Guest Speakers
Guest speakers lead the discussion groups and facilitate the projects. Further speakers for ALICE 2027 will be announced later.
Emily Dolson
Emily Dolson is Assistant Professor at Michigan State University, with appointments in Computer Science & Engineering and in Ecology, Evolution & Behavior, where she leads the ECODE Lab (Evolutionary Control of Digital Ecologies). The lab works on how to control the direction evolution takes in an ecological community, a problem that shows up in cancer, in microbiomes, and inside evolutionary algorithms. They come at it through artificial life, building digital systems that evolve on the same principles as biological ones and open-source tools that record every step of that evolution. She serves on the board of the International Society for Artificial Life, which named her its Distinguished Early-Career Investigator in 2025 and gave her its Exceptional Service Award in 2019.
Christian Guckelsberger
Christian Guckelsberger is a computer scientist, art historian, and Assistant Professor in Creative Technologies at Aalto University, where he leads the Autotelic Interaction Research group and holds an Academy Research Fellowship from the Research Council of Finland. His goal is to build artificial systems that are creative in their own right and in interaction with people. Because intrinsic motivation is central to creativity in humans and other animals, he argues it is a key to creativity in machines too. He builds psychologically plausible models of it, with a focus on multi-agent interaction, and applies them in videogames, fine art, and design. In games, creativity runs in both directions, in how people play and in how games are made. His work bridges artificial intelligence, human-computer interaction, psychology, philosophy, and creative practice.
Arend Hintze
Arend Hintze is Professor of Microdata Analysis at Dalarna University, where he runs the Hintzelab. By evolving digital agents rather than engineering them, the lab studies where intelligence comes from: which environments and selective pressures produce it. He helped design Markov Brains, evolvable networks of individual computational components instead of uniform layers. Information theory then lets him look inside the evolved brains, tracing how they move information around and how they differ from networks trained by backpropagation. Lately he has turned to generative AI, asking how large language models make decisions and what happens when such systems interact with one another. His paper Integrated Information Increases with Fitness in the Evolution of Animats was named the International Society for Artificial Life's Outstanding Publication of the Decade in 2025.
Julian Togelius
Julian Togelius is Full Professor of Computer Science and Engineering at New York University, where he co-directs the Game Innovation Lab, and incoming Professor of AI at Chalmers University of Technology. His research spans creativity, video games, and open-endedness: how AI systems can design their own goals and learn surprising skills, how AI can help people be more creative, and how games can be used to test and improve AI models. He has introduced methods for procedural content generation, human-like game playing, mixed-initiative design, and adapting games to models of player experience. His algorithmic work is mostly in evolutionary computation, especially quality-diversity search, and many of his main contributions have been new research problems and game-based benchmarks.