The control room of the future: AI empowered dashboards
Firms are increasingly investing in AI to support their operational decision-making processes. During this study day you get a realistic picture of the opportunities and limitations of AI for decision support via real-time dashboards.
Practical information:
Want to register?
- Register until: 07 Oct 2022
- Prerequisites: Basic knowledge of AI terminology (you know what AI is), no mathematical/coding experience is required
- Price: professionals: €250 / researchers: €80
Registrations are collected on the website of UGhent Faculty of Economics and Business. 👇
A special focus will be given to control rooms as this sector is increasing in importance with the digitization of operations. The workshop covers three topics:
- Implementing AI in Control Rooms
- Predictive Analytics
- Business Integration (managerial focus)
Programme
Morning
9.00 - Coffee reception
9.30 - Opening of the workshop by Bart Roets (Infrabel), moderator
9.40 - Keynote Railway Traffic Control Room, and Room for Control >>>Francesco Corman, Chair of Transport Systems at The Swiss Federal Institute of Technology - ETH Zurich
10.20 - How to address undesirable workload peaks and lows >>>Léon Sobrie, On Track Lab
11.00 - Coffee break
11.20 - Developing Decision support systems for Traffic Control >>>Wilco Tielman, ProRail
12.00 - The role of AI in decision support tools for electricity grid control rooms >>>Hakan Ergun & Hussain Kazmi, EnergyVille
12.40 - Lunch + (small) poster session
- Toon Vanderschueren - Prescriptive maintenance with causal machine learning
- Simon De Vos - Internal Placement: Job Recommender Systems with Social Regularization
- Changyu Men - Balancing workload in digital railway control rooms
Afternoon
13.40 - Structured Exploration of Complex Adaptations >>>Riccardo Patriarca, Sapienza Universitá di Roma and
Antonio Licu, Eurocontrol
14.20 - Christophe Hurter
15.00 - Coffee break
15.20 - Putting the AI in air traffic control >>> Evelina Gabasova, The Alan Turing Institute
16.00 - Debate: Making it real: Industry demand as a driver for further research >>>Peter Prater, International Critical Control Rooms Alliance (ICCRA).
16.40 - Closing workshop by Marijn Verschelde, IÉSEG School of Management
16.50 Networking Drink
Learning Goals
- You have a realistic image of the opportunities and limitations of AI in control rooms / safety-critical environments.
- Via a discussion about real-world implementations, you can build connections between practice and research & development.
Railway Traffic Control Room, and Room for Control
By Francesco Corman - Chair of Transport Systems at Swiss Federal Institute of Technology, ETH Zurich
We review different challenges and opportunities for traffic control in railway systems. From the point of view of sensing, state estimation and data fusion must be performed in a very short time, and for units that are typically spatially dispersed. From the point of view of determining a control objective to support automatic decisions, the challenge is how to understand the impact of a decision in terms of system performance. Almost all of those problems have to deal with unknown future states, which must be predicted, typically by model-based or black box approaches, also based on advanced analytics. Once an objective function and optimization variables are determined, optimization models can help to find a solution quickly and effectively. Further challenges are the acceptance of decision stakeholders, within the control room, but also within the travelers and operators, or the direct implementation of automatic digital control. For passenger-oriented traffic control, this is particularly interesting and challenging, due to the large amount of possible decisions per decision maker, and data that can partially describe those aspects, which calls for machine learning approaches. To reach all those goals, the system must have room for control, in another sense, flexibility in operations must be built in already from the planning, to be exploited in the real-time horizon when needed.
How to address undesirable workload peaks and lows
by Leon Sobrie - On Track Lab
Digitisation and employee workload (im)balance are intertwined. To address undesirable workload peaks and lows, we propose a 2-step machine learning model to provide real-time workload analytics per controller in digital safety-critical control rooms. The advocated model leverages a rich real-time data structure with disaggregated event-level taskload data. Next to exploring different machine and deep learning approaches, we compare the performance of a model that predicts aggregate workload with the performance of the aggregate of different models that predict specific task loads. We develop a business application that utilizes the proposed model to provide detailed predictive analytics that open the black box of workload imbalance and, in this way, empowers the control room manager with real-time insights.
The Black Box in AI Decision Making
by Christophe Hurter - ENAC
The Decision Making Process is already associated with AI. The algorithms are meant to help ATCOs in daily tasks, but they still face acceptability issues. Today’s automation systems with AI/Machine Learning do not provide additional information on top of the Data Processing result to support its explanation, making them not transparent enough. The Decision Making Process is expected to become a “White Box”, giving understandable outcome through an understandable process. XAI SOLUTIONS: Transparency and Explainability: ARTIMATION’s goal is providing a transparent and explainable AI model through visualization, data driven storytelling and immersive analytics. This project will take advantage of human perceptual capabilities to better understand AI algorithm with appropriated data visualization as a support for explainable AI, exploring in the ATM field the use of immersive analytics to display information.
Structured Exploration of Complex Adaptations
by Riccardo Patriarca - Sapienza Université di Roma & Antonio Licu - Eurocontrol
Modern systems are complex and understanding the nuances of everyday work requires to explore thoroughly system properties. EUROCONTROL recognized these needs when publishing its white papers on Resilience Engineering almost 15 years ago. The project called “Weak Signal” continued on that side. Besides the theoretical foundation on weak signals definition, detection and management, one of this project’s outputs is the development of a novel tool called SECA (Structured Exploration of Complex Adaptations). SECA helps detecting weak signals in normal air traffic management operations, creating shared organizational knowledge. This latter arises from a collaborative elicitation process that span from tacit and explicit dimensions. SECA is designed to support data gathering and data analysis, integrating traditional thematic analyses with modern Natural Language Processing. The presentation will show the early results of its adoption in two different European ANSPs and the way to scale it up from a prototype to a full-fledged solution, also including the possibility to use the same approach in other industries.
The lecturers
Teachers / speakers
Francesco Corman
Francesco Corman received his MS degree in management engineering from Roma Tre University in 2006 and his PhD degree from the Delft University of Technology in 2010. He is currently the Chair of the Transport Systems (Assistant Professor) at the Swiss Federal Institute of Technology, ETH Zurich, with main responsibilities in research and education in transport systems with a particular focus on analytics and optimization methods for railways, public transport and logistics system and their interconnection, with special focus on their operations.
Hakan Ergun
Hakan Ergun, born in Leoben Austria, obtained his Master of Science in Electrical Engineering at the Graz University of Technology (TU Graz) in October 2009. In February 2010 he has joined the Electa Research Group at KU Leuven, Belgium where he obtained his PhD in Electrical Engineering in January 2015. He was a postdoctoral researcher until 2018 and is nowa research expert at KU Leuven / EnergyVille.
His main research interests are optimization methods in power system planning, power system modelling, power system reliability, electricity markets and regulation. To this date he has worked in several national and international research projects on several aspects of power system modelling and optimization.
He has published several papers in international scientific journals and conferences. He is a senior member of IEEE and is an active member of CIGRE. He has been in the local organization committee of the IEEE EnergyCon 2016 conference in Leuven. He is a vice chair of the IEEE PES/PELS/IAS Benelux Chapter and has been the chapter chair between 2017 and 2018. He is an associate editor with the Canadian Journal of Electrical and Computer Engineering.
Evelina Gabasova
Dr Evelina Gabašová is a data scientist and machine learning researcher, working in The Alan Turing Institute, the UK’s national centre for data science and artificial intelligence. She writes about data science, machine learning and software development.
Christophe Hurter
Christophe Hurter is a Professor working at the University of Toulouse, France, leading the Interactive Data Visualization group (DataVis) of the French National School of Civil Aviation (École nationale de l'aviation civile, ENAC). His research covers explainable A.I. (XAI), big data manipulation and visualization (InfoVis), immersive analytics, and humancomputer interaction (HCI). He investigates the design of scalable visual interfaces and the development of pixel-based techniques. He is an associate researcher at the research center for the French Military Air Force Test Center (CReA, Base militaire de Salon de Provence) and at the Brain and Cognition Research Center (CerCo, Hospital University Center of Toulouse). He published 2 books, 4 book chapters, 20 patents, 25 journal papers, more than 100 per reviewed international research papers.
Hussain Kazmi
Hussain Kazmi is currently an FWO postdoctoral research fellow at KU Leuven, where his research is at the intersection of machine learning, optimal decision making and energy. His core area of expertise lies in developing algorithms that integrate domain expertise and downstream task information into data-driven models for smart(er) energy systems. Very recently, this research work has received the annual award of the International Institute of Forecasters. Currently, he leads a cross-European EIT InnoEnergy working group aimed at developing a data science program for energy engineers.
He holds a PhD at the intersection of data science and energy engineering from KU Leuven (2019), as well as MSc degrees in Sustainable Energy Technology, and Energy and Nuclear Engineering from Technical University of Eindhoven (The Netherlands) and Politecnico di Torino (Italy) respectively. In 2021, he was a visiting research scholar at KTH Royal Institute of Technology (Sweden). As the first data scientist at two different Belgian clean energy startups (Enervalis and iLECO), he has also helped set up data science teams in applied settings.
Antonio Licu
Tony is cumulating Head of Digital Transformation Office and Head of Operational Safety, SQS and Integrated Risk management Safety (NMD/SAF) Unit within Network Manager Directorate of EUROCONTROL (European Organisation for the Safety of Air Navigation).
He leads the deployment of safety management and human factors programmes of EUROCONTROL. He has extensive Air Traffic Control operational and engineering background (master degree in avionics).
Tony’s role as head of DTO (the organisational vehicle driving the Digital Transformation of Eurocontrol Network Manager) is to manage technology, innovation and Digital Transformation for iNM in close cooperation with EUROCONTROL organisational entities and the industrial partners.
Riccardo Patriarca
Riccardo Patriarca is a tenure track assistant professor at Sapienza University of Rome (Italy) – Dept. of Mechanical and Aerospace Engineering. He holds an BSc in Aerospace Engineering, an MSc in Aeronautical Engineering and a PhD in Industrial and Management Engineering (Doctor Europaeus). He has published widely (about 100 manuscripts published in academic journals and conference proceedings) on methodological and epistemological aspects of risk, safety, and resilience management as well as operations management in general. He aims to make systems safer and resilient when - and especially before - things go awry.
Bart Roets
Bart is a Belgian railroad engineer. He has extensive managerial and expert experience at Belgian railways, and international lobbying experience at the Community of European Railways (CER). Bart co-founded the On Track Lab, and currently serves as a Principal Engineer at Infrabel’s Performance Data division. His primary goal is to bridge the gap between practitioners and academics, and foster result-driven collaboration that is beneficial to both parties.
He has developed partnerships with academics from Belgium, France, the UK, the USA, and Australia. Bart holds a MSc in Electronics Engineering, a MSc in Public Management, and a PhD in Business Economics (all from Ghent University, Belgium). He is affiliated researcher at Ghent University, and adjunct professor at IÉSEG School of Management (France). He is also partner and key collaborator in several research projects at Virginia Tech’s System Performance Lab (USA).
Léon Sobrie
Léon Sobrie is a PhD student at Ghent University – Faculty of Economics and Business Administration. He holds a BSc and MSc in Business Engineering. His research focuses on developing and implementing machine learning models for predicting key metrics (delays, workload, safety) in digital control rooms aiming to provide real-time analytics for managers. These research efforts are executed in the context of the On Track Lab, a multi-disciplinary research lab on operations, transportation and network analytics founded by Ghent University, Infrabel and IÉSEG School of Management. His work, co-authored by Marijn Verschelde and Bart Roets, has been presented at international conferences (INFORMS 2021 (Anaheim, US ) & EURO2021 (Athens, Greece)).
Peter Prater
Peter Prater is Founder & Chair of the International Critical Control Rooms Alliance (ICCRA). A well-known face in the Public Safety ICT world, his commitment and passion evidenced through being the Founder and Chair of the International Critical Control Rooms Alliance (www.iccraonline.com), a long-time supporter of the TCCA and a Life Member of British APCO where he fulfilled many roles between 1994 and 2018.
Peter is Hexagon’s UK Managing Director for its Safety, Infrastructure & Geospatial division. Since taking up this appointment the team secured the contract to replace London’s Metropolitan Police Service’s aged command and control solution. He is passionate about the role and operation of critical control rooms and particularly how these are influenced by changes in technology and how they can support the Safe Cities agenda.
Prior to Hexagon, Peter started his working life as a user when he served for 15 years throughout the world with the British Army’s Royal Corps of Signals. Following his army career Peter worked as an independent consultant for 14 years, rising to Head of ICT Consulting at Hyder Consulting before joining Frequentis in 2009 in the role of Key Account Manager for its relationship with the Metropolitan Police Service. Peter’s background then is firmly based in the deployment and use of mobile and fixed communications and command and control systems in support of critical operations.
Marijn Verschelde
Marijn Verschelde is associate professor in quantitative methods at IÉSEG School of Management (LEM-CNRS 9221, Lille, France). He is also visiting professor at KU Leuven and co-founder of the On Track Lab. Further, he is co-coordinator of the new LEM research group Operations Research & Organizations Performance (OPER). His proven track record includes next to publications in the field of operations and applied micro-econometrics, collaborations with companies and central banks concerning firm performance. His most recent research focuses on machine learning for decision making.
Wilco Tielman
Wilco Tielman is a data scientist at ProRail since 2015 and a guest researcher at the Utrecht University within the Intelligent Systems group. He got his Msc degree in Technical Artificial Intelligence at the University of Utrecht. He has been working at ProRail on data science and AI related questions for the traffic control department, with the aim of closing the gap between research and practice.
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