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Abstract and Bio Speakers NGB/LNMB Seminar
Is Data Science a game changer for Operations Research?
Emile Aarts (Tilburg University);
Short Bio:
Professor Emile Aarts has been Rector Magnificus of Tilburg University since June 1, 2015. As part of this position, he is responsible for the university's education policy, including issues relating to the transition from higher secondary to academic education and the cooperation with the universities of applied sciences; the research policy, including indirectly funded and contract research; internationalization; the professorship policy; and academic representation (Doctorate Board).
Emile Aarts (1955) studied physics at Radboud University Nijmegen and obtained a PhD from the University of Groningen in 1983. Between 1983 and 2012, he worked for Philips Research in various research and management positions until he was appointed Chief Scientific Officer in 2009. Since 1990, he has been a part-time Professor of Computer Science at Eindhoven University of Technology. In 2012, he left Philips to pursue his academic career as Dean of the Department of Mathematics and Computer Science at Eindhoven University.
Emile Aarts has an extensive track record as a manager at academic and government institutions, on both national and European levels. He was a member of the European ICT Advisory Board (ISTAG) and, for more than ten years, chaired the Board of the NWO Division of Physical Sciences. He has co-authored more than fifteen books and two hundred scientific publications. He was involved in the launching of the innovation concept 'Ambient Intelligence' (1997) and set up the Philips ExperienceLab (1998), the Intelligent Lighting Institute (2008), and the Data Science Center Eindhoven (2013). In 2014, he launched the Data Science Cluster Initiative, in which Tilburg University and Eindhoven University of Technology have committed themselves for a Brabant data science expert center with international appeal.
His research interests include Data Science, Social Innovation, and Open Innovation.
Title: Data Science drives digital innovation
Abstract: Data science seeks to use all relevant, often complex and hybrid data to effectively tell a story that can be easily understood by non-experts. The emergence of this novel multi-disciplinary field of information processing results from the abundant availability and accessibility of data. This is often referred to as big data, were big relates to typical data assets such as volume, velocity, variability, and veracity. Data science can be considered as the driver of digital innovation. To turn data into value knowledge is needed from a broad range of disciplines extending from mathematics and computer science to ethics and law. Professionals who can handle this knowledge effectively are called data scientists and the corresponding profession is considered as one of the most attractive and exciting career opportunities of the 21st century. The Brainport City Region has identified its potential in relation to this novel human development challenge and announced in June 2016 the launched of the Brainport Data Science Initiative. Central to this large scale activity is the Jheronimus Academy of Data Science (JADS) in Den Bosch and its surrounding data science ecosystem.
The presentation will motivate the significance of the initiative and explain in detail its setup and various constituting components and activities.
Dimitris Bertsimas (MIT)
Short Bio:
Dimitris Bertsimas is currently the Boeing Professor of Operations Research, co-director of the Operations Research Center, faculty director of the Master of Business Analytics at the Massachusetts Institute of Technology. He has received a BS in Electrical Engineering and Computer Science at the National Technical University of Athens, Greece in 1985, a MS in Operations Research at MIT in 1987, and a Ph.D in Applied Mathematics and Operations Research at MIT in 1988. Since 1988, he has been with the MIT faculty.
Since the 1990s he has started several successful companies in the areas of financial services, asset management, health care, publishing, analytics and aviation.
His research interests include analytics, optimization and their applications in a variety of industries. He has co-authored more than 200 scientific papers and four textbooks, including the book 'The Analytics Edge' published in 2016. He is former area editor in Operations Research in Financial Engineering and in Management Science in Optimization. He is currently the editor in chief of INFORMS journal on optimization. He has supervised 63 doctoral students and he is currently supervising 25 others.
He is a member of the US National Academy of Engineering, and an INFORMS fellow. He has received several research awards including the Philip Morse lectureship award (2013), the William Pierskalla award for best paper in health care (2013), the best paper award in Transportation Science (2013), the Farkas prize (2008), the Erlang prize (1996), the SIAM prize in optimization (1996), the Bodossaki prize (1998) and the Presidential Young Investigator award (1991-1996).
Title: The future of OR
Abstract: Operations Research (OR) has historically focused on
the process from models to decisions. Data has been a secondary player.
Machine Learning (ML) has primarily focused on the process from data to models to predictions. Decisions have been a secondary player.
With the availability of data in electronic form in unprecedented quantities, and the realization in industry that data, especially big data, can create value, I argue that the future of OR is Analytics the scientific process of going from data to decisions via models that create value. Under this future, I discuss in this talk the significant opportunities and challenges ahead
especially on how ML will influence OR and vice versa: in both education and research.
Merwin de Jongh (Building Blocks)
Short Bio:
Merwin de Jongh, founder and CTO of Dutch data science company Building Blocks, has the drive and ability to bring state-of-the-art data science to life at places where they make most business impact. He has a strong focus on business value and is backed with strong technical skills, which enables him to implement complex solutions in business infrastructures. Empowering major companies to bring their data science practices to the next level!
Title: OR, the missing link in Data Science
Abstract: The predictive power of data science cannot be denied in today's businesses. It's predictive models and algorithms are considered as the most reliable methods to predict the probability real life events. However, in business we need to translate these predictive insights into actions and results to exploit the true value of our data science solutions. We need to apply optimization algorithms on top of these predictions to directly effects our business results. The latter is a step that only few businesses have made yet, and this is exactly where OR comes into play and completes the field of data science.
Gertjan de Lange (Connecting Business and Optimization SVP, AIMMS)
Short Bio:
Gertjan de Lange is a member of the leadership team of AIMMS and has been with AIMMS since 1995. He worked with many different customers and partners in distinct roles to enable the successful use of the AIMMS optimization technology. After being in charge of sales for almost 15 years, Gertjan took the role of SVP Connecting Business & Optimization at AIMMS in July 2014. This new role was created to promote and discuss the use of analytics, and specifically the use of optimization to potential users and research analysts in and outside the typical Operations Research community. Gertjan has been working on the overall AIMMS product strategy since 2010 to enhance the user experience and support new developments to continuously increase the value that customers can gain from AIMMS.
Gertjan holds an MSc degree in Applied Mathematics (OR) of University of Twente and currently resides, for AIMMS, in the Seattle area (WA, USA).
AIMMS - www.aimms.com - Haarlem, Seattle (WA, USA), Singapore, Shanghai (China).
Title (Pitch): The Intersection of OR and Data Science - opportunities, challenges, and innovation
Abstract:
In the history of our almost 30 years in the market, AIMMS has executed some pivotal changes that were needed to ensure the value of optimization could be fully realized by our customers. Driven by market requirements, customer feedback and innovation initiatives, we remain laser focused on continuing to bring more value each day and broadening awareness of the benefits of optimization.
Looking at the development of the Data Science industry, we realize there is a big opportunity for all of us and our customers. However, it also creates challenges as the modeling paradigms might conflict and the conversations shift. As things evolve there are likely to be significant implications and questions that may arise as new developments in data science, the consumerization of technology and software as a service, force us to think differently on how we service our community of partners and customers. Time will tell what exciting innovations lie ahead of us.
Patrick Hennen (COO at ORTEC Data Science and Managing Partner ORTEC)
Short Bio:
Patrick Hennen is managing partner at ORTEC and heading the consulting division. At ORTEC we want to improve the world using our passion for mathematics. Patrick has 15+ years of experience in the field of Data Science and Operations Research. With his passion for applying mathematics and technology Patrick guides customers to become data-driven innovators, enabling them to grow and build-up their own capabilities. 2 years ago he founded ORTEC Data Science, together with Albert Bogaard (partner at ORTEC and founder of Parkmobile), where the combination of Data Science and Operations Research empowers customers to make improved business decisions by applying predictive and prescriptive analytical models. Patrick has a Master of Science in Econometrics and Operations Research from the VU Amsterdam.
Title (Pitch): Data Science or Operations Research, what's in a name?
Abstract:
Data Science is one of the buzzwords of the last few years. According to Harvard Business Review the Data Scientist is the sexiest job of the 21st century. When I started 15 years ago as an OR Engineer, that was for sure the sexiest job at that time. So, are these just buzzwords or is there really a difference? What is the effect of the enormous amount of data generated these days, and how is this effecting the application of OR today? And why are techniques like neural nets and machine learning suddenly so popular? Just because there is more audience does not mean the game is any different. In this talk I will discuss from several viewpoints (business and academic) how Data Science could be seen as a game changer for OR, but also the other way around. So what's in a name?
Han Hoogeveen (Utrecht University)
Short Bio:
Han Hoogeveen did his PhD research at the Mathematisch Centrum in Amsterdam, held postdoc positions in Eindhoven and Princeton, and worked at the Math department of the TU Eindhoven. Since a long time he is working as an Assistant Professor at the Computer Science department of Utrecht University. His research interests are machine scheduling, planning, and rostering, where he focuses on problems that either originate from practice or that contain properties that are relevant in practice; current issues are robustness and including data mining. He has published many papers in top journals, and tries to motivate students to find good algorithms to solve real-life planning problems. He feels that Operations Research ('doing more with less') can really make a difference in every-day life.
Title: Finding optimal flower cutting strategies through a combination of optimization and data mining
Abstract:
We study a problem that plays an important role in the flower industry: harvesting cuttings from mother plants. Given the (non-constant) demand per week, we must determine beforehand how many mother plants we must plant, and we must decide for each mother plant how many cuttings we must harvest per week. Furthermore, if we cut off more than requested, then we have to decide in which week to cut and sell this. This does not sound very complicated, but working with living material introduces constraints that are rarely encountered in optimization problems. It is well known that if we cut off too many cuttings per week, then the plants cannot sustain this pace, and the production will drop sharply, but the exact relation is unknown. If we show a cutting pattern, which describes how many cuttings are cut per week, to an expert, then he can judge whether it is valid, but there are no properties known that a cutting pattern must satisfy to be valid.
We have tackled this problem by a combination of data mining and linear programming. We apply data mining to infer constraints that a feasible cutting pattern should obey, and we use these constraints in a linear programming formulation. Due to the linearity of the constraints obtained by data mining, this formulation can be reformulated such that it becomes easily solvable.
Aziz Mohammadi (Director Advanced Analytics, VodafoneZiggo)
Short Bio:
Aziz is currently Director Advanced Analytics within VodafoneZiggo. In this role he is responsible for building an Advanced Analytics capability that unlocks the value hidden the huge amounts of data available within the organization using Machine Learning and AI. Prior to joining VodafoneZiggo, Aziz worked for ING Nederland where he fulfilled various roles over the past 9 years. In his most recent role he was responsible for managing the Data Scientists team and worked on different use cases such as churn, detecting fraud, improving processes and detecting income. Aziz has been crucial to increasing the knowledge about big data within ING. He has also been very successful in using customer data to enhance their predictive power as an organization. Aziz lives in Amsterdam, is married and has a daughter of 8 months. He has a strong interest for everything related to new technology, Machine Learning and Artificial Intelligence and the way in which it will impact our lives.
Title: Industry prefers Data Scientists above Operations Researchers
Abstract:
The last 4 years I've spent significant time recruiting the best Data Scientists available on the global market. The last year I noticed a clear trend. Increasingly more students with a background in Econometrics/Operations research apply for our Data Scientist position. Although these students are in high demand in different sectors they come up short for these kind of positions. In this talk I will elaborate on why the typical Econometrician is not the best fit for Data Science jobs. Along the way I will also talk about the way VodafoneZiggo is building up its Advanced Analytics capability and how it uses Machine Learning and AI to turn data into value.
Seppo Pieterse (Fellow and Founder of Quintiq)
Short Bio:
Seppo Pieterse co-founded Quintiq with four colleagues in 1998. Since then he has been responsible for product development, consultancy and presales.
In the last 20 years Quintiq has grown from a group of five programmers to a company with around 1000 people, with 20 offices worldwide, solving planning puzzles for large companies around the world. In 2014 Quintiq joined Dassault Systemes.
Seppo holds a master's degree in computer science and in econometrics from VU Amsterdam.
Title (Pitch): How has Data Science changed OR practice?
Abstract:
Solving large and complex supply chain planning and optimization puzzles is not a simple challenge.
It requires having the right data, modeling the right business rules, deciding on the right KPIs, having good interaction and analysis of the puzzle, its solution and consequences and finally it requires world-class optimization.
Data science is starting to play an important role in this area, a role that for sure will grow rapidly in the coming years.
Alexander Rinnooy Kan (Universiteit van Amsterdam, Big Data Alliance)
Short Bio:
Alexander Rinnooy Kan is universiteitshoogleraar Economie en Bedrijfskunde aan de Universiteit van Amsterdam. Daarnaast is hij Eerste Kamerlid voor D66, is hij o.a. commissaris bij Siemens en Teijin, en voorzitter van de Raad van Toezicht AMC, Prins Bernhard Cultuurfonds, de Balie, Manifesta en EYE. Sinds september 2013 bekleedt hij de rol van vice-voorzitter van de Worldconnectors. Alexander Rinnooy Kan was tussen 2006 en 2012 kroonlid en voorzitter van de Sociaal-Economische Raad (SER). Daarvoor was hij voorzitter van VNO-NCW, Rector Magnificus van de Erasmus Universiteit Rotterdam en lid van de Raad van Bestuur van de ING Groep. Hij studeerde Wiskunde aan de Universiteit Leiden en Econometrie aan de Universiteit van Amsterdam. In 1976 promoveerde hij in de wiskunde aan de UvA.
Title: Data Science landscape in the Netherlands
Abstract:
Data Science - what's in a name - provides an attractive setting for the further development of Operations Research and will help to secure - to the extent required - the successful positioning of our future students on the labour market.
Arjen Vestjens (Managing Partner, CQM)
Short Bio:
Arjen Vestjens is Managing Partner of CQM. CQM helps organizations to create value from data. Using quantitative models from e.g., Data Science and Optimization, CQM creates the framework to analyze processes and improve decision-making based on facts. Optimizing planning and logistics, and improving product and process innovation. Intelligence that brings organizations to the next level on a structural basis. CQM analyzes and clarifies, with a genuine understanding of the specific issues a business faces. Hence, adding value to the business by improving the baseline.
Arjen Vestjens holds a PhD and MSc in Applied Mathematics from Eindhoven University of Technology.
Title: Award winning Data Science and Optimization in the Agro & Food sector
Abstract:
It is becoming more and more complicated for glasshouse growers to steer the purchasing and selling of energy. It is time-consuming and complex due to the enormous amount of factors and data that have to be taken into account when making a bid.
To help greenhouse growers optimize their energy management and lower energy costs, AgroEnergy has developed BidOptimal. An innovation that automates and optimizes the daily bidding process of growers: at what price do I trade what amount of gas and electricity?
BidOptimal calculates APX bids for four days in advance, whereby the heat cost price for the grower is minimized within all boundary conditions. In this way, the grower gets a good return and valuable energy is used as sustainably as possible.
The greenhouse grower obviously has no knowledge of the exact mathematics that BidOptimal has inside, but without the intelligence of CQM the tool would not work. Every day, BidOptimal does only have two hours to carry out all the calculations for more than 100 growers.
With statistical models and deep learning, we predict the relevant data ('predictive analytics'). To achieve good predictions, many different data sources are used in real-time. The optimization model then determines the best solution for almost 30,000 variables within 15,000 boundary conditions, resulting in APX bids ('prescriptive analytics'). A typically data science project at CQM.
Bernard Vroom (Operations Research Group Manager, Air France-KLM)
Short Bio:
Bernard Vroom studied Econometrics and Business Administration in Groningen. After his studies he started to work for KLM. During his career he has been working in different functions in different areas of the company.
Five years ago he joined the central Operations Research Department and started experiments around Big Data and Data Science.
Currently he is group-manager within Air France-KLM leading a team of OR-specialists and Data Scientists.
Title: Benefits of combining Data Science and Operations Research at Air France - KLM
Abstract:
Within Air France-KLM Operations Research plays a very important role to combat competition and to improve internal processes.
Data Science and big data technologies have been introduced firstly for specific use cases in different areas.
Now, we see more and more use cases in which the combination of Operations research and Data Science result in new opportunities.
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