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Software Engineer at Google
Passionate deep learning engineer and researcher with highly developed communication and presentation abilities, honed over more than a decade of experience in industry and academia. Major projects include (see projects section for more detail): Deep Learning for Object Detection in Radar Multi-Sensor Data Collection and Visualization Automated Ground Truth Label Generation Other projects include: GAN based radar data synthesis/augmentation, reinforcement learning for intelligent radar scanning, short range radar gesture recognition using PCA/SVM, and CNN-based radar super-resolution.
Colorado State University
Doctor of Philosophy (Ph.D.), Theoretical and Mathematical Physics
January 1, 2012 – January 1, 2017
Colorado State University
Master’s Degree, Theoretical and Mathematical Physics
January 1, 2012 – January 1, 2014
University of Oregon
Bachelor’s Degree, Theoretical and Mathematical Physics
January 1, 2008 – January 1, 2012
Senior Software Engineer
April 1, 2023 – Present
Mountain View, California, United States
Software Engineer
June 1, 2020 – April 1, 2023
Mountain View, California, United States
Metawave Corporation
Director of AI
January 1, 2020 – June 1, 2020
Palo Alto CA
Metawave Corporation
Head of AI
September 1, 2018 – January 1, 2020
Palo Alto CA
Metawave Corporation
AI Engineer
November 1, 2017 – September 1, 2018
Palo Alto CA
Colorado State University
Graduate Research Assistant
May 1, 2014 – May 1, 2017
Colorado State University
Graduate Teaching Assistant
August 1, 2012 – May 1, 2014
Annual Giving Program
Student Fundraiser
August 1, 2010 – August 1, 2011
Eugene, Oregon
Automated Ground Truth Label Generation — C++, Python, Tensorflow, PyTorch
January 1, 2019 – Present
Given the lack of publicly-available datasets featuring radar (and the uniqueness of Metawave’s radar) as well as the limitations and cost of human annotators, I lead the AI team to produce our own multi-sensor perception pipeline. Using the data collected with the data collection system, the pipeline produces object detections in 4 dimensions (x,y,z, velocity), also including shape and class information. Some critical components of this system include: ● Leveraging existing camera segmentation algorithms, optimized for inference on our local hardware ● Implementing SOTA lidar object detection algorithms, and extending them for use with FMCW lidar ● Building (from scratch) Kalman filter based tracking with egomotion corrections applied from GPS/IMU ● A “Dataset API” which allowed a user to specify a set of processing operations to apply to a node in the dataset graph. These operations would then execute in parallel as the software recursively traversed the graph. I defined the direction of the AI team, led direct reports in their work on the camera segmentation pipeline, and personally wrote approximately 90% of the code.
Multi-Sensor Data Collection and Visualization — C++, TensorRT
June 1, 2018 – Present
This project successfully developed a system with the following capabilities: 1. Visualize the raw and processed data coming from our sensor suite (multiple radars, cameras, lidars, etc.) both in real time and streamed from disk 2. Collect data from all sensors over long periods with zero loss of information 3. Display the results of real-time and/or post-processed AI inference I was solely responsible for every aspect of this system, with the exceptions of physically mounting the sensors and developing low-level radar communication code. Some of my major challenges and accomplishments were: ● Developing a multi-threaded application to collect and render this data using only a 4 core laptop CPU ● Optimizing the DNN to run at reduced precision to meet timing requirements on a low power GPU ● Developing a system to conduct and export sensor calibrations on the fly ● Constructing novel visualization techniques to enable easy interpretation of high-dimensional radar data
Deep Learning for Object Detection in Radar — Python, C++, TensorFlow
November 1, 2017 – Present
While deep learning algorithms and radar signal processing are both mature fields, their intersection is not. Since joining Metawave in 2017, my overarching goal has been to develop systems to perform object detection and classification using solely data from Metawave’s unique radar. At CES 2019, I demonstrated the world’s first long-range, 77 GHz radar object detection system, running in real-time on a test vehicle. As the sole AI team member up to this time, I single-handedly constructed the training dataset (using a reference camera and crowdsourced annotators), designed and trained the deep neural network which performed the task, and built the system to perform inference and visualize the outputs in real-time. The neural network architecture which achieved the best performance was completely novel compared to other object detection backbones.
Cultural Fit Analysis
The candidate's background is heavily skewed towards AI/ML engineering and research, with significant experience in developing complex, real-time perception systems. While these skills are valuable, the target role is 'Data Analyst'. The projects demonstrate deep technical expertise in data processing, analysis, and visualization, which are relevant. However, the focus on building AI systems from the ground up, leading teams, and advanced physics research might indicate a preference for more engineering-heavy or research-oriented roles rather than a pure data analyst position. The transition from AI Director/Head to Software Engineer at Google suggests adaptability, but the core experience is still very specialized. The lack of explicit data analyst roles or projects focused purely on business intelligence, reporting, or statistical analysis (beyond ML model evaluation) suggests a potential mismatch in day-to-day responsibilities and expectations for a typical Data Analyst role. The breadth of skills is high, but the alignment with a 'Data Analyst' role specifically is moderate.
Soft Skills & Operational Fit
The candidate's project descriptions highlight strong leadership, independent problem-solving, and the ability to manage complex technical challenges (e.g., optimizing DNNs for low-power GPUs, developing multi-threaded applications). The experience as a Director/Head of AI indicates strategic thinking and team leadership. The academic background and research assistant role suggest strong analytical rigor and attention to detail. However, without psychometric test results, a full assessment of work attitude, stress handling, and team collaboration is not possible.