Biography
I have ten years of experience developing end-to-end solutions at the intersection of IoT, mixed reality, and AI. I hold a PhD in computer science from UC Berkeley, where I focused on smart buildings. I also completed a postdoc on capturing and visualizing wireless network traffic. I’m interested in distributing computation across cloud-edge architectures, particularly to support IoT and networking applications.
9/12/22: I’m excited to announce that I have joined the Innovation Team at Resideo Technologies as an IoT Systems Research Scientist!
Download my resumé.
Interests
- Network monitoring
- Sensor networks/IoT
- Microservice architectures
- Observability
Education
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PhD in Computer Science, 2021
University of California, Berkeley
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MS in Computer Science and Engineering, 2017
University of Michigan
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BS in Computer Science, 2011
George Mason University
Projects
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Mixed Reality Network Introspection
I developed a network introspection system and mixed reality visualization for viewing communication in sensor-gateway-cloud IoT architectures. Technologies included OpenThread, WiFi, ultra-wideband localization, Unity, AWS, MQTT, Python, Golang, C#. Currently incorporating spectrum sensing.
Vehicular Network Visualization
In a collaboration with IBM Research, I streamed network capture, telemetry, and camera feeds from a self-driving car simulator into Unity to visualize autonomous vehicle communication during collaborative perception tasks.
Augmented Reality IoT App
I designed and built an augmented reality Android app to help users discover smart home capabilities and have successful interactions with intelligent assistants in unfamiliar smart spaces. I also led user research and evaluations.
Open Source IoT Library
I wrote and maintain lifxlan, an open source Python library for communicating with LIFX brand WiFi smart bulbs over a local network using the UDP LAN protocol. 450+ stars on Github.
Power Meter Analytics
I used machine learning to calibrate readings from energy harvesting power meters.
Smart Home Telepresence
I prototyped a smart home telepresence application that uses technology present in smart homes today. I developed a lightweight microservice runtime that allowed the same application code to run in smart homes with different device brands and configurations.
Imitation Learning for Smart Lighting Control
I implemented an LSTM deep learning recurrent neural network (RNN) using the Python Keras framework. I trained the system to anticipate when home residents will turn on lights and do it for them automatically.
Design and Fabrication
I designed and fabricated a CubeSat-themed mood light powered by Arduino. This required PCB design and fabrication, aluminum machining (vertical mill, bandsaw), and laser cutting.
CS Outreach Program
I founded the CS Kickstart summer program at University of Michigan to increase recruitment and retention of women in computer science. I assembled and led a five-person leadership team that built the program from the ground up. Our program piloted interventions that were eventually rolled into the introductory CS curriculum.
Publications
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(2022).
ARticulate: One-Shot Interactions with Intelligent Assistants in Unfamiliar Smart Spaces Using Augmented Reality.
IMWUT.
(2021).
One-Shot Interactions with Intelligent Assistants in Unfamiliar Smart Spaces.
EECS Dept., UC Berkeley.
(2021).
XRShark: Mixed Reality Network Introspection.
White Paper.
(2018).
Bluetooth Low Energy in the Wild Dataset.
DATA ‘18.
(2018).
The Big House Dataset: Desired Applications and Interactions.
DATA ‘18.
(2018).
Experience: Android Resists Liberation from Its Primary Use Case.
MobiCom ‘18.
(2017).
Devices and Data and Agents, Oh My: How Smart Home Abstractions Prime End-User Mental Models.
IMWUT.
(2016).
Perpetual Sensing for the Built Environment.
IEEE Pervasive Computing.
(2015).
The Haunted House: Networking Smart Homes to Enable Casual Long-Distance Social Interactions.
IoT-App ‘15.
(2014).
Deltaflow: Submetering by Synthesizing Uncalibrated Pulse Sensor Streams.
ACM e-Energy ‘14.