Neuromorphic AI Research
Doctoral Candidate in Electrical Engineering at Eindhoven University of Technology, working on hardware-software co-design for spiking and artificial neural networks in physical devices, with a focus on efficient on-device continual learning.
Doctoral Candidate, Electrical Engineering, Electronic Systems at TU/e, affiliated with the Neuromorphic Edge Computing Systems Lab.
MSc in Electrical Engineering with a microelectronics specialization from Delft University of Technology (awarded September 29, 2023), following a BEng in Electrical Engineering with a VLSI/electronics specialization from Concordia University (awarded December 8, 2020).
Research centers on neuromorphic technologies, forward-only learning, spiking neural networks, and physical AI systems that can learn efficiently at the edge.
Experience spans board design in KiCad, digital design in Verilog, embedded programming in C/C++, analog design in Cadence, and deployment of artificial and spiking neural networks in PyTorch and Python.
Investigating biologically inspired learning rules and active dendrites to reduce catastrophic forgetting in time-to-first-spike neural networks.
Developing memory-efficient and scalable alternatives to backpropagation through time for training spiking neural networks on dynamic signals.
Exploring optical correlators and hardware-aware training pipelines that combine neuromorphic principles with physical photonic systems.
Traces propagation: memory-efficient and scalable forward-only learning in spiking neural networks, Neuromorphic Computing and Engineering.
Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs, presented at OFC 2025.
Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks, IEEE AICAS 2024.
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Background includes internships in embedded hardware design and a research internship at IBM Zurich, alongside work spanning neuromorphic AI, embedded systems, and microelectronics.