Julian Togelius

Julian Togelius

Associate Professor

Department of Computer Science and Engineering

Tandon School of Engineering, New York University

370 Jay St. Room 610, Brooklyn, NY 11201, USA

Director of the NYU Game Innovation Lab

Guest Professor, University of Skövde

Co-founder, modl.ai

IEEE Fellow

julian@togelius.com

View / Download CV (PDF)

Research Interests

I'm working on artificial intelligence techniques for making computer games more fun, and on games for making artificial intelligence smarter. I ask what AI can do for games, and what games can do for AI.
I want to make computer games adapt to their players through finding out what players want (whether they know it or not) and creating new game levels, challenges or rules that suit the players. I also want to make opponents and collaborators in games more intelligent and believable, research that has applications far outside of computer games.
I currently focus on search-based procedural content generation, which means using evolutionary algorithms to search the space of game content, such as levels, maps or game rules. A key problem is how to evaluate the candidate content - how to create a fitness function for game content.

Current PhD Students

Sam Earle (2020–2026, NYU): Open-ended Learning through Procedural Content Generation
[Google Scholar]
Catalina Jaramillo (2019–2025, NYU, part time): Fairness in AI-assisted Assessment
[Google Scholar]
Graham Todd (2021–2026, NYU): Game Generation through Quality-Diversity and Foundation Models
[Google Scholar]
Zehua Jiang (2022–2026, NYU): Reinforcement Learning for Content Generation
[Google Scholar] [Homepage]
Maria Edwards (2022–2026, NYU, part time): LLMs for Creativity Support
[Google Scholar]
Matthew Siper (2022–2026, NYU): Autoregressive Content Generation with the Path of Destruction
[Google Scholar]
Jesse Lew (2021–2026, NYU, part time): Augmentation Strategies for Computer Vision
[Google Scholar]
Tim Merino (2023–2027, NYU): Generative Models for Game Content
[Google Scholar]
Yuchen Li (2024–2028, NYU): Research areas: Procedural Content Generation (PCG); human-in-the-loop PCG; reinforcement learning for PCG; LLM agents for gameplay and game design.
[Google Scholar] [Homepage]
Debosmita Bhaumik (2022–2025, University of Malta, secondary advisor): Constrained Mixed-Initiative Content Generation
[Google Scholar]
Muhammad Umair Nasir (2022–2026, University of the Witwatersrand, secondary advisor): Practical PCG Through Large Language Models
[Google Scholar]

Past PhD Students

Anubhav Jain (2022–2025, NYU, secondary advisor): Fairness and Controllability in Image Generation
M Charity (2019–2024, NYU): Online Creative Collaborative Content Generation
Ruben Rodriguez Torrado (2017–2022, NYU): Learning Simulation-based Policies
Michael Green (2016–2022, NYU): Tutorial generation in video games
Rodrigo Canaan (2017–2021, NYU, secondary advisor): Collaborative Design Innovation Games
Gabriella Barros (2014–2018, NYU): Data Games
Tiago Machado (2015–2019, NYU): Mixed-initiative game design tools
Andre Mendes (2015–2020, NYU): Multi-stage learning for selection tasks
Philip Bontrager (2015–2020, NYU): Exploring latent space in generative models
Ahmed Khalifa (2015–2020, NYU): General video game level generation
Marco Scirea (2014–2017, ITU Copenhagen): Affective Music Generation and its effect on player experience
Steve Dahlskog (2012–2016, Malmö University): Patterns and procedural content generation in digital games
Christoffer Holmgård (2012–2015, ITU Copenhagen): Player decision modeling with procedural personas
Antonios Liapis (2011–2014, ITU Copenhagen): Mixed-initiative Game Design Automata
Corrado Grappiolo (2010–2014, ITU Copenhagen): Unveiling Collaborative Group Identities in Social Synthetic Environments from Interaction Data
Noor Shaker (2009–2013, ITU Copenhagen): Towards Player-Driven Procedural Content Generation
Tobias Mahlmann (2009–2013, ITU Copenhagen): Modelling and Generating Strategy Game Mechanics
Afsaneh Doryab (2009–2012, ITU Copenhagen, secondary advisor): Context-aware information adaptation in collaborative settings

Publications

Google Scholar