Tim Verdonck professor

Research Tim Verdonck

Research team

Expertise

The development and application of machine learning techniques for (mainly) financial, actuarial and economic data sets. The development and application of robust statistics for anomaly detection. Tim Verdonck is chairholder of the BNP Paribas Fortis Chair on Fraud Analytics, the Allianz Chair on Prescriptive Business Analytics in Insurance and the BASF Chair on Robust Predictive Analytics.

VeloCoach. 01/09/2024 - 31/08/2025

Abstract

Brailsports aims to revolutionise endurance training with AI-optimised sports-scientific models, including the Lactate Threshold and Personalized Fitness Fatigue models. The outputs of these models are currently implemented in an online dashboard, providing coaches tailored insights and customized analytics about their athletes' performance, recovery and training needs. This empowers coaches to make data-driven decisions, enhancing the training regimen's effectiveness and efficiency. This project's objective is to elevate our minimal viable product into a commercializable one, targeting the designated beachhead market of endurance coaches. Looking ahead, Brailsports envisions expanding from a coach-centric platform to developing athlete-focused solutions. We envision a sophisticated mobile application that serves as a personal coach in every athlete's pocket. This app will offer dynamic, AI-driven training plans that adjust in real time to reflect the athlete's current performance metrics, physiological responses and evolving objectives. By doing so, Brailsports aims to democratise access to elite-level training insights, making personalized, science-backed training regimens accessible to athletes at all levels. This expansion will empower athletes with the tools they need for optimal performance, fostering a more informed, connected, and personalized approach to endurance training. In addition to the commercialization of the dashboard, we aim to take the first steps in the development of this mobile application in this project.

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  • Research Project

Flanders Artificial Intelligence Research program (FAIR) – second cycle. 01/01/2024 - 31/12/2028

Abstract

The Flanders AI Research Program is a strategic basic research program with a consortium of eleven partners: the five Flemish universities (KU Leuven, University of Ghent, University of Antwerp, University of Hasselt, Vrije Universiteit Brussel) and six research centers (imec, Flanders Make, VIB, VITO, Sirris and ILVO). The program brings together 300+ researchers on new AI methods that can be used in innovative applications in health, industry, planet&energy and society. This way, the program contributes to a successful adoption of AI in Flanders. The ambition is for Flanders to occupy a strong international position in the field of strategic basic research in AI, and this within a strong and sustainable Flemish ecosystem. Five focus research themes have been selected: responsible AI, human-centered AI, sustainable AI (energy-efficient and high-performance), productive and data-efficient AI (systems that require little data, which perform by combining data with domain knowledge and experience of experts) and resilient and high-performant AI (robust against changes in the environment). The description of the work packages and their research tasks defines the aspects within these themes that will be investigated in the program. The AI solutions are demonstrated in real-life use cases. These results not only demonstrate the effectiveness, but also inspire companies for adoption and researchers for further research. The Flanders AI Research Program is part of the Flanders AI Policy Plan. More info: www.flandersairesearch.be

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  • Research Project

FraudPANDA -- Counteracting Fraud using Pro-Active (Network) Detection and Analysis; 01/11/2023 - 31/10/2025

Abstract

Financial transaction fraud remains an important societal challenge, despite decades of research on automatic fraud detection systems. In light of these struggles, I propose to tackle fraud from a different perspective: rather than reactively detecting fraud after it occurs, I aim to proactively prevent fraud before it even happens. The envisioned research project, FraudPANDA, aims to counter-act fraudsters by using Pro-Active (Network) Detection and Analysis. Firstly, I aim to proactively detect who is at risk of becoming targeted before the attack happens. I will develop methods to find the relevant customer characteristics. Secondly, I aim to analyse different preventive interventions and analyse their efficacy at decreasing a customer's fraud risk. This will be based on insights from causal inference. Thirdly, for both methods, I will also explore the possibility of using network information to improve prevention performance. This is motivated by recent success of network analysis in fraud detection. In the end, the ultimate goal of FraudPANDA is an innovative solution that operationalizes these insights by optimally allocating preventive interventions and minimizing fraudulent activities. A field test will be conducted at an industrial partner to evaluate FraudPANDA's ability to proactively prevent fraud in real-world scenarios.

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  • Research Project

Robust Directed Acyclic Graph Learning for Causal Modeling. 01/11/2022 - 31/10/2026

Abstract

Due to technological advances, the available amount of data has increased exponentially over the last decade. The field of data science (DS) has followed this growth as it provides an indispensable tool for translating data into insight and knowledge. Where DS was traditionally concerned with learning associations in data, it has become clear in recent times that causal relations often provide a deeper understanding of the data and a stronger tool in many practical applications. One of the established approaches to causal modeling is to use a directed acyclical graph (DAG) to represent the causal relations. These DAGs have to be learned based on observed data. Many of the SOTA techniques for DAG learning are very sensitive to anomalies, and yield unreliable results in their presence. We aim to develop methods for DAG learning that remain efficient and reliable under contamination of the data. The project starts by building a solid foundation for the concepts of robustness in DAG learning. Building upon these foundations, we will then proceed to build a general robust DAG learning methodology. The project envisions three different but complementary approaches to the development of robust DAG learning methods. The developed methodology will be evaluated theoretically and empirically, and tested in a variety of real world cases.

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  • Research Project

StatUA. 01/01/2022 - 31/12/2026

Abstract

StatUa was recognized and funded as a core facility of the University of Antwerp in 2009, with the mission to facilitate scientific research at UAntwerp via statistical and methodological support of researchers. Following positive evaluations, this recognition was renewed twice: in 2011 for the period 2012-2016 and in 2016, for the period 2017-2021. In virtually all fields of research, the importance of proper methodological setup and state-of-the-art statistical analysis is increasing. Since its recognition, StatUa has assisted researchers from all faculties from UAntwerp. This resulted in co-autorship of a StatUa collaborator in over 200 internationally peer-reviewed publications. Moreover, these publications only represent a fraction of all research projects to which StatUa has contributed. Apart from direct support to individual researchers, StatUa has a close collaboration with Antwerp Doctoral School for teaching statistics and methodology to doctoral students, through short courses and hands-on practical sessions. Within the UAntwerp, StatUa is an important and well-known point of contact for researchers with statistical and methodological questions. The primary focus of this proposal is to renew the recognition of StatUa as a core facility, to continue our statistical and methodological support of the researchers at UAntwerp.

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  • Research Project

POSITE: Process optimization with sequential individual treatment effects. 01/11/2021 - 31/10/2025

Abstract

Process optimization is of crucial importance to businesses. Because of the involved complexity, businesses typically rely on domain experts for operating business processes. In this proposal, the aim is to develop a data-driven approach by leveraging recent advances in causal machine learning (CML) to support decision-making. To this end, Process Optimization with Sequential Individual Treatment Effects (POSITE) is introduced. POSITE is a powerful, reliable and flexible methodology that is capable of learning models to accurately predict causal effects in complex, sequential decision-making processes using causal machine learning. The resulting causal models are interpretable and robust, as such ensuring reliability and usability. The methodology is embedded within a cost-sensitive, constrained and stochastic decision-making framework to guide process operators in optimization process outcomes. The envisioned solution is an actionable and innovative approach toward process optimization that opens various directions for future fundamental and applied research.

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  • Research Project

IMEC-Super Bio-Accelerated Mineral weathering: a new climate risk hedging reactor technology (BAM). 01/09/2021 - 31/08/2025

Abstract

Conventional climate change mitigation alone will not be able to stabilise atmospheric CO2 concentrations at a level compatible with the 2°C warming limit of the Paris Agreement. Safe and scalable negative emission technologies (NETs), which actively remove CO2 from the atmosphere and ensure long-term carbon (C) sequestration, will be needed. Fast progress in NET-development is needed, if NETs are to serve as a risk-hedging mechanism for unexpected geopolitical events and for the transgression of tipping points in the Earth system. Still, no NETs are even on the verge of achieving a substantial contribution to the climate crisis in a sustainable, energy-efficient and cost-effective manner. BAM! develops 'super bio-accelerated mineral weathering' (BAM) as a radical, innovative solution to the NET challenge. While enhanced silicate weathering (ESW) was put forward as a potential NET earlier, we argue that current research focus on either 1/ ex natura carbonation or 2/ slow in natura ecosystem-based ESW, hampers the potential of the technology to provide a substantial contribution to negative emissions within the next two decades. BAM! focuses on an unparalleled reactor effort to maximize biotic weathering stimulation at low resource inputs, and implementation of an automated, rapidlearning process that allows to fast-adopt and improve on critical weathering rate breakthroughs. The direct transformational impact of BAM! lies in its ambition to develop a NET that serves as a climate risk hedging tool on the short term (within 10-20 years). BAM! builds on the natural powers that have triggered dramatic changes in the Earth's weathering environment, embedding them into a novel, reactor-based technology. The ambitious end-result is the development of an indispensable environmental remediation solution, that transforms large industrial CO2 emitters into no-net CO2 emitters.

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  • Research Project

IDLab - Internet and Data Lab 01/01/2021 - 31/12/2026

Abstract

The IOF consortium IDLab is composed of academic supervisors at the IDLab Research Group, a UAntwerp research group with members from the Faculty of Science and the Faculty of Applied Engineering. IDLab develops innovative digital solutions in the area of two main research lines: (1) Internet technologies, focusing on wireless networking and Internet of Things (IoT), and (2) Data science, focussing on distributed intelligence and Artificial Intelligence (AI). The mission of the IDLab consortium is to be the number one research and innovation partner in Flanders and leading partner worldwide, in the above research areas, especially applied in a city and its metropolitan surroundings (industry, ports & roads). To realize its mission, IDLab looks at integrated solutions from an application and technology perspective. From an application point of view, we explicitly provide solutions for all stakeholders in metropolitan areas aiming to cross-fertilize these applications. From a technological point of view, our research includes hardware prototyping, connectivity and AI, enabling us to provide a complete integrated solution to our industrial partners from sensor to software. Over the past years, IDLab has been connecting the city and its surroundings with sensors and actuators. It is time to (1) reliably and efficiently connect the data in an integrated way to (2) turn them into knowledgeable insights and intelligent actions. This perfectly matches with our two main research lines that we want to extensively valorise the upcoming years. The IDLab consortium has a unique position in the Flemish eco-system to realize this mission as it is strategically placed across different research and innovation stakeholders: (1) IDLab is a research group embedded in the Strategic Research Centre imec, a leading research institute in the domain of nano-electronics, and more recently through groups such as IDLab, in the domain of digital technology. (2) IDLab has a strategic link with IDLab Ghent, a research group at Ghent University. While each group has its own research activities, we define a common strategy and for the Flemish ecosystem, we are perceived as the leading partner in the research we are performing. (3) IDLab is the co-founder of The Beacon, an Antwerp-based eco-system on innovation where start-ups, scale ups, etc. that work on IoT and AI solutions for the city, logistics, mobility and industry 4.0 come together. (4) Within the valorisation at UAntwerp, IDLab contributes to the valorisation within the domain 'Metropolitanism, Smart City and Mobility'. To realize our valorisation targets, IDLab will define four valorisation programs: VP1: Emerging technologies for next-generation IoT; VP2: Human-like artificial Intelligence; VP3: Learning at the edge; VP4: Deterministic communication networks. Each of these valorisation programs is led by one of the (co-)promoters of the IDLab consortium, and every program is composed of two or three innovation lines. This way, the IDLab research will be translated into a clear program offer towards our (industrial) partners, allowing us to build a tailored offer. Each valorisation program will contribute to the different IOF objectives, but in a differentiated manner. Based on our current experience, some valorisation programs are focusing more on local partners, while others are mainly targeting international and EU funded research projects.

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  • Research Project

BrailSports. 01/05/2023 - 30/04/2024

Abstract

In this IOF POC, we aim to bring together the expertise in sports science and machine learning to develop intelligent tools for coaching endurance sports. These tools will assist the coach in tracking the fitness level of the athletes and provide early warning of any potential issues within the physiological data. By leveraging the power of machine learning, we hope to create a more efficient and effective coaching process that can help athletes reach their full potential. Additionally, by integrating sports science knowledge, we aim to ensure that the tools we develop are grounded in the latest research and understanding of how the body responds to endurance training.

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  • Research Project

IMEC-AI4FoodLogistics. 01/04/2021 - 31/03/2023

Abstract

The project targets (i) a novel, virtual and distributed data ecosystem for food delivery to physical shops that becomes hyper responsive and efficient thanks to (ii) more accurate forecasting and personalization models that use enhanced AI and scheduling technologies to (iii) optimize the end-to-end logistics from farmers to Distribution Centers (DCs) to stores and customers.

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  • Research Project

data-driven anomaly detection and cashflow prediction for accountants 01/09/2020 - 31/08/2024

Abstract

Just like many industries today, the accountancy sector is also confronted with disruptive digitalization. This digitization means that accountants are expected to provide more and more proactive services, where the focus used to be on executive and compliance-related work. With our project we want to help accountants to fulfill these new expectations. By applying advanced statistical methods and machine learning techniques, we want to focus strongly on following two research topics. First of all, we want to test and develop different methods to discover anomalies ​​in accounting data. This helps the accountant to automate standard checks, but also to discover potential opportunities. Secondly, we want to test and develop robust and interpretable cash flow forecasting models. In both areas we are convinced that there is still enormous potential to create added value for the accountant. The collaboration with Boltzmann provides the ideal context for this project due to the presence of a rich, ever-expanding dataset, combined with professional expertise in various areas within the framework project team.

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  • Research Project

Robust and sparse methods to model mean and dispersion behavior in Generalized Linear Models. 01/10/2019 - 30/09/2023

Abstract

The Generalized Linear Model (GLM) is a very popular and flexible class of regression models that generalizes ordinary linear regression by allowing for example non-normal response variables. Logistic regression, which is widely used for binary classification, and Poisson regression, often used to model count data, both belong to this class. The parameters are typically estimated using maximum likelihood, but this very often leads to various problems when analyzing real data from practice. Firstly, outliers in the data may heavily influence classical methods, yielding unreliable results. Secondly, estimation and interpretability becomes very difficult or impossible when the number of variables becomes very high. Thirdly, real data often display a more complex dispersion behavior than expected under the GLM model. To solve these issues, sparse and robust estimation methods that model simultaneously the mean and the dispersion behavior in the context of GLMs will be developed. Their mathematical properties will be thoroughly investigated. The newly proposed methods should also be computationally efficient such that modern large datasets can be analyzed easily. Open-access user-friendly software will be provided.

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  • Research Project

IMEC-A glimpse into the Arctic future: equipping a unique natural experiment for next-generation ecosystem research (FutureArctic). 01/06/2019 - 31/05/2022

Abstract

Climate change will affect Arctic ecosystems more than any other ecosystem worldwide, with temperature increases expected up to 4-6°C. While this is threatening the integrity and biodiversity of the ecosystems in itself, the larger ecosystem feedbacks triggered by this change are even more worrisome. During millions of years, atmospheric carbon has been stored in the Arctic soils. With warming, the carbon can rapidly escape the soils in the form of CO2 and (even worse) the strong greenhouse agent CH4. Despite decades of research, scientists still struggle to unveil the scale of this carbon exchange, and especially how it will interact with climate change. An overarching question remains: how much carbon will potentially escape the Arctic in the future climate, and how will this affect climate change? FutureArctic embeds this research challenge directly in an inter-sectoral training initiative for early stage researchers, that aims to form "ecosystem-of-things" scientists and engineers at the ForHot site. The FORHOT site in Iceland offers a geothermally controlled soil temperature warming gradient, to study how Arctic ecosystem processes are affected by temperature increases as expected through climate change.

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  • Research Project