Google Scholar citations
Led by my explainable Parkinson’s detection research published in Elsevier’s Computers in Biology and Medicine.
Developer · occasional researcher · persistent debugger
I build backend systems.
Sometimes they behave.
I’m Richard, a software engineer at Amazon. Most days I work on distributed systems, AWS infrastructure, data pipelines, and AI agents. The rest of the time I’m figuring out why a perfectly reasonable system has decided that today is the day it develops a personality.
View Scholar profile ↗
01 · Career
Career progression
Most recent first—from Amazon SDE II back to graduate research at USC.
Amazon · Creators Tech
Software Development Engineer
May 2024 — Present · New York
Software Development Engineer IIApr 2026 — Present
Software Development Engineer IMay 2024 — Apr 2026
Build distributed systems, ML pipelines, and AI-agent workflows for creator compliance and fraud detection.
Java · Python · AWS · Bedrock · SageMaker · DynamoDB · Kinesis
Ignyte Group
Associate Consultant
Jan 2024 — Apr 2024 · Washington, DC
Built a ServiceNow case-management prototype for Washington State court workflows.
JavaScript · ServiceNow · Enterprise workflows
Sayari Labs
Data Engineer Intern
Aug 2023 — Dec 2023 · Washington, DC
Ran Spark and Airflow pipelines that processed millions of public corporate records for risk-intelligence products.
Spark · Airflow · GCP · BigQuery · Kubernetes
Amazon
Software Development Engineer Intern
May 2023 — Aug 2023 · New York
Built a Java service for real-time affiliate promotions, designed to handle more than 600 transactions per second.
Java · ECS Fargate · DynamoDB · AWS CDK
USC Information Sciences Institute
Graduate Research Assistant
Apr 2022 — May 2023 · Los Angeles
Built data and entity-resolution pipelines for PubGraph, a scientific knowledge graph with 385M+ entities and 14.5B relationships.
Python · Neo4j · Elasticsearch · KGTK
02 · Research
Selected research
Explainable ML, scientific knowledge graphs, and citation networks.
View Google Scholar01An Explainable ML Model for Early Detection of Parkinson’s DiseaseVGG16 transfer learning with LIME-based clinical interpretability; 95.2% diagnostic accuracy.02PubGraph: A Large-Scale Scientific Temporal Knowledge GraphA research-ready temporal graph with hundreds of millions of entities and billions of relationships.03Is Dynamicity All You Need?A longitudinal analysis of how research interests shift—and how that movement relates to academic success.04Citation Intent Classification Through Weakly Supervised Knowledge GraphsGraph-based citation intent classification built from multi-hop scientific publication neighborhoods.
03 · About
“I like distributed systems because apparently one computer wasn’t enough trouble.”
I’m a developer first. At Amazon, I build cloud systems for fraud detection, policy enforcement, and creator compliance. Before that, I moved through data engineering, enterprise applications, and research. Basically, several different ways to discover that data is messy.
I hold an M.S. in Computer Science from the University of Southern California.
Distributed systemsAgentic AICloud architectureData engineeringMachine learningKnowledge graphs