Connor Goggins
Founder at Stealth AI Startup
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolConnor Goggins is a founder and machine learning engineer with nine years of experience building production ML systems and full-stack software, currently launching a stealth AI startup in New York. He brings hands-on expertise in deep learning, reinforcement learning, computer vision, and NLP from roles at Citadel, AWS Amazon AI, and internships at Uber and Tinder. At Citadel he worked in equity quantitative research, blending ML rigor with finance-grade engineering, and previously contributed performance benchmarking and operator fixes to the widely used Apache MXNet deep-learning library. Connor’s background spans cloud ML product work (Elastic Inference at AWS) to large-scale model/operator performance tuning, reflecting a rare mix of research-level ML and production engineering. Educated at Columbia with additional coursework at Washington University and Stanford summer sessions, he pairs academic breadth with practical shipping experience.
9 years of coding experience
6 years of employment as a software developer
Undergraduate Coursework - Summer, Undergraduate Coursework - Summer at Stanford UniversityBachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at Columbia UniversityComputer Science, Computer Science at Washington University in St. LouisCollege Preparatory School, College Preparatory School at Phillips Academy
English, Spanish
Github Skills (12)
mxnet10
deep-learning10
performance-testing10
python9
convolutional-neural-networks8
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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