New professor Gui Liberali’s inaugural address: machine learning and individuals
Gui Liberali, Endowed Professor of Digital Marketing at Rotterdam School of Management, Erasmus University (RSM) and ERIM Fellow will deliver his inaugural address Learning with a purpose: The balancing acts of machine learning and individuals in the digital society on Friday, 25 May 2018. He will argue that the sheer scale and velocity of online experiments allow firms to learn and apply valuable knowledge rapidly to develop better consumer services and products on an unprecedented scale, but there are challenges. Advances in machine learning and reinforcement learning are providing ways to tackle some of these challenges.
The internet transformed some of the most basic processes in society, such as trade, payment, and communication. There is more access to products, services, and opinions than ever, but at the same time, human behaviour is tracked more closely than ever. For example, large online retailers offer hundreds of thousands of products, and can readily observe in great detail how each consumer interacts with any of them. They can also rapidly deploy in-vivo, randomized online experiments at the individual level but on a population scale to test concepts, insights and communication approaches which can lead to better services and products. However, there are often billions of possibilities, such as product-consumer combinations for product recommendations. The scale and complexity of these experiments create amazing challenges.
Thus, firms face balancing acts. For example, they need to constantly choose between profiting from what they already know about consumers (such as the genres of movies already watched) and learning more about the same consumers (such as by recommending a movie from an untested genre). Consumers are also facing their own balancing acts. In the digital society, people inevitably leave digital footprints but have some discretion in terms of how much information we want to keep private. Typically, a consumer who is more open to sharing their preferences is also exposed to higher risks, but at the same time they can get better access to the products and services they need.
In his inaugural address, Professor Liberali will show how advances in machine learning and reinforcement learning can alleviate these challenging balancing acts. After providing some background information, he will briefly describe how these methods are helping firms and consumers, illustrating with his own work. Then he will indicate key exciting areas for future research. He will conclude this address by illustrating the implications for marketing science and prescriptive analytics more generally.
Professor Gui Liberali’s inaugural address is open to the public, and will take place on Friday, 25 May 2018. The ceremony will start at 16:00 in the Auditorium of the Erasmus Building on Woudestein campus, Burgemeester Oudlaan 50, Rotterdam and will be followed by a reception.
Professor Liberali’s address will be preceded by a two-day Workshop on Multi-Armed Bandits and Learning Algorithms. Participants from all around the world actively working in the development and application of multi-armed bandits and learning algorithms in various disciplines will attend. This forum will particularly encourage discussions of the approaches that have evolved in computer science, management science, operations research and statistics.
Gui Liberali is an Endowed Professor of Digital Marketing at RSM. He holds a doctorate in marketing and a BSc in computer science. His research interests include optimal learning, multi-armed bandits, digital experimentation, natural language processing, morphing theory and applications (e.g., website morphing, advertisement morphing), dynamic programming, machine learning, and product line optimisation. His work has appeared in journals such as Marketing Science, Management Science, International Journal of Marketing Research, Sloan Management Review, and European Journal of Operational Research. He is Vice-President for Membership of the INFORMS Society for Marketing Science (ISMS) for the 2018-2019 term.
He is the founder and director of the Erasmus Centre for Optimization of Digital Experiments (eCode), with the goal of developing and disseminating machine learning and optimisation methods, concepts, and tools that help firms improve the way they use the internet to connect with and interact with their consumers. This includes new ways to design and optimise digital experiments, new multi-armed bandits algorithms (such as website morphing), randomised controlled trials of marketing methods and tools (A/B experiments), online field experiments, and behavioural analytics.