Ian Gemp
Staff Research Scientist
Google DeepMind
London UK
imgemp at google dot com
I am a Staff Research Scientist working at Google DeepMind (GDM) in London on the Game Theory team. My work focuses on developing equilibrium algorithms (the hammer) and novel game-theoretic formulations of learning problems (the nail). My conference publications have been recognized with awards in areas of eigendecomposition, equilibrium computation, and social choice theory. My research at the intersection of LLMs and game theory has appeared in Wired and Quanta magazine.
If you're interested in the general evaluation of AI agents. He are two tutorials I gave with Manfred Diaz, Marc Lancot, and Kate Larson on the topic at AAMAS (link) and SAGT (link) 2025.
Selecting the "best" option.
Consider the case where "best" means lowest altitude - find the longitude and latitude of the location with lowest altitude on a contour map.
A point of balance between competing forces.
This encompasses topics such as predator-prey models in biological systems, supply and demand in microeconomics, and even optimization (think about the force of gravity pulling a ball to the point of lowest altitude in a region).
Learning a strategy to maximize a reward.
Imagine teaching a dog a trick or task by feeding it a treat each time it succeeds. Eventually, the dog learns what it needs to do to earn the treat. This area of machine learning draws inspiration from both biology and psychology.
Multiple agents and their environment.
This includes any situation where multiple humans, robots, etc are engaged in a scenario, whether it be competitive, cooperative, or neither so long as their actions affect each other either directly or through the shared environment. It's nearly impossible to think of a situation in real life where this is not the case. As an example, consider two people competing in a game of rock-paper-scissors.
Reducing the number of dimensions.
Dimensionality refers to the number of dimensions, features, attributes, etc (think columns on an excel sheet), so dimensionality reduction just means reducing the number of columns such that you still have enough information on the spreadsheet to complete your task but hopefully much faster now that there's less data to process.
Education
MS, PhD (CS) Univ of Massachusetts
Amherst MA 2016, 2018
MS (ESAM) Northwestern Univ
Evanston IL 2011
BS (ME/ESAM) Northwestern Univ
Evanston IL 2010
High School Kinkaid
Houston TX 2006
Experience
Google DeepMind
Research Scientist 2019 - Present
UMass Amherst
Amherst MA
Research Assistant 2013 - 2018
AWS AI Algorithms
New York NY
Applied Scientist Intern Summer 2018
Adobe Research
San Jose CA Data Science Intern Summer+Fall 2016
Capgemini SA&I
Chicago IL
Consultant 2011-2013
Northwestern Univ
Evanston IL
Research Assistant 2007-2010