ADA Research Group


Most of our work is focussed on the automated construction of high-performance effective algorithms for challenging computational problems. Many of our current projects are motivated and driven by two general paradigms, programming by optimisation (PbO) and automated machine learning (AutoML), and a broad range of techniques that enable these paradigms; they involve work on methodological foundations, algorithms, tools and applications to a broad range of well-known problems from academia and industry.

We are well-known for our work on automated algorithm configuration, selection and performance prediction techniques, as well as on stochastic search. In the past, our work has improved the state of the art in solving prominent problems from artificial intelligence, such as propositional satisfiability (SAT), the travelling salesperson problem (TSP), AI planning, supervised machine learning and mixed integer programming (MIP), as well as real-world applications, such as hard- and software verification, flow cytometry, exam timetabling, circuit routing and data centre management (see also our list of publications).


Some Current Projects

TAILOR - the ADA research group participates in TAILOR, a European project aiming to connect researchers in the field of Trustworthy Artificial Intelligence (AI). AI has grown in the last ten years at an unprecedented pace. It has been applied to many industrial and service sectors, becoming ubiquitous in our everyday life. More and more often, AI systems are used to suggest decisions to human experts, to propose actions, and to provide predictions. Because these systems might influence our life and have a significant impact on the way we decide, they need to be trustworthy. The TAILOR project consists of five main research areas: Trustworthy AI, Paradigms and Representations, Acting, Social AI and Auto AI.

HumaneAI - the ADA research group participates in the HumaneAI project, which brings together Europe’s leading AI labs to work on the development of human-centered AI, with a strong emphasis on ethics, values by design and appropriate consideration of related legal and social issues.

Physics-aware Automated Machine Learning for Earth Observation Data (with Phi-lab @ European Space Agency and CML @ Leiden University) - adding domain knowledge from the physical sciences to (automated) machine learning for accurate, physically consistent and reliable models for remote sensing data, applicable to domains such as vegetation monitoring, climate modeling and other fields relevant to Digital Twin Earth.

Sparkle - a PbO-based problem-solving platform designed to enable the wide-spread and effective use of programming by optimisation (PbO) techniques for improving the state of the art in solving a broad range of prominent AI problems, including SAT and AI Planning.

Grey-Box Algorithm Configuration (with Thomas Bäck @ LIACS) - aims to develop next-generation general-purpose algorithm configuration techniques that limited knowledge about the internal structure of the algorithm to be configured.

Automated Semi-supervised Learning - leverages automated machine learning (AutoML) methods for improving machine learning methods for different tasks involving labelled and unlabelled data.

Next-generation SLS-based SAT Solving (with Shaowei Cai @ Institute of Software, Chinese Academy of Sciences, China) - uses programming by optimisation (PbO) and conceptual advances in stochastic local search (SLS) to improve the state of the art in solving propositional satisfiability problems from various applications.

Multi-objective Algorithm Configuration for Multi-objective Combinatorial Problems (with Aymeric Blot, Laetitia Jourdan and Marie-Éléonore Kessaci @ Inria Lille Nord-Europe, France) - automatically constructs better solvers for multi-objective combinatorial problems using programming by optimisation (PbO) via multi-objective automated algorithm configuration techniques.

Understanding Scientific Progress using Full-text Analysis of Scholarly Publications (with Wout Lamers, Ludo Waltman, Nees Jan van Eck and Paul Wouters @ CWTS) - uses text analysis and machine learning to assess the way in which scientific progress is enabled through published work.


Some Past Projects

AutoML4HybridModels - AutoML for creating hybrid Earth science models

AUTOML4COVID-19Forecasting - Automated Machine learning for COVID-19 forecasting.

AUTOML4EO - Integrating Remote Sensing Pre-trained Models into AutoML Systems

MultiETSC - Automated Machine Learning for Early Time Series Classification

VPint - Value propagation-based spatial interpolation

SMAC - Sequential Model-based Algorithm Configuration

MO-ParamILS - A Multi-objective Automatic Algorithm Configuration Framework

ParamILS - An Automatic Algorithm Configuration Framework

Hydra - Automatically Configuring Algorithms for Portfolio-Based Selection

Auto-WEKA - Automatic Model Selection and Hyperparameter Optimization in WEKA

AutoFolio - An Automatically Configured Algorithm Selector

SATzilla - Portfolio-based algorithm selection for SAT

AClib - Algorithm Configuration Library

ASlib - Algorithm Selection Library

ESA - Empirical Scaling Analyser

Ablation Analysis - Analysing and Evaluating Parameter Importance

FANOVA - An Efficient Approach for Assessing Hyperparameter Importance

MonoSAT - A SAT Modulo Theory (SMT) Solver for Monotonic Theories over Booleans, Bitvectors and Graphs

SATenstein - Automatically Building Local Search SAT Solvers From Components

UBCSAT - An Implementation and Experimentation Environment for SLS Algorithms for SAT and MAX-SAT