Alex Richards

I'm a Tech Lead with a Machine Learning Engineer skillset working to deliver, at the "Speed of Light", best-in-class performance across a range of NVIDIA's Inference Microservices (NIMs) within the NeMo Retriever organization. Previously, I worked on the autonomous vehicles AI infrastructure to onboard customers and achieve the highest level of MLOps across a multitude of machine learning verticals. Before NVIDIA, I worked at LiveReach AI, a startup that provides clients with real-time IoT sensor analytics. I lead the development of several computer vision products, architected an MLOps infrastructure for processing data and training / evaluating a variety of deep learning models, and improved a WiFi data pipeline by creating algorithms for computing metrics, trilaterating devices, and optimizing sensor configuration parameters. I've coauthored an IEEE journal published paper on object localization and classification (deep learning), a chapter of "Collaborative Recommendations: Algorithms, Practical Challenges, and Applications", and won 2nd place in a deep learning competition hosted by NVIDIA at an IEEE conference. Previously, I have worked at the Cognitive Action unit of UC Berkeley's Ivry lab; there I incorporated procedural learning models into an interface that abstracts the simulation and data visualization processes. I've also worked in the data mining lab at San Jose State University in the knowledge discovery and data mining (KDD) field; more specifically, I experimented with state-of-the-art cosine similarity search algorithms. My technical interests span the fields of brain-computer interfacing, artificial intelligence, and distributed ledgers (block-chains/DAGs). My ultimate aspiration is to model human brain data as part of the design of a chronic, high-fidelity brain-computer interface. My personal interests include hiking, camping, dancing, DJing, playing video/board/card games, investing, coffee, and enjoying the company of good friends.