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︎ Info
[2021 - Present]
Speech2Spikes
A real-time pre-processing algorithm that enables speech recognition on neuromorphic processors
(Accenture Labs)
MechSense
A fully multi-material 3D printed sensor for revolute motion
(Accenture Labs)
Fire Probe
An IoT sensor for wildfire detection, mitigation, and risk assessment
(Accenture Labs)

A smart joint sensor for measuring performance and chronic pain
(Work in Progress)
[2020 - 2021]
The Ventilator Project
Affordable ventilators to combat a global pandemic
Brain Switch
Brain-computer interface for ALS patients with real-time machine learning
(MIT Media Lab)
AttentivU
Wireless glasses that measure biopotentials and promote well-being
(MIT Media Lab)
Printed Monitors
3D printed studio monitors with flat, full-range sound and excellent off-axis response

HYDROGEL
Open-source 3D printer for printing fluid materials in hydrogel support
(Work in Progress)
If I Kiss You
Sculptural art from hundred-year old paper player piano scroll

[Pre-2020]
Metamaterial strain gauge for soft robots with high signal-to-noise ratio and orthogonal force rejection
Watchtower Robotics
MIT startup fixing water infrastructure using soft robots
(Techstars, MassChallenge)
CNC Controller
Three axis controller with motion control over Ethernet

About Me {
I'm a computer scientist combining physical sensors with intelligent algorithms to create smart systems that improve people's well-being.

My work involves neuromorphic computing, brain-computer interfaces,
3D printing, & IoT devices.

I'm a researcher in the Future Technologies Group @ Accenture Labs.
}

Mark

[ Brain Switch ]



BS / 2001
Architecture of Brain Switch

The Brain Switch is a complete brain-computer system allowing for real-time correspondence of simple user needs to a caretaker without speaking. This Switch acts as the first step in restoring communication to those with motor function disabilities, creating a solid foundation on which to develop additional technologies. In this architecture, two mobile devices connect to a server, one as the notification device used by the caretaker for remote insights and the other, used by or near the patient.

The patient device streams packets of raw multi-channel electroencephalography data from a consumer grade brain-computer interface back to the server. With this information, a shallow convolutional neural network is trained and used to classify mental states in real-time. Using Brain Switch, the caretaker can be notified remotely, can ask questions, and monitor the well-being of the patient, all without a muscle being moved.

This project utilized a broad spectrum of skills, exploring data science pipelines, app development and server integration. Working with Dr. Nataliya Kosmyna in the Fluid Interfaces group, the initial prototype has been deployed to a patient with ALS and his family to begin testing and data collection. Dataset and paper in progress.


BS / 2002
Reducing Covariate Shift Across Intra-Patient Runs
Mark