Connor Goggins
Founder at Stealth AI Startup
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolConnor Goggins is a New York–based founder and machine learning engineer with eight years of experience building production ML and full-stack systems across finance and cloud. He was a Quantitative Developer at Citadel focused on equity quantitative research and previously built Amazon AI services as a Software Engineer II at AWS. His technical strengths span deep and reinforcement learning, computer vision, and NLP, with hands-on expertise optimizing ML primitives — including contributing to Apache MXNet’s performance benchmarking framework, adding large-tensor operator tests and fixing core bugs. Now leading a stealth AI startup, he combines research-grade modeling with production experience to move models from prototype to low-latency, reliable deployments. He holds a B.S. in Computer Science from Columbia and completed coursework at Stanford and Washington University, reflecting strong academic foundations paired with practical, high-performance delivery.
9 years of coding experience
6 years of employment as a software developer
College Preparatory School, College Preparatory School at Phillips AcademyBachelor of Science (B.S.), Computer Science, 3.8 GPA, Bachelor of Science (B.S.), Computer Science, 3.8 GPA at Columbia University in the City of New YorkComputer Science, 3.9 GPA, Computer Science, 3.9 GPA at Washington University in St. LouisUndergraduate Coursework - Summer, 4.15 GPA, Undergraduate Coursework - Summer, 4.15 GPA at Stanford University
English, Spanish
Github Skills (14)
mxnet10
deep-learning10
performance-testing10
python9
convolutional-neural-networks8
convolutional-neural-network8
neuralnetwork8
neural-network8
linear-algebra8
tensorflow7
operation7
tensor7
tensorrt7
pooling7
Programming languages (6)
Github contributions (5)
apache/mxnet
Feb 2020 - May 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
ML Engineer
ML EngineerContributions:24 commits, 26 PRs, 182 comments in 3 months
Contributions summary:Connor primarily contributed to the performance benchmarking framework for the MXNet deep learning library. Their work focused on implementing and testing various operators related to linear algebra, random sampling, neural network activations, pooling, convolution, and miscellaneous operations. The user also added functionality for testing large tensors and fixed bugs in existing implementations.
pythonschedulerdataflowmutationdata-science
connorgoggins/incubator-mxnet
Jan 2020 - May 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Contributions:361 pushes, 30 branches in 3 months
pythonschedulerdataflowmutationorchestration
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