• Conducting research in support of the NIST AI Risk Management Framework.• Administering the MSBA practicum program.

• Teaching graduate and undergraduate classes, including: ○ Responsible machine learning: https://jphall663.github.io/GWU_rml/. ○ Data mining: https://github.com/jphall663/GWU_data_mining. ○ Data ethics.• Advising on curricula in the department of decision sciences.

Creating lectures, assignments, and assessments for graduate data mining and machine learning courses, and teaching SAS programming workshops for the department of decision sciences.

Founded niche services organization that focuses on machine learning and AI risk management. Initial engagements relate to: • Traditional model governance. • Generative AI risk management.• High-stakes machine learning model audits.

Providing guidance in support of H2O.ai's responsible AI efforts.

• Product management and design for award-winning AI prediction engine, H2O Driverless AI: https://www.infoworld.com/article/3251828/application-development/infoworlds-2018-technology-of-the-year-award-winners.html?nsdr=true#slide17.• Leading science and product efforts around explainable AI, ethical AI, model debugging, and security of machine learning.• Invited presentations at international research conferences: https://ww2.amstat.org/meetings/jsm/2019/onlineprogram/AbstractDetails.cfm?abstractid=304321.

• Designer and data science lead for interpretability, transparency, and trustworthiness efforts for AI prediction engine, H2O Driverless AI.• Advising some of the best commercial data science groups in the U.S. on advanced machine learning projects.• Co-organizer for Washington D.C. Artificial Intelligence and Deep Learning meetup group: https://www.meetup.com/Washington-D-C-Artificial-Intelligence-Deep-Learning/.

• Influencing machine learning research direction for SAS Enterprise Miner, a leading commercial data mining and machine learning solution with over 1 B US$ lifetime revenue. • Advising Fortune 500 customers on real-world, large-scale machine learning problems; involved in numerous multi-million US$ customer engagements.• Enabling technical sales teams and user communities by creating white papers, blogs, and YouTube videos, and developing and maintaining open-source, public GitHub repositories to share advanced machine learning use cases; maintained the most popular SAS repository on GitHub. • Collaborating with colleagues to design an automated system to clean and cluster a data set for market segmentation purposes (U.S. Patent Numbers 9202178, 9367799, and 9367602). Methods to be implemented is SAS’ next-gen data mining and machine learning solutions.

• Eleventh person worldwide to become a Cloudera certified data scientist.• Researching and implementing new data mining and machine learning approaches for SAS Enterprise Miner; data mining for special projects.

• Co-invented the aligned box criterion for estimating the number of clusters in a data set (U.S. Patent Number 9424337). This method was later implemented in two commercial distributed, in-memory data mining and machine learning solutions.• Developed SAS Enterprise Miner R Integration, enabling R models to be deployed in-database, in-Hadoop, and through web services and to be monitored using industry standard model management tools.• Won internal SAS data mining challenge with 0.99014 correct classification on Kaggle MNIST data.• Teamed up with colleagues to introduce deep learning research into SAS Enterprise Miner R & D; awarded "Coolest Idea" prize with two colleagues in SAS Advanced Analytics Science Fair for "Deep Learning in Enterprise Miner" project.

• Constructed response models for consumer credit risk group. • Scheduling work flow and communications with corporate sponsors for a team of five Master’s students.

Developed Java applications for physical security and administered basic Ubuntu Server/Apache Tomcat systems.

Implemented proof of concept for wrapping the C Cairo graphics library in the C++ skia (Skia Graphics Engine) API.