Agenda
December
-
11:0012:00
Mouna Safir (ENS Lyon)
Title: Fault tolerance in distributed systems
Abstract:
One of the key principles of distributed computing is to replicate data and distribute tasks to improve the availability and fault-tolerance. But this distribution poses new challenges, in particular the coordination of processes. To ensure the consistency and proper functioning of the system, distributed entities must exchange information and reach joint decisions, even in the case of breakdowns or unpredictable behaviour. In this context, fault-tolerance becomes a central issue. Distributed systems must continue to operate despite partial failures or abnormal behaviours of certain components.
In my research, I explored two ways to strengthen the resilience of distributed systems:
- The k-agreement, which makes it possible to ensure coordination despite the faults.
- Self-stabilization, which aims to automatically restore the system after temporary disturbances.In the course of my presentation, I will present the main results achieved in these two lines of research.
EnglishLaBRI 178 -
14:0015:00
TBA
EnglishLaBRI, salle 178 -
14:0017:00
Jury members:
- Robert ROSS, Deputy Division Director/Senior Computer Scientist, Argonne National Laboratory (United States) - Rapporteur
- Jesus CARRETERO, Full Professor, Universidad Carlos III de Madrid (Spain) - Rapporteur
- Gabriel ANTONIU, Director of Research, Inria - Rapporteur
- Thomas HERAULT, Director of Research, Inria - Examiner
- Vania MARANGOZOVA, University Professor, Université Grenoble Alpes - Examiner
- Julien KUNKEL, Professor, University of Göttingen (Germany) - Examiner
- Brice GOGLIN, Director of Research, Inria - Director of Research
Salle Ada Lovelace (Inria) -
15:3018:30
Clément Gaspard will defend his thesis on December 8, 2025, at 3:30 p.m., in Lecture Hall 2 of Building 2A at the Bordeaux University Institute of Technology (Gradignan).
The title of his thesis is: “Locomotion, step planning, and fall resistance in humanoid robots via reinforcement learning policies.”
Amphi 2 Bât 2A (IUT de Bx, Gradignan) -
14:0017:00
The adoption of electronic health records has considerably enhanced access to large volumes of clinical data. While this accessibility is invaluable for both healthcare delivery and research, it also introduces new challenges arising from the complexity of medical data. These challenges include its implicitness (i.e., the need for domain expertise to interpret data), imperfections such as inconsistency, uncertainty, and incompleteness , and its inherently temporal nature. This thesis investigates how logic-based approaches can address these challenges.
First, I investigated an ontology-driven approach to illustrate how ontologies can be used to evaluate medical data quality, with a focus on lung cancer phenotyping. This involved designing an ontology to capture essential domain knowledge and applying it to query the Clinical Data Warehouse of Bordeaux University Hospital. The work highlighted both the benefits of ontologies in representing domain knowledge and identifying inconsistencies, as well as their limitations, particularly in handling temporally inconsistent healthcare data.
Building on this experience, I then proposed a novel logic-based framework for inferring high-level events from temporal clinical data, in a way that better aligns with clinical reasoning and decision-making . The framework defines logical rules specifying the existence conditions of an event at a given time-point, along with optional termination conditions that signal its possible end. It also introduces two aggregation methods to construct event intervals from these conditions. Furthermore, the formalism supports the definition of meta-events, obtained by combining or generalizing other events, and integrates confidence levels and a repair mechanism to handle imperfections in event detection. To validate the framework, I implemented its core components using Answer Set Programming, a declarative logic programming paradigm, and evaluated the resulting system, CASPER, on two medical use cases. The evaluation showed both computational feasibility and alignment with expert medical opinions.
Amphi LaBRI -
15:3016:30
Locomotion, step planning, and fall resistance in humanoid robots via reinforcement learning policies
EnglishAmphi 2 Bat A IUT Gradignan -
14:3018:00
Chabname Ghassemi Nedjad will defend her thesis on December 11, 2025, at 2:30 p.m. in the LaBRI lecture hall.
The title of her thesis is: “Modeling and solving combinatorial optimization problems for reverse ecology.”
Amphi LaBRI -
11:0012:00
Hussein Kazemi (LaBRI)
Title: Trajectory visibility at first sight
Abstract:
Let P be a simple polygon with n vertices, and let two moving entities q(t) and r(t) travel at constant (possibly distinct) speeds vq and vr along line-segment trajectories τq and τr inside P. We study the exact first-visibility time t∗= min t≥0: q(t) r(t) ⊆P, the earliest moment at which the segment joining q(t) and r(t) lies entirely within P.
Prior work by Eades et al. focused on this question in the setting of a simple polygon. They gave a one-shot decision algorithm running in O(n) time. For a stationary entity and a moving one, they suggested a structure that, after O(n log n) pre-processing, answers the decision query in O(log n) time, requiring O(n) space. In addition, for moving entities, after preprocessing time of O(n log⁵ n), they construct a data structure with O(n^{3/4} log³ n) query time and O(n log⁵ n) space. Variants for polygonal domains with holes or when entities cross the boundary of P lie beyond our scope.
In this work, we go beyond the decision to compute t* exactly under three models for a simple polygon P. When both trajectories are known in advance, we preprocess P in O(n) time and space and thereafter answer each query in O(log n) time. If one trajectory τr is fixed while τq is given as query, we build a structure in O(n log n) time and space that computes t∗in O(log² n) time per query. In a setting where the trajectories are not known in advance, we develop a randomized structure with O(n^{1+ε}) expected pre-processing time and O(n) space, achieving an O(√n polylog(n)) expected query time for any fixed ε > 0.
EnglishLaBRI 178