About me

I’m a PhD student in the Zenke Lab at the Friedrich Miescher Institute in Basel, Switzerland. I work on understanding the mathematical principles behind predictive self-supervised learning in order to see if and how the brain could be learning in a similar way.

In the past, I studied Electrical Engineering at the Indian Institute of Technology in Madras, and Robotics at the École polytechnique fédérale de Lausanne. I also spent a fun half year interning with the Robotics AI team in Amazon, Berlin.

Publications

An eigenspace view reveals how predictor networks and stopgrads provide implicit variance regularization
Halvagal, M. S.*, Laborieux, A.*, & Zenke, F.
NeurIPS 2022 Workshop on Self-Supervised Learning
[Paper]

The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks
Halvagal, M. S., & Zenke, F.
bioRxiv, accepted in Nature Neuroscience
[Paper] [Code]

Inferring subjective preferences on robot trajectories using EEG signals
Iwane, F., Halvagal, M. S., Iturrate, I., Batzianoulis, I., Chavarriaga, R., Billard, A., & Millán, J. D. R.
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
[Paper]

Talks and Poster Presentations

• Presented a poster at the Self-Supervised Learning Workshop at NeurIPS 2022
• Presented a poster at Bernstein 2022
• Presented a poster at Cosyne 2022
• Gave an online talk in the SNUFA Seminar Series