Database Reliability Engineering Team- Project Lead for MySQL, Vitess, and SQLAlchemy upgrades and deployments- Automation systems for Vitess workflows configs and deployment, rotating MySQL hosts, etc.- Upgrade infrastructure (backups, bootstrapping, etc.) to support MySQL 8 roll out- Migartion of MySQL monitoring + dashboards from SignalFX to PrometheusTechnologies: MySQL, ProxySQL, ZooKeeper, Vitess, and related tools and technologies

Stream Processing Team- Designed and implemented benchmarking for various throughput and state size demands for Apache Beam pipelines running in the Yelp streaming infrastructure - Deployed Kew task queue service to new AWS environment in order to support migration from ReviewBoard to Git pull requests for Yelp internal repositories - Built unit test infrastructure for Apache Beam java and python sdk at Yelp- Refactored Stream SQL service to support microsecond timestamp accuracyLanguages and Libraries: java, python, scala, Apache Flink, Apache Beam, Amazon Web Services (AWS), Docker, Apache Kafka, and Paasta

A sports and trading card website and iOS app designed to provide collectors, investors, and enthusiasts with comprehensive pricing data for trading cards. Compz enables users to look up the average prices of both ungraded and graded cards (by PSA, BGS, CGC, and SGC) and helps users identify cards to buy and grade for the best return on investment.Key features include:- Price Lookup: Search for average ungraded and graded card prices.- Investment Insights: Research cards with the best return on investment and most profitable sales ever graded.The iOS app is currently available in the App Store:https://apps.apple.com/us/app/compz/id6737206247Or visit www.compz.nyc to explore the platform!

Responsibilities:- Assisted leadership with founding the Machine Learning and Data Science org as the first ML engineer hired, including assisting with hiring and on boarding process of a diverse pool of talented engineers and researchers- Authored patent and grant applications for novel machine learning research- Collaborated with researchers to develop novel computer vision architectures- Developed, trained, tested and optimized models and algorithms in computer vision, machine learning, and deep learning. These models included: human pose estimation, image and video classification/recognition, object detection/localization/tracking, and optical flow estimation- Designed and built systems for experimentation and evaluation in order to empirically test and compare various models by utilizing applicable evaluation metrics for the given problem- Implemented, trained and deployed ML production systems locally or cloud-based- Performed data collection for training ML models, which included designing and implementing pipeline architectures for data preprocessing, manipulation, duplication, and post processing Languages and Libraries:- c++, keras, html5, lime, mxnet, numpy, pandas, python, opencv, scikit-learn, seaborn, sonnet, tensorboard, and tensorflow

- Supported faculty and research groups within the Psychology Department with computer, network, security, storage, and communication systems- Assembled custom computer builds for user's specified needs- Built, maintained, and troubleshooted computer and software programs for a variety of use cases