Alexandra Maria Proca

Imperial College London, Department of Computing


I’m a PhD student and a President’s scholar in the Department of Computing at Imperial College London, supervised by Pedro Mediano and Murray Shanahan. Broadly, my research interests span the fields of computational/theoretical neuroscience and machine learning/mechanistic interpretability. I’m interested in using the tools of machine learning for developing general theories of learning and cognition to better understand both biological and artificial minds. I’m particularly interested in how information is learned, represented, and processed in neural populations (mixed selectivity, superposition, neural dynamics, learning dynamics) for flexible behavior. My work is grounded in studying simple, interpretable models (such as linear networks and low-rank RNNs) using mathematics, statistical physics, and dynamical systems approaches.

Before attending Imperial, I received my bachelors degree in computer science and neuroscience (with a music minor) from the University of North Carolina at Chapel Hill and then completed a masters degree in machine learning at University College London. During my degrees, I worked as a research assistant in several labs on various topics in the fields of machine learning and neuroscience. After finishing my masters, I worked as a research assistant in the Department of Computer Science at ETH Zürich with João Sacramento, studying the use of hypernetworks for meta-learning. For more information, you can view my CV.

I really enjoy discussing and engaging with science and philosophy with other people. I currently help organize Qualiaheads, a club of graduate students studying the state of research in consciousness science.

Outside of research, I love anything outdoors (marathon running, hiking, skiing, traveling, etc.). I also enjoy playing music and writing. I’ve been playing piano for 18 years and while I lived in Zürich, I was a singer in a local band. I occassionally write poetry and (less frequently) share it.


Jan 2024 I’ll be presenting our work on inferring context in gated linear networks at COSYNE.
Jan 2024 Our paper on discovering modular solutions that generalize compositionally was accepted to ICLR.
Aug 2023 I’ll be attending the 2023 Analytical Connectionism course at the Gatsby Unit.
May 2023 Our paper was accepted to CCN 2023.
Jan 2023 Awarded the Imperial College London President’s PhD Scholarship.
Aug 2022 I gave a talk at the AMCS Modelling Consciousness Cabin Workshop in Dorfgastein, Austria.
Jul 2022 I presented a poster on PID in multitask ANNs at ASSC25 in Amsterdam.
Jul 2022 I gave a talk on meta-learning with hypernetworks at the Sinergia Meeting in Bern, Switzerland.

selected publications

  1. Discovering modular solutions that generalize compositionally
    ICLR, 2024
  2. Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
    arXiv, 2022