Invited speakers

Julian Togelius is an associate professor of Computer Science and Engineering at the New York University NYU. He is interested in most topics in the intersection between computational intelligence and games and currently focuses on search-based procedural content generation. His work includes: creating theoretically founded and/or data driven models of player experience, by simulated gameplay using heuristic agents augmented by behavioral models of human players, and by measuring the performance of learning algorithms on candidate game rules; the adoption of evolutionary reinforcement learning schemes in digital game domains, exploring techniques such as incremental evolution, competitive co-evolution and the hybridization of evolution with ontogenetic RL algorithms; developing fair and reliable benchmarking of RL algorithms and game AI in general takes by inventing and running competitions based on digital action games, such as car racing and Super Mario Bros; player modeling through preference learning, and on data mining on large sets of game data. His per reviewed publication record includes 2 books, 31 journal articles, 130 conference/symposium papers, 6 book chapters and 32 workshop papers, with an h-index of 78 on google scholar.

Matthew Guzdial is the head of the GRAIL Lab at the Department of Computing Science and the Alberta Machine Intelligence Institute at the University of Alberta. Here he currently undertakes research in three primary research areas: the application of artificial intelligence (AI) and machine learning (ML) to the generation of content, with a focus on the domain of video games, known as Procedural Content Generation: the application of AI and ML to better understanding users, players, and designers, known as User Modeling; and the application of concepts from computational creativity to core AI and ML problems, particularly Transfer Learning and Explainable AI.

Matthew Varghese is currently a Machine Learning Research Engineer at  Activision / Microsoft Xbox (Redmond, USA), where he is working to apply cutting edge machine learning techniques (including time series analysis, deep learning, anomaly detection, computer vision and reinforcement learning) to videogames. His recent research activity includes the development of a novel CV-based approach that detects cheaters in online games, based purely on video feeds.