- Rewrote Sinabs to make it more performant (SNN training framework for Speck)
- My library Tonic adopted by SynSense: dataset management and data wrangling
- Managing the production pipeline for vision team
- Establish the use of good practices to make training and deployment faster
- Training SNNs for better performance through data augmentation
- Architectural exploration such as recurrence in the network, spike frequency adaptation
- How to save energy, fewer spikes is the goal
- Training Spiking Neural Networks Using Lessons from Deep Learning: Eshragian, Neftci, Wang, Lenz
- Adversarial Attacks on Spiking Convolutional Networks: Büchel, Lenz, Sheik, Sorbaro
- EXODUS: Stable and Efficient Training of Spiking Neural Networks: Bauer, Lenz, Haghighatshoar, Sheik
- under submission: Ultra-low-power image classification on neuromorphic hardware
- Converting an ANN to an SNN using temporal coding and a single spike per neuron
- A method to normalise ANN activation which scales to deeper layers
- CapoCaccia workshop: presented our chips, gave tutorials and demos
- AMLD: a full-day workshop with our hardware, outreach to new students
- Open Neuromorphic: Platform for neuromorphic open source code and hardware
- NeuroBench: benchmarking and metrics for neuromorphic computing
- Leading role in the algorithms vision team
- Strong engineering background
- 3 papers published, 1 under submission
- 2 patents submitted
- Involved in numerous community efforts
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