Welcome!

to the personal website of:

Matthew Farrugia-Roberts
Research Assistant (AI Alignment and Reward Hacking)
Krueger AI Safety Lab

Nouns: Matthew, Matt, he/him, they/them (singular)—all fine.

Contact: ‘matthew’ at this domain.

Website perpetually under construction. This page includes my bio, announcements, research interests, publications, teaching, coursework, and affiliations.

About me

I am a student, researcher, and teacher from Melbourne, Australia. I’m working on understanding goal misgeneralisation with Usman Anwar at Krueger AI Safety Lab. I also lead developmental interpretability research at Timaeus, and I help run a virtual AI safety reading group at metauni.

Previously, I completed a Master of Computer Science degree at the University of Melbourne, with a thesis on lossless compression of neural networks, supervised by Daniel Murfet. During the degree I completed a virtual research internship at the Center for Human-compatible AI, studying reward learning theory with Adam Gleave and Joar Skalse. I also completed an exchange semester at ETH Zürich.

Before that, I worked as a tutor and lecturer at the University of Melbourne, for classes on programming, algorithmics, artificial intelligence, theoretical computer science, networks, and operating systems. I also completed a Bachelor of Science (taking these classes and others) shortly beforehand.

Announcements

Coming soon:

I’m giving a guest lecture on ethics and the future of AI at the University of Melbourne in late May (time/date TBC).
I’m planning Hi, JAX!, a free online introductory JAX course for June/July/August.
I am seeking opportunities for doctoral study in the UK or Europe. So far I applied to the ETH AI Center, Oxford, and Cambridge.

Research interests

Broad research interests:

Intelligence, learning, and computation (e.g., agent foundations, bounded/computational rationality, artificial intelligence, cognitive science)
Technology and society (e.g., existential risks from advanced intelligent systems, political philosophy, history and future of humanity)

So far, I’m still a student on these topics, with much to learn.

While I’m establishing myself as an academic, I have focussed on some narrower topics:

AI alignment (reward learning theory, goal misgeneralisation, developmental interpretability)
Deep learning theory (neural network geometry, singular learning theory)
Computer science education (discrete mathematics, theoretical computer science)

Publications by topic

Developmental interpretability:

Jesse Hoogland(=), George Wang(=), Matthew Farrugia-Roberts, Liam Carroll, Susan Wei, and Daniel Murfet, 2024, “The developmental landscape of in-context learning”. Conference paper under review. Preprint on arXiv.

Neural network geometry:

Matthew Farrugia-Roberts, 2023, “Computational complexity of detecting proximity to losslessly compressible neural network parameters”. Conference paper under review. Preprint on arXiv.
Matthew Farrugia-Roberts, 2023, “Functional equivalence and path connectivity of reducible hyperbolic tangent networks”. Conference paper (poster) presented at NeurIPS 2023. Preprint on arXiv.
Matthew Farrugia-Roberts, 2022, Structural Degeneracy in Neural Networks, Master’s thesis, School of Computing and Information Systems, the University of Melbourne. Available online.

Reward learning theory:

Joar Skalse(=), Matthew Farrugia-Roberts(=), Alessandro Abate, Stuart Russell, and Adam Gleave, 2023, “Invariance in policy optimisation and partial identifiability in reward learning”. Conference paper (poster) presented at ICML 2023. Preprint on arXiv.

Computer science education:

Matthew Farrugia-Roberts, Bryn Jeffries, and Harald Søndergaard, 2022, “Teaching simple constructive proofs with Haskell programs”. Conference paper: extended abstract presented at TFPIE 2022, full paper published in EPTCS.
Matthew Farrugia-Roberts, Bryn Jeffries, and Harald Søndergaard, 2022, “Programming to learn: Logic and computation from a programming perspective”. Conference paper presented at ACM ITiCSE 2022.

Teaching

Teaching in 2024:

COMP90087 The Ethics of Artificial Intelligence (TA, guest lecture on AI safety)

Teaching in 2023:

COMP90087 The Ethics of Artificial Intelligence (TA)

Teaching in 2021:

COMP30024 Artificial Intelligence (co-Head TA)
COMP30026 Models of Computation (co-Head TA)
COMP90087 The Ethics of Artificial Intelligence (TA)

Teaching since 2016:

COMP90087 The Ethics of Artificial Intelligence (2021, 2023: TA)
COMP30026 Models of Computation (2016: TA, 2017–2020: Head TA, 2021: co-Head TA)
COMP30024 Artificial Intelligence (2017–2019: Head TA, 2020–2021: co-Head TA)
COMP90059 Introduction to Python Programming (2018: Lecturer and coordinator)
COMP20007 Design of Algorithms (2016: TA, 2017: Head TA, 2018: Coordinator)
COMP10001 Foundations of Computing (2017: TA)
COMP30023 Computer Systems (2017: TA)
COMP90038 Algorithms and Complexity (2016: TA)

Coursework

Master of Computer Science, University of Melbourne, part-time 2019–2022

Coursework in theoretical computer science and machine learning (coursework portfolio)
- Coursework average mark 98.75%
Minor thesis project on structural degeneracy in neural networks
- Thesis mark 95.5% (top of year)
Overall weighted average mark 96.25% (top of year)
Dean’s Honours List (top 5 percentile marks across Faculty of Engineering and Information Technology)

Exchange semester, ETH Zürich, 2020

Coursework on theoretical computer science, statistical learning theory, network modelling, and neuroscience (coursework portfolio)
- Grade-point average 5.92 / 6.00

Bachelor of Science, University of Melbourne, 2014–2016

Major in Computing and Software Systems (coursework in computer science and software engineering) plus electives in physics, mathematics, and education
- Average mark 93.04%
Dean’s Honours List (top percentile marks in Science faculty, all three years)
AAII Prize in Computer Science (top of class in AI subject—actually I achieved top-of-class marks fairly regularly but this time it came with an industry award)
ACS Student Award (best marks across third year computer science classes)

Affiliations

Current affiliations:

Research assistant at Krueger AI Safety Lab.
Research lead at Timaeus.

Past affiliations:

Research assistant at the Computational and Biological Learning Lab, University of Cambridge.
Visiting research associate at the Melbourne Deep Learning Group.
Independent AI safety researcher, supported by Manifund grant “Introductory Resources for Singular Learning Theory”.
Research assistant at the School of Computing and Information Systems, the University of Melbourne.
Teaching assistant at the Centre for AI and Digital Ethics and the School of Computing and Information Systems, the University of Melbourne.
Master of Computer Science student at the Melbourne Deep Learning Group and the School of Computing and Information Systems, the University of Melbourne.
Virtual research intern at the Centre for Human-compatible AI, University of California, Berkeley.
Virtual research intern at the (then-named) Brain, Mind & Markets Laboratory, the University of Melbourne.
Casual tutor and lecturer at the School of Computing and Information Systems, the University of Melbourne.

Any views expressed on this website are not intended to represent the views of any of my affiliated institutions.