Home

Dung Lam obtained his Ph.D. from The University of Texas at Austin and has published over 26 refereed research papers. His current interests are Spark, Kafka/Samza, mixed OLAP-OLTP infrastructures, and Python. His focus areas have been graph models, NoSQL, software engineering, data mining, and multi-agent systems. Projects include integrating heterogeneous data in graph models, applying pattern recognition and trend analysis to predict future events, and leveraging automated reasoning to explain agent behavior. Dr. Lam developed the graph-based Core-Facets data warehousing framework to integrate disparate data sources, correlate data entities, and extract data facets for multiview and context-aware data analyses. His experience with the latest NoSQL and big data technologies, such as Neo4j, Cassandra, and Hadoop, along with his expertise in data analysis is well-suited for reasoning over huge volumes of structured and unstructured data. In earlier work, he adapted Bayesian data-mining models to improve prediction accuracy by combining time-series analysis with Bayesian networks to account for temporal trends and seasonal cycles. Additionally, he has conducted independent verification and validation of complex and safety-critical software-based products. His dissertation enabled the software comprehension of autonomous software agents by generating explanations of observed activities.

• Resume
• LinkedIn profile