My main expertise is in EEG signal processing and diffuse optics (fNIRS, pulse oximetry, DOT, DCS, etc) with neuroscientific applications. More generally, I am a practitioner theorist interested in developing novel techniques for “accurate” and “efficient” non-invasive neuroengineering, specifically two topics: 1) multimodal neuroimaging and 2) neurostimulation. I’m interested in advancements in, e.g.,

• Algorithms for multimodal data fusion: how to combine information from different modalities for better inference
• Optimal sensor placement: where should one place the electrodes/optodes for the most accurate sensing/stimulation, given a region of interest
• Deep learning techniques in neuroimaging: how can we make the most out of undersampled and/or noisy data, where conventional methods can have poor performance
• Theoretical limits of non-invasive neuroengineering: what is the best resolution one can achieve when using, e.g., DOT and tES
• Mathematical modeling of biophysics: neuronal response to stimulation, photon propagation in tissue, etc

Utilizing the techniques optimized for both accuracy and efficiency, ultimately, I would like to enable portable monitoring and intervention that can be used for, e.g., rehabilitation, prognosis prediction, degenerative diseases, etc.