Francesco Alesiani
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    • Adaptive Width Neural Networks accepted at ICLR 2026
    • LRIM benchmark accepted at ICLR 2026
    • Presented "Guidance of Diffusion-Based Conditional Generative Models for Antibody Design" at PriGM@EurIPS 2025
    • Attended SIMBIOCHEM@EurIPS 2025 — Viktor presented "Transferable long-range interactions in machine-learned interatomic potentials"
    • Paper accepted in Journal of Chemical Theory and Computation (JCTC)
    • Paper on DIMOS (PyTorch molecular dynamics framework) accepted in J. Chem. Phys.
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  • Publications
    • Clifford Kolmogorov-Arnold Networks
    • Logical Guidance for the Exact Composition of Diffusion Models
    • Adaptive width neural networks
    • Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
    • Geometric Kolmogorov-Arnold Superposition Theorem
    • Long-Range Ising Model - A Benchmark for Long-Range Capabilities in Graph Learning
    • Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
    • Variational Kolmogorov-Arnold Network
    • ACCELERATING PARTICLE SIMULATIONS USING MACHINE LEARNING AND MOLECULAR DYNAMIC SIMULATIONS
    • Hierarchy-based Clifford Group Equivariant Message Passing Neural Networks
    • Information system for generation of complex molecule by using graph representing molecule
    • Machine learning knowledge management based on lifelong boosting in presence of less data
    • Learning logical rules over graph structured data using message passing
    • Generalized Precise Orbit Prediction of LEO Satellites via Physics Informed Machine Learning
    • Graph Reasoning Networks
    • Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
    • Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
    • Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad
    • Machine learning for optimized learning of human-understandable logical rules from medical or other data
    • Hyper network machine learning architecture for simulating physical systems
    • Systems and methods for learning human-understandable logical rules from data
    • Method for verifying information
    • LEO Satellite Orbit Prediction with Physics Informed Machine Learning
    • Method and system for scalable multi-task learning with convex clustering
    • Affinity graph extraction and updating systems and methods
    • Partial planar point cloud matching using machine learning with applications in biometric systems
    • Mechanism for reducing information lost in set neural networks
    • Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
    • CAPE: channel-attention-based PDE parameter embeddings for SciML
    • Continual Invariant Risk Minimization
    • Continual learning of artificial intelligence systems based on bi-level optimization
    • Continuous-Discrete Message Passing for Graph Logic Reasoning
    • Differentiable MaxSAT Message Passing
    • Gated information bottleneck for generalization in sequential environments
    • Implicit bilevel optimization: Differentiating through bilevel optimization programming
    • Learning neural pde solvers with parameter-guided channel attention
    • Self-tuning Hamiltonian Monte Carlo for accelerated sampling
    • End-to-end channel estimation in communication networks
    • Method and system to differentiate through bilevel optimization problems using machine learning
    • Method for predicting a motion of an object
    • Scalable, accurate and reliable measure of variable dependence and independence, and utilization of the measure to train a neural network
    • BiGrad: Differentiating through bilevel optimization programming
    • Constrained clustering for the capacitated vehicle routing problem (cc-cvrp)
    • Human-centric research for nlp: Towards a definition and guiding questions
    • HyperFNO: Improving the generalization behavior of Fourier Neural Operators
    • Modular-relatedness for continual learning
    • Pdebench: An extensive benchmark for scientific machine learning
    • Principle of relevant information for graph sparsification
    • Principle of Relevant Information for Graph Sparsification (Supp. Material)
    • Method and system for generating robust solutions to optimization problems using machine learning
    • Methods and systems for graph approximation
    • Constrained vehicle routing using clusters
    • Method and system for reliable computation of a program
    • Method for verifying information
    • Bilevel Continual Learning
    • Bilevel Continual Learning
    • Measuring dependence with matrix-based entropy functional
    • Optimization of collection and consolidation operations in cross-border multi-modal distribution networks
    • Reinforcement Learning for Route Optimization with Robustness Guarantees.
    • Towards interpretable multi-task learning using bilevel programming
    • Method for motion planning for autonomous moving objects
    • Learning an interpretable graph structure in multi-task learning
    • Measuring the discrepancy between conditional distributions: Methods, properties and applications
    • Method for robust control of a machine learning system and robust control system
    • System and method for real-time large image homography processing
    • Efficient and scalable multi-task regression on massive number of tasks
    • Robust timetable optimization for bus lines subject to resource and regulatory constraints
    • Method for the continuous processing of two-level data on a system with a plurality of nodes
    • Method and system for providing demand-responsive dispatching of a fleet of transportation vehicles, and a mobility-activity processing module for providing a mobility trace database
    • Method for incident detection in a time-evolving system
    • On learning from inaccurate and incomplete traffic flow data
    • Reinforcement learning-based bus holding for high-frequency services
    • Reliable bus dispatching times by coupling Monte Carlo evaluations with a Genetic Algorithm
    • Locally growing rapid tree (LGRT) motion planning for autonomous driving
    • Remote testimony: How to trust an autonomous vehicle
    • A Scenario-Oriented Approach for Noise Detection on Traffic Flow Data
    • Drift3flow: Freeway-incident prediction using real-time learning
    • D2. 13-Final report eCoMessages
    • 2014 Index IEEE Intelligent Transportation Systems Magazine Vol. 6
    • A probabilistic activity model for predicting the mobility patterns of homogeneous social groups based on social network data
    • Educated rules for the prediction of human mobility patterns based on sparse social media and mobile phone data
    • Opportunistic solution-space reduction techniques for reducing the time complexity of dynamic speed control with microsimulation on motorways
    • Optimization of charging stops for fleet of electric vehicles: A genetic approach
    • Afshin Abdi, Qualcomm Milad Abolpour, University of Oulu Ibrahim Abou-Faycal, American University of Beirut Maria Abu-Sini, Technion
    • D242. 213 (D2. 13) Final report ecoMessages
    • Real-Time Eco-Driving Prototype
    • VTC2013-Spring Technical Programme Committee
    • Cooperative ITS messages for green mobility: an overview from the eCoMove project
    • A Scenario-Oriented approach for Noise detection on Traffic Flow data
    • SubProject No. SP2 SubProject Title Core Technology Integration Workpackage No. WP2. 5 Workpackage Title Integration and Verification Task No. 2.5. 3 Task Title Verification test of
    • Blind receiver for space-time differentially-encoded CDMA systems on multipath fading channels
    • Demonstrator 1 Negotiated Priority at Intersections: The Oslo Case Study
    • Optimal Speed Profile Trajectory Computation for Vehicle Approach at Intersection with Adaptive Traffic Control
    • Regional Scale Real-Time Origin-Destination Matrix Estimation Technique and Deployment Results
    • MAPPING TRAFFIC MANAGEMENT SYSTEMS DATA INTO DETAILED NAVIGATION NETWORKS.
    • CVIS. D. 3.3 Architecture and System Specifications
    • D. FOAM. 3.1 Architecture and System Specifications
    • Satellite-Aided Navigation and Related Applications for Dangerous Transport
    • Differential space-time CDMA with turbo decoding
    • Performance of adaptive modulation techniques in the UMTS system
  • Blog
    • Presented "Guidance of Diffusion-Based Conditional Generative Models for Antibody Design" at PriGM@EurIPS 2025
    • Attended SIMBIOCHEM@EurIPS 2025 — Viktor presented "Transferable long-range interactions in machine-learned interatomic potentials"
    • Paper accepted in Journal of Chemical Theory and Computation (JCTC)
    • Paper on DIMOS (PyTorch molecular dynamics framework) accepted in J. Chem. Phys.
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Learning logical rules over graph structured data using message passing

Mar 1, 2024·
Francesco Alesiani
,
Markus Zopf
,
Cristobal Felipe Corvalan Morbiducci
· 0 min read
Last updated on Mar 1, 2024
← Machine learning knowledge management based on lifelong boosting in presence of less data May 1, 2024
Generalized Precise Orbit Prediction of LEO Satellites via Physics Informed Machine Learning Jan 1, 2024 →

© 2026 Francesco Alesiani. This work is licensed under CC BY NC ND 4.0

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