurate and reliable. In our context, robustness of a solution means that it can withstand unpredictable changes or adversarial attacks, and at the same time operate as intended. Reliability in my research is most of the times translated in providing omnipresent and continuous connectivity, which can be measured in the probability of meeting (very stringent) communication deadlines.

• Zero-Delay Connectivity (Zero-Delay): My research aims to produce solutions that are as lightweight as possible and to eliminate latency bottlenecks, and ultimately to enable real-time, mission-critical applications across the global network.

• Mobile Edge Computing (MEC): Since my PhD, a core environment for my research has been the edge / cloud. Here, the goal is to push intelligence and massive computational power directly to the network's edge, bringing the algorithmic decision-making as close to the end-user as possible.

• Vehicle-to-anything & Autonomous Mobility (V2X): In my recent works focusing on Vehicle-to-Network (V2N) scenarios, I apply my frameworks to highly dynamic vehicular environments. The challenge here is ensuring that moving vehicles maintain seamless, coordinated intelligence with the infrastructure despite constant mobility.

• Integrated Sensing and Communications (ISAC): Through my current MSCA PF project (RIXISAC), I am exploring environments where wireless communication and radar sensing are merged, and the possibility to use also Reconfigurable Intelligent Surfaces (RIS) in doing so. In this exploration I design frameworks and algorithms while collaborating with a team of talented people that handles the systems / implementation part within the Network Systems group at TU Delft. 

• Virtual & Adaptive Next-Gen Infrastructure (Next-Gen): In the context of large-scale EU projects like ORIGAMI and DAEMON, I focus on the transition from rigid, traditional hardware into flexible, software-defined 6G cloud architectures that can host AI-native operations.

• Content Caching (Caching): A foundational mechanism in my research where I design strategies to proactively store high-demand data at the edge. By mathematically predicting demand, or nudging it with the use of Recommender Systems (RecSys), I aim to drastically reduce delivery times and relieve core backhaul congestion.

• Recommender Systems (RecSys): Moving beyond traditional content suggestion, my work links user preferences directly to network efficiency. My work introduced in the literature a fundamental framework where caching and RecSys decisions were coordinated, and that was widely adopted from subsequent work on the topic. The main idea behind this interplay is that caching-aware RecSys can be used to nudge user requests toward edge-available content, optimizing overall network performance, albeit always providing a desired "Quality of Recommendations" to the users, based on their individual preferences.

• Computation Offloading & Scheduling (Offload & Sched): I mathematically model the dynamic shifting of heavy and heterogeneous processing tasks from mobile devices to (shared) edge servers. My goal is to optimally schedule these tasks so that strict execution deadlines are met while respecting energy consumption and accuracy requirements. 

• Radio Resource Management (RRM) & Channel Decoding (Radio & Decode): I formulate optimization problems to manage the wireless network from the high-level radio links down to the bare-metal signals. This ranges from intelligently pairing mobile users to access points to balance network loads, all the way down to the physical layer, where my research has contributed to highly efficient algorithmic channel decoding to accurately recover data from noisy wireless environments.

• Optimization & Applied Math (Opt & Math): As an applied mathematician, i enjoy utilizing traditional math tools ranging from continuous and (non-)convex optimization to complex combinatorial frameworks. Guided by the geometry of the feasible space of each optimization problem that formalized the studied scenario, I develop algorithmic techniques that approximate optimal solutions for (usually NP-hard) problems, provably trading computational complexity for performance guarantees when exact solutions cannot meet strict networking deadlines. When standard tools fall short, I design highly efficient heuristics, and I leverage queueing theory to analytically characterize and manage the delay introduced at the network edge.

• Online and Reinforcement Learning (Learning): By combining the predictive power of machine learning with the rigor of mathematical optimization, I design real-time algorithms that make optimal decisions under partial or strictly zero prior knowledge of system conditions. By studying the geometry of the feasible space to define regularization functions, i design asymptotically optimal solutions, scaling efficiently across both time horizon and problem dimensions. This makes learning a powerful engine for extremely dynamic environments, such as ISAC systems.

• Explainable AI & Machine Learning (XAI / AI / ML): I leverage recent advances in AI and ML to tackle problems that are computationally intractable with traditional optimization, learning optimal solutions and discovering hidden variable correlations. Although very accurate, these models are usually black boxes. By using State-of-the-Art Explainable AI (XAI) techniques, i enjoy revealing exactly how and why specific network decisions are taken, guaranteeing transparency and allowing me to reverse-engineer what parameters must shift to trigger alternative actions.

• Generative AI (GenAI): I am actively exploring the use of advanced foundation models to automate complex heuristic designs and network operations, translating modern AI capabilities into telecom resource management.

• Systems Prototyping & Testbeds (Prototypes): I actively collaborate with experimental systems teams that build physical prototypes to demonstrate static and dynamic scenarios of (RIS-assisted) ISAC systems, utilizing cutting-edge equipment from TU Delft and NEC Labs. We directly validate algorithmic performance by conducting Radio Frequency (RF) measurements with SDR dual-channel transceivers and Vector Network Analyzers, and integrating high-speed, low-energy MicroController Units. Hopefully, the insights from these experimental setups help industry partners, such as NEC Labs, design their next generation of Reconfigurable Intelligent Surfaces.

• Architectural Design & 6G Roadmapping (6G Architecture): Leveraging my involvement in EU consortiums, I abstract my mathematical findings to help define the high-level strategic blueprints, position papers, and standards for the intelligence stratum of future networks.

The full list of my publications can be found in the link.  

Selected publications:

1. L.E. Chatzieleftheriou,  J. Pérez-Valero, J. Martín-Pérez, P. Serrano, “Optimal Scaling and Offloading for Sustainable Provision of Reliable V2N Services in Dynamic and Static Scenarios”, IEEE Transactions on Service Management, Sept 2025. - Q1 in JCR       

2. L.E. Chatzieleftheriou, A. Destounis, GP. Paschos, I. Koutsopoulos. "Blind Optimal User Association in Small-Cell Networks". IEEE International Conference in Computer Communications (IEEE INFOCOM), 2021. -  CORE A*, top conference in computer networks

3. L.E. Chatzieleftheriou, M. Karaliopoulos, I. Koutsopoulos. "Jointly Optimizing Content Caching and Recommendations in Small Cell Networks". IEEE Transactions on Mobile Computing, vol. 18, no. 1, pp. 125-138, 2019. - Q1 in JCR

The full list of the projects that generously funded my research can be found in the link, together with the publications that were produced within each of them and other details. 

Selected projects:

1. 9/2025-8/2027, RIXISAC: Reflective intelligent surface-assisted, Intelligent, and eXplainable Integrated Sensing and Communication systems, related with Award A8. Personal grant funded by the European Commission under the call HORIZON-MSCA-2023-PF-01, no-101155506, Total budget: 187,624.32€. 

Role: Leader and writer of project proposal, researcher. 

    Related with Grant A8, MSCA Postdoctoral/Individual Fellowship.

Related with the peer-reviewed C14 conference paper, and the manuscript R1 that is under review.

Theses project available, see below! 

2. 1/2024-08/2025, NI-OL-XAI: Enabling Network Intelligence through Online Learning and eXplainable Artificial Intelligence, Related with award A7, Juan de la Cierva 2022. Personal grant based on merits and small research proposal, funded by Ministerio de Ciencia Y Innovación (National and EU funds), no-JDC2022-050266-I, 67.400€. 

Role: Leader and writer of project proposal, researcher. 

Related with the peer-reviewed publications J6 (journal) and C10, C11, C12, C13 (conferences).

I particularly value the applicability of my research in real systems and the transfer of ideas and knowledge to society/industry.

T4.  9/2025-8/2027, RIXISAC includes a secondment in NEC Research Labs Europe. Role: researcher. The research done within RIXISAC (project P10) will hopefully help NEC Labs design their next RIS generation.

T3.  6-12/2022, Adaptive resource scheduling policies for 6G (funded by Nokia Bell Labs - Germany). Role: Researcher (XAI for Radio Resources Scheduling in vRAN systems). Transferable project, with potential incorporation to Nokia’s internal products, to make them more “explainable”.

T2. 2/2020 & 6-8/2019, Involved in internal research project while being Visiting Researcher at HUAWEI Technologies, Paris Research Center, France. Role: Researcher (Studied the association of users to Access Points (APs) and the resource allocation for 5G networks. Formulated optimization problem and used both mathematical and machine learning tools to solve it. Designed solution and evaluated it over real traffic traces provided by HUAWEI). Transferable project, with potential incorporation to HUAWEI’s internal systems to make them more efficient.

T1.  7-8/2012, Involved in internal project while interning in EIAPEVO, Italy. Role: Research assistant. Conducted cluster analysis and quantitative/qualitative investigation on zoonoses-related data. Used R and SPSS.

If you are a motivated student who wants to design the theoretical intelligence behind next-generation networks and see it validated in real testbeds, feel free to reach out to discuss open thesis topics! 

See available MSc and BSc thesis topics in the link!  😀