Portrait de Katja Hofmann

Katja Hofmann

Senior Principal Researcher

À propos

I am a Senior Principal Researcher at Microsoft Research (opens in new tab) (based in Cambridge, UK) where I lead the Game Intelligence (opens in new tab) team that focuses on machine learning research with a focus on video games. Our north star is to unlock genuine human-AI collaboration by driving deep research insights and demonstrating new AI capabilities in data-rich game environments. My long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems.

My team and I are part of LEAP (opens in new tab) (Language, Learning, Audio and Privacy). We actively collaborate with teams across Microsoft Research and other parts of Microsoft.

I served as Senior Program Chair (ICLR 2021) and General Chair (ICLR 2022) of the International Conference on Learning Representations (ICLR (opens in new tab)) and continue to serve on the conference board, since 2024 as Secretary of the board.

As part of the Microsoft Research PhD Scholarship program, I have deeply enjoyed co-supervising, and successfully graduating, the following PhD students:

Before joining Microsoft Research, I completed my PhD in Computer Science as part of the former ILPS group at the University of Amsterdam (opens in new tab). I worked with Maarten de Rijke (opens in new tab) and Shimon Whiteson (opens in new tab) on smart search engines that learn directly from their users. For a list of my publications before joining MSR, please see the ILPS (Information and Language Processing Systems) list of publications (opens in new tab), MSR Academic (opens in new tab), or dblp (opens in new tab).

Contenu en vedette

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. We address…

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense…

Strategically Efficient Exploration in Competitive Multiagent Reinforcement Learning

High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems. A large body of work has demonstrated that exploration mechanisms based on the principle of optimism under uncertainty can significantly improve the sample…

Designer-centered reinforcement learning

Designer-centered reinforcement learning

In video games, nonplayer characters, bots, and other game agents help bring a digital world and its story to life. They can help make the mission of saving humanity feel urgent, transform every turn of a corner into a gamer’s…

Malmo, Minecraft and machine learning with Dr. Katja Hofmann

Malmo, Minecraft and machine learning with Dr. Katja Hofmann

Episode 39, August 29, 2018 - Dr. Hofmann talks about her vision of a future where machines learn to collaborate with people and empower them to help solve complex, real-world problems. She also shares the story of how her early years in East Germany, behind the Iron Curtain, shaped her both personally and professionally, and ultimately facilitated a creative, exploratory mindset about computing that informs her work to this day.

Teacher Algorithms for Deep Reinforcement Learning Students | JRC Workshop 2021

Teacher Algorithms for Deep Reinforcement Learning Students | JRC Workshop 2021

Artificial Intelligence (AI) 20 May 2021 Speaker: Rémy Portelas, INRIA (collaboration with Pierre-Yves Oudeyer, INRIA and Katja Hofmann, Microsoft) This virtual event brought together the PhD students and postdocs working on collaborative research engagements with Microsoft via the Swiss Joint…

Optimistic Actor Critic avoids the pitfalls of greedy exploration in reinforcement learning

Optimistic Actor Critic avoids the pitfalls of greedy exploration in reinforcement learning

One of the core directions of Project Malmo is to develop AI capable of rich interactions. Whether that means learning new skills to apply to challenging problems, understanding complex environments, or knowing when to enlist the help of humans, reinforcement…

The road less traveled: With Successor Uncertainties, RL agents become better informed explorers

The road less traveled: With Successor Uncertainties, RL agents become better informed explorers

Imagine moving to a new city. You want to get from your new home to your new job. Unfamiliar with the area, you ask your co-workers for the best route, and as far as you can tell ... they’re right!…

Project Malmo competition returns with student organizers and a new mission: To democratize reinforcement learning

Project Malmo competition returns with student organizers and a new mission: To democratize reinforcement learning

When I was asked about my favorite movie in a game with friends after my wedding ceremony, I replied Star Wars. That was about two decades ago, and, yes, it’s still the case. I especially like Return of the Jedi.…

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) competition is a new challenge that…