Lucille Eleanor Nguyen

contact [at] lucenguyen [dot] com

My Blog, Poverty of Attention

I am a data scientist with experience in fraud prevention, financial analytics, and statistical modeling in a wide range of industrial applications. I currently work in the Predictive Modeling group at LegalMatch, where I work on AI/machine learning engineering, statistical forecasting, and algorithmic matching for the legal services industry. I also consult on data science, artificial intelligence, and computational social science issues at Chromatic Data, a strategic artificial intelligence consultancy focused on synthetic data for social impact organizations.

I have academic training in data science, mathematics, social science, and philosophy. I'm currently studying technology management and artificial intelligence in the graduate program in Artificial Intelligence Management at Georgetown University. I hold a M.Sc. in Data Science from Eastern University, with a capstone/thesis (available here) on the philosophy and application of the Akaike Information Criterion for statistical inference. I graduated Phi Kappa Phi in the Liberal Arts and Sciences at Northern Illinois University summa cum laude focusing on quantitative social science and the philosophy of science. I also did graduate coursework in mathematics at Northwest Missouri State University.

I also hold a certification as a Climate Protection Professional, am a published poet, was formerly a professional photographer, am an amateur baker and chef, and am an avid enjoyer of the outdoors.

• Strategic Energy Innovations Climate Corps Fellow, working with the Bay Area Rapid Transit District and Capitol Corridor Joint Powers Authority on transportation operations research, sustainability, and project management.
• Worked at the US Department of Agriculture, Forest Service Office of Safety and Occupational Health as a data scientist developing analytic pipelines to support key program areas like emergency medical services, fire and aviation management, occupational safety, and casualty prevention.
• Additionally wore a second hat as a Emergency Medical Program Specialist, working on operations and research alongside medical research partners at Vanderbilt University and Wake Forest University for directing emergency medical services. This included statistical research on novel datasets on remote medical care that are currently being used to update guidelines for nationwide emergency medicine practices.
• Consulted for the US Department of Transportation Office of the Secretary of Transportation - Policy on geographic information systems, statistical modeling, and sustainability analytics with a focus on web mapping tools for infrastructure development.
• Liased with the Department of Homeland Security, Department of the Interior, and responded to Executive and Legislative branch inquiries on disaster response, emergency medical care, and utilizing computational tools to improve public safety and predict emergent events.
• Developed epidemiological and occupational health data collection procedures for government, utilizing anonymized data and privacy-preserving technologies to track and prevent in-the-field health issues for disaster response efforts.
• Participant in the Woodrow Wilson International Center for Scholars Technology Labs on artificial intelligence developments and policy proposals for executive and legislative branch staff. All events were conducted under Chatham House Rules for presenters and participants in the labs.
• Routinely contributed to public sector data science initiatives, including attenting District of Columbia-based conferences in civic technology, being an active member of the Artificial Intelligence Community of Practice with the General Services Administration's Information Technology Modernization Centers of Excellence, and was chosen for a workgroup for the NSF/MITRE Federal Artificial Intelligence Workforce Effort.
• Worked at Federal Student Aid, An Agency of the US Department of Education as a data scientist in the Statistical Research and Modeling Group on fraud prevention, financial analytics, and data governance initiatives for managing the over $1.6 trillion federal student loan portfolio.

Personal Note and Reflections

On a personal note, much of my life's work has been inspired by Dr. Kenneth P. Burnham, Emeritus Professor of the Department of Fish, Wildlife, and Conservation Biology at Colorado State University and retired Senior Scientist with the United States Geological Survey. In addition to writing Model Selection and Multimodel Inference: An Information-Theoretic Approach (available here from Springer Nature) with the late theoretical ecologist Dr. David R. Anderson that inspired my master's research "Inference Using the Akaike Information Criterion", a personal note he once sent me has been part of my guiding career principles:

"Too much of statistics, and statisticians, are driven by pure mathematics at the expense of useful applied statistics and data science."

At the age of 11, I happened across the late mathematician-logician Haskell B. Curry's text trying to resurrect the formalist school of the philosophy of mathematics after the consequences of Gödel's incompleteness theorems were understood, Outlines of a Formalist Philosophy of Mathematics. A student of David Hilbert, he was surely inspired by Hilbert's famous words: "Wir müssen wissen. Wir werden wissen." I have re-read Curry's philosophical opus every year for over a decade now to better understand, to use Curry's terminology, the science of formal systems (or formal methods) that is mathematics. The greatest romance I have ever known is the reason of theorems and axioms that is mankind's attempt to organize the structure of reality with the language of mathematics.

Pure mathematics has long been a driving force in my life. I was first admitted to college at the age of 13 based upon my love of differential equations and the combinatory calculus. Yet life has always proved more complex than the rigid formalisms of logical operations might allow: turbulent flows and complex systems define the world we have inherited for ourselves. Though David Hilbert and his protege Haskell Curry might believe that we must and will know, sometimes we must accept good enough inferences to the best explanation without true knowledge. Sometimes the "useful applied statistics and data science" helps us get asymptotically ever-closer to the ideal of realizing the nature of Curry's "essence of mathematics... not in any particular kind of formal system, but in formal structure as such."

If my life has been defined by any one thing, it has been chasing after the shadows of formal structure projected on the walls of Plato's cave. Whether those lights that project those shadows have been epistemology, social science, or scikit-learn, I have always sought to appreciate the trace of this world beyond our own. I have found Dr. Anderson's information-theoretic language and his call for practical applications particularly useful tools in this regard. I can say in no small part that my academic and professional careers have been driven by this ambition.

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Affiliations are given for identification purposes only. All opinions are conducted in a personal capacity. The views expressed are my own and do not necessarily represent the views of my employer(s), past or present.