Computational Neuroscience | Data Science | Machine Learning | Visual psychophysics

Computational neuroscientist with 11+ years of experience including signal processing, machine learning, visual psychophysics, and experimental design. I have worked on motion capture (Microsoft’s Xbox Kinect); electrophysiology, computational modeling, and psychophysics (NYU PhD); and on multi-modal neural interfaces and ML in wearble devices (Wispr).

Through roles in neuroscience, software, and human-computer interfaces, I’ve developed problem-solving expertise via hands-on research experiences - reach out if you want to talk about relevant opportunities!

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Industry projects

Neural interfaces wth Wispr

Computational Neuroscientist, collaborating with ML, hardware, and data collection teams

Approaches: Experimental design, signals processing, data & pre-processing pipelines, machine learning

I owned a variety of projects that shaped the product roadmap and related prototypes for a wearable neural interface. I analyzed multi-modal time-series data (EMG, IMU, video, audio), built data pipelines (computer vision, signal processing), and conducted model feature exploration.

Motion Capture for Microsoft’s Xbox One Kinect

Software Engineering Intern

A cutting-edge device for human-computer interaction, the Kinect allowed user motion to be used as input to the Xbox One. I worked to create an automated pipeline for high-quality, labeled data by parsing motion capture data for use by the Kinect’s machine learning models which could replace a labor-intensive, manual process.

Academic projects

Spatial frequency selectivity in thalamus and primary visual cortex

With Tony Movshon, Eero Simoncelli

Approach: Physiology, modeling, psychophysics

Aim: Selectivity for the spatial scale of images begins in the earliest stages of vision, but undergoes a transformation towards sharper and more varied selectivity in visual cortex. We manipulate the contrast and spatial frequency content of images to probe the differential representations in the thalamus and primary visual cortex, and explore their underlying mechanisms and computation. Working with electrophysiological data and computational models, we have uncovered interesting properties of contrast gain control and the hierarcical processing of stimulus selectivity.

Saccadic Suppression

With Roozbeh Kiani, Marc Zirnsak

Approach: Human psychophysics

Aim: Visual perception is altered during saccadic eye movements. We used a detection task to densely sample changes in achromatic luminance sensitivity around the time of saccades. This has enabled us to better understand the spatiotemporal profile of saccadic suppression, uncovering important asymmetries with respect to gaze.

Publications

For any of the below, contact me for a copy of the poster and/or abstract.

Conference/Journal	Title
Manuscript in prep (2024)	Asymmetric Saccadic Suppression
Manuscript in prep (2024)	Contrast-dependent spatial frequency selectivity in macaque LGN and V1 neurons
PhD thesis, 2023	Spatial frequency selectivity in macaque LGN and V1
Vision Sciences Society, 2020	Differing mechanisms for contrast-dependent spatial frequency selectivty in macaque LGN and V1 neurons
Vision Sciences Society, 2019	Contrast-dependent spatial frequency selectivity in macaque V1 neurons explained with tuned contrast gain control
Cognitive Computational Neuroscience, 2017	Asymmetric Saccadic Suppression: Preserved Luminance Sensitivity along the Saccade Trajectory
Society for Neuroscience, 2016	Effects of contrast and spectral dispersion on spatial frequency tuning in V1

Education

Institution	Focus	Years	Degree
New York University	Neural Science	2015-2023	PhD
University of Miami	Physics, Math	2011-2015	BS