Talks and presentations

Brain-Inspired Spiking Neural Networks for Online Learning

April 01, 2024

Talk, Georgia Tech ECE Research Rally, Georgia Institute of Technology, Atlanta, Georgia

This talk focused on giving an motivation for brain-inspired computing and neuromorphic computing to undergraduate students and discuss the capabilities of Spiking Neural Networks (SNNs) for advancing online learning mechanisms. The discussion included the temporal dynamics and computational advantages of SNNs, aiming to illustrate how these biological-inspired systems can lead to more efficient and robust artificial intelligence technologies.

Evolution Strategies in Heterogenous Recurrent Spiking Neural Network for Dynamical Control

February 01, 2024

Talk, Georgia Tech DCL Symposium Spotlight Talk, Georgia Institute of Technology, Atlanta, Georgia

The spotlight talk presented at the Georgia Tech DCL Symposium revolved around the application of evolution strategies in heterogeneous recurrent spiking neural networks. The aim was to explore the potentials of these networks in dynamical control systems, emphasizing the integration of heterogeneity in neuronal and synaptic dynamics to enhance learning and adaptation in complex environments.

Dynamics in Diversity: Harnessing Heterogeneity in Neuronal and Synaptic Dynamics in SNNs

January 01, 2023

Talk, SNUFA: Invited Talk and Poster, SNUFA

This invited talk and poster presentation at the SNUFA conference delved into the diverse dynamics of neuronal and synaptic activities within Spiking Neural Networks (SNNs). It highlighted the importance of harnessing this heterogeneity for the development of more efficient and capable neural systems, presenting recent findings and methodologies in the field.

Learning to Predict Using Network of Spiking Neurons

January 01, 2023

Talk, ICERM Workshop on Mathematical and Scientific Machine Learning, ICERM, Brown University, Rhode Island

Presented at the ICERM Workshop, this talk explored the novel approaches of using networks of spiking neurons for predictive modeling. It covered the theoretical underpinnings as well as practical applications, demonstrating how spiking neural networks can offer significant advantages in processing speed and efficiency for machine learning tasks.

Phase-Sensitive Common Spatial Pattern for EEG Classification

October 01, 2019

Talk, IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE SMC, Location TBD

This talk at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) presented a novel approach to EEG signal classification using a phase-sensitive common spatial pattern. The methodology enhances the ability to differentiate between various mental states, offering improved performance in brain-computer interface applications. The discussion included results from recent studies and potential implications for future research.