Seminars
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INGI Lunchtime Seminars take place about twice a month and they address various topics related to computing sciences.
They are open to everyone, no registration is required : come and have a seat! Sandwiches and drinks are usually provided. Please fill in the form before day D-1 at 12:00 to reserve a sandwich
If you are not an ICTEAM member, join our mailing list to stay informed about the next seminars!
INGI seminars
Seminars to Come
[INGI] 2026-01-22 (13:00) : Distributed, Coordination-Free Programming: 10 Years of Progress Since Lasp
At Shannon, Maxwell a.105
Speaker:
Peter VAN ROY (UCL)
Abstract: Consensus is a critical building block for building fault-tolerant distributed systems. It is widely believed that without consensus, large distributed applications on the Internet could not exist. But recent advances show that consistent replication can be achieved without consensus by using convergent data structures such as CRDTs (conflict-free replicated data types). This is called coordination-free programming and it has become a credible alternative to consensus. The Lasp system is the first to compose CRDTs. It was published in 2015 in the ACM Symposium on Principles and Practice of Declarative Programming (PPDP) and the paper won the 10-year most influential paper award at PPDP 2025. Lasp’s coordination-free model has inspired a decade of progress in academia and industry. As the industry shifts toward multi-region deployments, Lasp’s core insight — that coordination can be the exception, not the rule — continues to shape how we build reliable, scalable systems today.
[INGI] 2026-02-05 (13:00) : Fair Tabular Data Generation: an Approach using Autoregressive Decision Trees
At Nyquist Maxwell a.164
Speaker:
Benoît Ronval (ICTEAM)
Abstract: In both research and industry, tabular data is among the most widely used data types. Represented using instances (rows) with features (columns), such data is easy for humans to interpret and readily usable by most machine learning algorithms.
Despite its large usage, acquiring new tabular data can be challenging. Data collection can require access to private sources or large-scale surveys, which may be costly and may suffer from low response rates. Moreover, real-world tabular datasets frequently exhibit bias, leading machine learning models to produce unfair classifications for certain subgroups, particularly with respect to sensitive attributes such as the nationality or the education level of a person.
In this seminar, I will present our new method TabFairGDT, which aims to generate data that can reduce fairness concerns in the predictions of machine learning models trained on this data. The approach leverages decision trees in an autoregressive generation framework, including a fairness optimization step. I will also discuss the advantages of decision trees for tabular data generation and present experimental results, including classification performance, fairness metrics, and data quality analyses.
[INGI] 2026-06-07 (13:00) : TBA
At shannon, Maxwell a.105
Speaker:
Peter VAN ROY (UCL)
Abstract: t'a
Previous Seminars
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[INGI] 2025-12-11 (13:00) : Combinatorial Transition Testing in Dynamically Adaptive Systems: Implementation and Test Oracle
At Shannon, Maxwell a.105
Speaker:
Pierre Martou (ICTEAM)
Abstract: Due to the large number of possible interactions and transitions among features in dynamically adaptive systems, testing such systems poses significant challenges. To verify that such systems behave correctly, combinatorial interaction testing (CIT) can create concise test suites covering all valid pairs of features of such systems. While CIT claims to find all errors caused by two features, it does not cover certain errors occurring only for specific transitions between features. To address this issue we study the technique of Combinatorial Transition Testing (CTT), which includes both generation and detection of what we call behavioural transition errors. From an initial generation algorithm that combines both interaction and transition coverage but lacks scalability, we propose an optimised version that enables CTT even for hundreds of features. From a valid test suite covering all transitions, we complete our testing approach with a test oracle that detects all behavioural transition errors without any prior knowledge of the system's behaviour. After a comprehensive analysis over a large number of feature models, we conclude that size of CTT-generated test suites and test effort needed to use our test oracle are linearly correlated to CIT-generated ones and that CTT grows logarithmically in the number of features.
[INGI] 2025-12-04 (13:00) : Empowering QUIC with Connection Delegation to Save Energy
At Shannon, Maxwell a.105
Speaker:
Vany Ingenzi (ICTEAM)
Abstract: The end-to-end principle is fundamental to the design of the Internet, requiring that hosts participating in a transport connection remain continuously capable of processing packets. However, this principle conflicts with the energy-saving needs of battery-powered wireless devices, which benefit from short and intermittent activity periods.
In this talk, we present a QUIC connection delegation mechanism that allows a client to transfer an active connection to a trusted third party by securely migrating its connection state. The third party then maintains the connection and handles data exchanges on behalf of the original client, allowing the client device to enter a power-saving mode. Our evaluation shows that QUIC connection delegation can reduce energy consumption over Wi-Fi by up to 55% when the smartphone can utilize its sleep mode, and by 25% when accounting for background traffic and an active display.
[INGI] 2025-11-27 (13:00) : Uncovering Malicious Persistence: Machine Learning-Based Detection of Windows Scheduled Tasks
At Shannon, Maxwell a.105
Speaker:
Khaled Rahal (ERM & ULB)
Abstract: Advanced Persistent Threats (APT) represent a serious security concern because they
carry out long-term and carefully planned attacks. While a lot of research has gone into finding
ways to detect these threats; one crucial area often gets less attention, namely the persistence
mechanisms that allow attackers to stay hidden and maintain access to systems over
time. In this work, we investigate scheduled tasks, a widely used persistence technique
in Windows environments, and analyze their role in APT operations. We conducted an
in-depth study of how attackers leverage scheduled tasks to maintain stealthy access and
execute malicious actions over time. We introduce Detecting APT Through Malicious
Scheduled Tasks (DAPTASK), an approach that leverages Sysmon log data, Word2Vecbased
feature representation, and Machine Learning (ML) classifiers to identify malicious
1scheduled tasks commonly used in APT persistence techniques. Our approach achieves
a high detection performance, with an F1-score of 95.19%. Furthermore, we provide a
labeled dataset, which can serve as a valuable resource for researchers developing APT
detection methods, the dataset and the code used are publicly available at https://gitlab.cylab.
be/cylab/daptask. Our approach enhances APT detection by addressing persistence
techniques, a critical yet often neglected attack vector.
[INGI] 2025-11-17 (13:00) : From Cloud to Edge: How to Make AI Inference Accurate, Fast and Resource-Aware ?
At Nyquist Maxwell a.164 room.
Speaker:
Patient NTUMBA WA NTUMBA (CNAM Paris)
Abstract: Edge computing is becoming a critical enabler for real-time Internet of Things (IoT) applications powered by artificial intelligence (AI). These applications often require accurate, low-latency and high-throughput inference while operating on resource-constrained edge servers. In this seminar, we present a holistic framework for optimising AI model provisioning at the edge. Our approach combines HProfiler, a data-driven model profiling tool that generates AI model variants tailored for accuracy-throughput trade-offs and specific edge resources, with AIForwarder, a reinforcement learning-based mechanism that dynamically manages model activation and request-to-model forwarding. By balancing accuracy, energy consumption, and request loss, our solution adapts to dynamic workloads and heterogeneous IoT demands.
Short Bio: Patient Ntumba is a postdoctoral researcher in the RoC team at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. He obtained his PhD from Sorbonne University, conducting his doctoral research at the INRIA Paris research center in the MiMove team. His research interests focus on networks, distributed systems, and optimization. His current work centers on in-network distributed learning and Edge AI inference.
Short Bio: Patient Ntumba is a postdoctoral researcher in the RoC team at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. He obtained his PhD from Sorbonne University, conducting his doctoral research at the INRIA Paris research center in the MiMove team. His research interests focus on networks, distributed systems, and optimization. His current work centers on in-network distributed learning and Edge AI inference.
[INGI] 2025-11-10 (11:00) : Double seminars by Hugo Rimlinger (LIP6) & Moritz Müller (université de Twente)
At Shannon room, Maxwell building
Section 1: GeoResolver: An Accurate, Scalable, and Explainable Geolocation Technique Using DNS Redirection
Speaker:
Hugo Rimlinger (LIP6)
Abstract: Obtaining an accurate, explainable and Internet scale IP geolocation dataset has been a longstanding goal of the research community. Despite decades of research on IP geolocation, no current technique can provide such a dataset. In particular, latency-based geolocation techniques do not scale, because, on one hand, we have thousands of available vantage points to perform measurements, but on the other hand, we have no way to select the right ones for each IP address. In this paper, we present GeoResolver, which is a serious step towards our goal, by using the idea that when multiple operators redirect two prefixes to the same servers, these prefixes should be close to each other. With this intuition, we define a methodology to measure and compare the redirection of prefixes to servers using ECS DNS measurements, and select the prefixes with the smallest redirection distance to a target prefix to issue the latency measurements to targets in that prefix. GeoResolver performs nearly as well as a brute force approach, geolocating 94% of the targets that could actually be geolocated at metro level, while using 4.3% of the probing budget compared to the state of the art. On the Internet scale CAIDA ITDK dataset, GeoResolver geolocates 16% of the IP addresses at metro level, 3.4 times more than the state of the art. In addition, GeoResolver is robust to public resolvers or hypergiants stopping supporting ECS.
Section 2: Monitoring highly distributed DNS deployments: challenges and recommendations for the root server system
Speaker:
Moritz Müller (Université de Twente)
Abstract: DNS name servers are crucial for the reachability of domain names. For this reason, name server operators rely on multiple name servers and often replicate and distribute each server across different locations across the world. Operators monitor the name servers to verify that they meet the expected performance requirements. Monitoring can be done from within the system, e.g. with metrics like CPU utilisation, and from the outside, mimicking the experience of the clients. In this talk, we focus on the latter. We take the root server system as a use case and highlight the challenges operators and researchers face when monitoring highly distributed DNS deployments from the outside. We also present recommendations on building a monitoring system that is more reliable and that captures only the relevant metrics.
[INGI] 2025-10-30 (13:00) : Hybrid artificial intelligence for solving computationally hard problems
At Shannon, Maxwell a.105
Speaker:
Quentin Cappart (ICTEAM)
Abstract: Combinatorial optimization provides methods to make the best possible decisions in complex scenarios, with practical applications in areas such as transportation, logistics, and healthcare. Traditionally, solving methods for combinatorial problems (such as integer programming, constraint programming, or local search) have focused on solving isolated problem instances, often overlooking the fact that these instances frequently originate from related data distributions. In recent years, there has been a growing interest in leveraging machine learning, particularly neural networks, to enhance combinatorial solvers by utilizing historical data. Despite this interest, it remains unclear how to effectively integrate learning into such engines to boost overall performance. In this presentation, I will share my journey in tackling this challenge, from my initial attempts to my current research directions. I will offer personal advice for researchers interested in exploring this fascinating field, highlighting the potential and opportunities for designing efficient hybrid artificial intelligence for solving computationally hard problems.
[INGI] 2025-10-16 (13:00) : Malware Detection with Machine Learning: Challenges and Perspectives
At Shannon, Maxwell a.105
Speaker:
Serena Lucca (ICTEAM)
,
and
Samy Bettaied (ICTEAM)
Abstract: This presentation explores some challenges and perspectives of applying machine learning to malware detection, drawing insights from two complementary studies. The first investigates the surprising effectiveness of simple models—such as One-Rule and AdaBoost—on state-of-the-art malware detection datasets, revealing that a small subset of dominant features often drives classification performance, leading to minimal differences between simple and deep learning approaches. The second study provides a systematic comparison of tabular and graph-based feature representations under unified conditions, evaluating their trade-offs in computational cost, detection accuracy, and robustness to adversarial attacks. Together, these works question the common assumption that complex models or sophisticated feature types always yield superior results, and instead highlight the importance of understanding feature dominance, dataset biases, and practical constraints in real-world malware detection.
[INGI] 2025-10-15 (10:00) : Autonomous Systems under AReST: Advanced Revelation of Segment Routing Tunnels
At Shannon, Maxwell a.105
Speaker:
Florian Dekinder (ULiège)
Abstract: Segment Routing (Sr), an advanced source routing mechanism, is a promising technology with a wide range of applications that has already gained traction from hardware vendors, network operators, and researchers alike. However, despite the abundance of activity surrounding Sr, little is known about how to gauge Sr deployment and its usage by operators.
This paper introduces a methodology, called AReST (Advanced Revelation of Segment Routing Tunnels), for revealing the presence of Sr with Mpls as forwarding plane (Sr-Mpls). AReST relies on standard measurement tools, like traceroute and fingerprinting, and post-processes the collected data for highlighting evidence of Sr-Mpls. Our results show that AReST is efficient in revealing the presence of Sr-Mpls in various autonomous systems, obtaining a perfect precision on our ground truth directly obtained from an operator. We also make a preliminary characterization of the Sr-Mpls deployment and show that it is commonly deployed within Content, Transit, and Tier-1 providers and, occasionally, in interworking with classic Mpls. The data collected, as well as our source code, is available to the research community.
[INGI] 2025-10-09 (13:00) : Sloth: A Kernel-Bypass Scheduler Maximizing Energy Efficiency under Latency Constraints
At Shannon room, Maxwell building
Speaker:
Clément Delzotti (ICTEAM/UCLouvain)
Abstract: The continuously increasing network speeds make packet processing on CPUs increasingly challenging. At a line rate of 100 Gbps, today’s CPUs struggle to execute complex network functions. This trend calls for offloading packet processing to other devices. This work explores how Graphical Processing Units and programmable Network Interface Cards can be used instead of a general-purpose CPU for packet processing. GPUs have been proposed to accelerate network processing thanks to their massively parallel architectures. Recent NICs provide tighter integration with GPUs, with the ability to write received packets directly to GPU memory. Recent SmartNICs, or DPUs, can also receive packets to their own memory and process them with embedded ARM or RISC processors. Such improvements allow bypassing the CPU entirely by processing packets only on the GPU cores or on the SmartNIC’s cores.
In this work, we review various models for packet (co-)processing applied to Network Function Virtualization, including CPU+GPU hybrid, SmartNIC-only, and GPU-only approaches. We introduce a novel communication model between
CPU cores and the GPU, enabling scalable CPU-GPU hybrid utilization while minimizing CPU resources needed. We show that for a computation-heavy workload, current CPU-only implementations manage to handle up to 45 % of the 100 Gbps line rate. In contrast, GPU implementations can saturate it. We also show that recent SmartNICs are getting powerful cores that can replace the main CPU for some traditional packet processing, alleviating the load on the host, which can now entirely be dedicated to running applications. We finally propose a novel energy-efficiency aspect, showing that GPUs and DPUs outperform traditional CPUs by 2 to 3× in terms of Joules/packet.
[INGI] 2025-10-09 (13:00) : Sloth: A Kernel-Bypass Scheduler Maximizing Energy Efficiency under Latency Constraints
At Shannon, Maxwell a.105
Speaker:
Clément Delzotti (ICTEAM/UCLouvain)
Abstract: In recent years, multi-hundred-gigabit networking
applications such as Virtual Network Function (VNF) and Key
Value Store (KVS) implementations have relied on kernel-bypass
and polling to achieve maximum throughput. However, this
performance improvement comes at the expense of high CPU
usage and power consumption.
This paper first analyses the trade-off between the power
consumption, the latency and the throughput of VNF applica-
tions. We then present Sloth, an energy-aware scheduler that
adapts the number of cores used by an application and their
frequency. Sloth uses the information gathered in a training phase
to maximize the energy reduction in real time while maintaining
a user-provided service-level objective. Sloth manages to reduce
CPU power consumption by up to 50% compared to the classical
DPDK polling approach with only a 30 μs latency increase. Sloth
also saves up to milliseconds of latency compared to state-of-the-
art solutions at equivalent power consumption
[INGI] 2025-09-25 (13:00) : Learning from logical constraints with Lower- and Upper bound arithmetic circuits
At Shannon, Maxwell a.105
Speaker:
Alexandre Dubray (ICTEAM)
Abstract: In this work focuses on the field of 𝐧𝐞𝐮𝐫𝐨-𝐬𝐲𝐦𝐛𝐨𝐥𝐢𝐜𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (NeSy), which aims to bridge the gap between deep learning methods (neural) and the logical knowledge available in certain domains (symbolic). It has been accepted to the Main track at IJCAI 2025, one of the world’s premier conferences on Artificial Intelligence.
Standard deep learning struggles with logic-based reasoning. One solution is to encode known constraints as 𝐚𝐫𝐢𝐭𝐡𝐦𝐞𝐭𝐢𝐜 𝐜𝐢𝐫𝐜𝐮𝐢𝐭𝐬 to enable a gradient-based guidance of the parameters being learned. But, for logical knowledge that is too complex to be fully encoded, existing methods use a single lower-bound approximate circuit, often compromising the quality of the computed gradients.
The authors introduce a 𝐝𝐮𝐚𝐥-𝐛𝐨𝐮𝐧𝐝 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡, 𝐮𝐬𝐢𝐧𝐠 𝐛𝐨𝐭𝐡 𝐚 𝐥𝐨𝐰𝐞𝐫- 𝐚𝐧𝐝 𝐚𝐧 𝐮𝐩𝐩𝐞𝐫-𝐛𝐨𝐮𝐧𝐝 𝐜𝐢𝐫𝐜𝐮𝐢𝐭, to tightly control the error in the gradient approximation. This improves the robustness and trustworthiness of constraint-based learning.
[INGI] 2025-08-26 (11:00) : Robustness for Tabular Machine Learning: A Back-and-Forth Journey between Research and Industry
At Shannon room
Speaker:
Maxime Cordy (SnT Université du Luxembourg)
Abstract: Adversarial attacks are widely recognized as a critical security threat for machine learning, but most were originally designed in the context of image recognition, where arbitrary pixel-level perturbations are applied. Such attacks often fail to capture the realities of domains governed by strict constraints on valid inputs. This is particularly the case in tabular machine learning, where only feasible feature-level perturbations can occur. This talk explores the gap between classical adversarial attack formulations and real-world applicability. I will review our recent research on constrained feature-space attacks, which aim to generate realistic adversarial examples under domain-specific restrictions. Drawing on our own experience in applying these methods to industrial use cases, I will highlight the challenges of evaluating robustness in practice and discuss opportunities for new research in this area.
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Please visit seminars archive for the whole list.