Brian Wilcox

Brian Wilcox has worked in the software engineering industry since 2015. In 2015, they worked as a Networks Researcher for Northeastern University's NU MONET program, where they implemented MAC Protocols in Matlab on Teledyne Benthos SM-975 Acoustic Smart Modems. Brian also published a paper titled "Programming Acoustic Modems for Underwater Networking" in the Embark Undergraduate Engineering Review. In 2016, Wilcox worked as a Software Engineering Co-op at MIT Lincoln Laboratory, where they leveraged various libraries to design, write, and optimize signal processing algorithms in C++ for real-time Radar applications. Brian also worked as a Robotics Researcher at Northeastern University that same year, where they implemented a Horizon Image Localization algorithm for an autonomous car in Matlab and wrote sensing code for a Self-driving Lincoln MKZ in ROS and LCM in Python. In 2018, Wilcox worked as a Fraud Risk Management (Data Scientist) Intern at LendingClub, where they used NumPy, Pandas, and Tensorflow to develop models and analyze datasets to improve performance of Credit Risk and Fraud Risk Models. Brian also worked as a Software Engineering Intern at Tensyr Inc., where they developed Machine Learning systems for Autonomous Vehicles and other robotic applications, and as a CV/DL Research Assistant (Quantitative Imaging and Artificial Intelligence Lab) at Stanford University, where they developed new models and optimization techniques with the goal of improving upon prior DL performance metrics. Currently, Wilcox works as a Software Engineer - DL SDK and Compiler at Quadric.io, where they are developing C++ SDK to expose parallel paradigms in Edge Computing, developing support for Memory-Bound Computer Vision and Deep Learning kernels, researching various Artificial Intelligence workloads across different industries, and performing quantization/stability checking on various edge kernels.

Brian Wilcox obtained a Bachelor of Science (B.S.) in Electrical and Computer Engineering from Northeastern University between 2014 and 2017. Brian then went on to pursue a Master of Science (M.S.) in Electrical Engineering: Optimization and Machine Learning from Stanford University between 2017 and 2019. Additionally, they obtained four certifications from Coursera and Stanford University between 2017 and 2019, including Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Neural Networks and Deep Learning, Structuring Machine Learning Projects, and Machine Learning.