Nigel Lee

About Nigel Lee

Diversity of experience, consistency of excellence. In a nutshell, that would describe my 25+ year career in electrical engineering.At MIT Lincoln Laboratory, I gained expertise in adaptive array processing applied to passive sonar processing. My work helped to advance the state of the art in passive sonar detection and estimation, by increasing the robustness of adaptive algorithms toward mismatch and source motion.At EuclidIQ, I charted the company\'s technical direction to develop video compression algorithms that optimize human perceptual quality. I spearheaded the company\'s effort to develop a credible subjective test methodology to validate our claimed 20% compression gains over industry benchmarks. At Corista, I focused on improving medical image classification, executing multiple machine learning techniques, including deep convolutional neural networks (CNNs) and feature-based classifiers. My research pointed the company toward a particular deep CNN implementation that achieved 90% classification accuracy, a 20% improvement over previous results.At STR, I returned to defense applications, including sonar and radar. However, I also established myself as one of STR’s machine learning experts, applying deep CNNs to improve sonar and radar image classification and generative adversarial networks to increase the realism of synthetic data.In my current position at Draper, I continue to apply my machine learning expertise in multiple projects, for example designing a framework to evaluate and improve 8+ machine learning algorithms for demographic inference with multimodal data, achieving 15% classification improvement. I have done extensive technical writing over the course of my career, authoring successful papers, patents, and proposals. I enjoy simplifying complex technical concepts for general audiences and explaining both their significance and their potential. Over my career, I have found that the while the goals and challenges of any given field may be different, all fields benefit from critical thinking, consistent and clear evaluation of performance, and creativity built on solid, fundamental understanding of theory. Those are the qualities I have brought in achieving a standard of excellence at each of my positions. The diversity of my experience enables me to cull ideas and techniques from seemingly disparate fields and apply them in novel ways.I\'m always looking to grow my personal and professional network. Feel free to connect via LinkedIn or contact me directly at n••••••@gmail.com.

Experience

1. Principal Scientist and Group Leader (Machine Intelligence Group)

   Draper

   May 2023 — Present · Cambridge, MA, US

   Directing team of ten to develop novel methods to detect hypersonic vehicles via atmospheric disturbances• Coordinating team of four to design machine learning and deep learning classifiers to draw inferences about subject mental states based on time-varying sensor data• Implemented 8+ machine learning and deep learning classifiers to predict sensor failure from other sensor data• As group leader, staffing group members with fulfilling projects and supporting group members to excel in their work and advance their careers

Education

• Princeton University

  Doctor of Philosophy - PhD, Electrical Engineering

• Brown University

  Bachelor of Science - BS, Electrical Engineering

Skills

• Computer Vision
• Algorithms
• Parameter Estimation
• Linux
• Video Processing
• Powerpoint
• Signal Processing
• Signal Detection
• Simulations
• Video Compression
• Statistical Signal Processing
• Classification
• Software Development
• Latex
• Algorithm Design
• Scientific Writing
• Matlab
• Cloud Computing
• Image Processing