Amazon

Engaged in the development of cutting-edge recommender systems, focusing on the integration of advanced sentence transformer embeddings to enhance personalized user experiences. Participated in a highly confidential project aimed at redefining how recommendations are generated and delivered, leveraging state-of-the-art machine learning techniques to improve accuracy and user satisfaction. This initiative, while not yet publicly released, promises to set new benchmarks in personalized content delivery and user engagement.

Contracting through Apex Systems

Applied Scientist III (Contractor)

Babylon

Developed a stream-based patient monitoring system utilizing Apache Flink to track vital metrics such as blood pressure and blood sugar, enabling adaptive alert dispatch to mobile devices for high-risk cohorts.
Engineered an Apache Iceberg-based data lakehouse to securely store patient data (FHIR), facilitating seamless integration with Sagemaker and Athena for advanced data analysis.
Enhanced ZenDesk service desk ticket routing efficiency through the development of a topic classification model, employing distilBERT fine-tuning via Transformers to accurately categorize tickets into various topics including medications and appointments.
Advanced medical diagnosis coding accuracy by creating a recommendation model that utilizes embeddings (Neural Matrix Factorization and NMF), targeting the improvement of medical code detection and recapture (HCC, ICD-10).
Implemented and operationalized patient risk models using XGBoost to predict hospital readmittance and rising costs, integrating feature preprocessing with Athena SQL and model inference in Sagemaker for deployment in the AI platform.
Conducted initial experiments with Large Language Models (LLMs) and prompt augmentation techniques for clinical note summarization, leveraging AWS Bedrock foundation models and Langchain for prototype development.

Staff Applied Scientist

Visa

Specialized in deploying high-reliability, low-latency real-time inference systems. Achieved optimal performance by refining TensorFlow models through graph freezing, distillation, and quantization techniques, and leveraged NVIDIA Triton for deployment. Enhanced system efficiency and performance by migrating select stream processing components from Python to Rust.

Machine Learning Scientist

Habana Labs

Contributed to a team effort in executing distributed pretraining of Large Language Models (LLM) on a Kubernetes cluster, optimizing for scalability and efficiency. Participated in cluster instrumentation and metrics oversight, employing Kubernetes device plugins for data collection and integrating Prometheus and Grafana for effective metrics reporting and visualization.

Machine Learning Engineer

Expedia Group

Contributed to the implementation of multi-armed bandits and reinforcement learning for web page layout optimization, enhancing user engagement and satisfaction.
Owned the deployment of dozens of models to the AI platform, transforming initial concepts from Jupyter notebooks into production-level code, ensuring scalability, reliability, and performance across the platform.

Staff Machine Learning Engineer

Successfully migrated the NLP classifier system from a combination of Spacy + NLTK TF-IDF to a BERT-based fine-tuning approach. This enhancement significantly improved the detection of terms of service violations related to off-platform trades and conversations. Additionally, facilitated the collection of human-labeled data using the SpaCy Prodigy labeling system for more accurate model training.

Led the design and implementation of the machine learning platform, utilizing microservices architecture to ensure modularity and scalability. Incorporated comprehensive instrumentation and monitoring capabilities to maintain high performance and reliability across all ML operations.

Senior Machine Learning Engineer

Led the Applications team in developing tools designed to enhance the iOS application development experience on the IBM Bluemix platform. Oversaw team direction, sprint planning, conducted code reviews, and provided mentorship to foster team growth and product excellence.
Contributed as a developer to the Watson iOS SDK, integrating Watson's advanced machine learning services into the mobile ecosystem, thereby broadening the scope of cognitive computing on iOS devices.
Participated in the Swift-on-the-Server team, focusing on crafting a lightweight microservice/web server in native Swift for deployment on Linux platforms, enhancing server-side Swift capabilities and performance.

Software Engineering Team Lead

The University of Texas at Austin

Served as an Instructor for Computer Organization and Architecture, engaging and educating 400 students on the foundational principles and structures of computer systems.
Taught Elements of Mobile Computing, focusing on Android development, to a dedicated cohort of 40 students, providing them with hands-on experience in mobile application development.
Led a Programming Languages course with an emphasis on JavaScript for 40 students, imparting essential programming skills and modern JavaScript frameworks and practices.

Lecturer of Computer Science

College of William and Mary

Conducted lectures in Discrete Structures and Mathematics for 100 students, covering fundamental mathematical concepts critical for computer science.
Led Data Structures and Algorithms classes for 200 students, focusing on essential programming techniques and efficiency in computing.
Taught a specialized course in Mobile Cloud Computing to 25 students, exploring the integration of mobile technologies with cloud-based services.

Visiting Assistant Professor

University of Virginia

Accumulated over six years of experience in crafting machine learning algorithms aimed at understanding human behaviors—such as social interaction, physical activity, and speech patterns—as well as health indicators like stress, through the analysis of data from wearable physiological and inertial sensors.

Graduate Research Assistant

U.S. Naval Research Laboratory

Conducted research utilizing augmented reality head-mounted displays and trackers, focusing on the development and application of AR technologies.

Research Assistant Internship

Machine Learning Scientist at Babylon

Robert F. Dickerson is a machine learning scientist and engineer with a PhD in Computer Science and about 10 years of experience applying AI/ML to the industry. He recently worked as a Staff Applied Scientist at Babylon Health's Data Science and AI Platform team, where he was involved in the development and deployment of AI solutions for digital healthcare.

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