Bryan GassSenior ML research scientist / Boston

Senior ML research scientist / Boston, Massachusetts

Bryan Gass

I build learning systems and think about memory, agency, and control.

I’m a senior machine learning research scientist working where learning systems meet information theory and dynamical systems. Most of my questions begin with a simple one: what lets an intelligent system carry experience forward?

Painted portrait of Bryan Gass

Selected work

A longer argument

Law Does Not Return a Boolean

Fidryn is a proposed programming language for legal instruments: precise where law is mechanical, explicit where judgment enters, and incapable of hiding authority, discretion, or ambiguity inside a Boolean.

Read the essay →

In progress

Questions, notes, and the route here.

Current questions

A few problems I keep returning to.

  1. 01

    Long-context learning

    I am interested in models that can carry useful experience across very long horizons without making memory prohibitively expensive.

  2. 02

    Continual agency

    What would it take for an agent to keep learning after deployment—to accumulate skills, revise its world model, and remain useful over time?

  3. 03

    Control and alignment

    I use ideas from control and information theory to ask how intelligent systems gain influence, and how they might do so without diminishing ours.