Wenbo Ma

Wenbo Ma

About Me

I am pursuing a PhD in Operations Research/Decision Sciences at the George Washington University School of Business, advised by Miguel Lejeune. Because of my research interest, I am also taking coursework within the PhD Program in Integrated Biomedical Sciences at GW’s School of Medicine. In 2023 Fall, I enrolled and studied for one semester in the PhD program in Data Sciences and Operations (DSO) at Marshall School of Business, University of Southern California (USC) , before returning to D.C. for family reasons.

My current research interest lies at the intersection of mathematical optimization and network science, with applications in two seemingly unrelated yet personally fascinating fields: medical sciences/network medicine and quantitative trading.

In medical sciences, I am exploring how mathematical optimization can enhance the understanding, diagnosis, treatment, and healthcare delivery for life-altering illness in neurology and immunology, including neuro-degenerative disorders (e.g., Alzheimer’s disease), immune disorders (e.g., autoimmune diseases), and vestibular disorders (e.g., Mal de Débarquement Syndrome(MdDS)).

In quantitative trading, my goal is to design computational models that empower retail and individual traders to make more informed investment decisions. This contrasts with the traditional emphasis on supporting institutional traders.

Before transitioning to academia, I was a data scientist/quant in industry, leading cross-functional projects to develop large-scale, production-ready machine learning and mathematical optimization models. These models were designed to improve operational efficiency in areas such as fraud prevention, customer experience, credit risk assessment, and pricing optimization. For instance, at Capital One, I developed a novel integer programming model to optimize fund availability following customer check deposits, while simultaneously mitigating the bank’s exposure to fraud risk. This solution significantly accelerates funds availability for millions of the customers who deposit checks through Capital One’s mobile app, providing faster access to their money. Over the course of my career, I have worked at various roles at Goldman Sachs, Capital One, Freddie Mac, AvalonBay(AVB), and Deloitte.

Outside of work, I enjoy playing and watching sports — particularly tennis, swimming, basketball — as well as reading about history.

Publications

Refereed Journals

Non-peer-reviewed publications

Industry Experience

Full-Time

  • Risk Strats/Quant, Goldman Sachs (Remote from Los Angeles, CA and McLean, VA)
  • Principal Data Scientist, Capital One (McLean, VA)
  • Quantitative Analyst, Freddie Mac (McLean, VA)
  • Data Scientist, AvalonBay Communities (NYSE: AVB) (Arlington, VA)
  • Data Analyst, Deloitte (New York, NY)

Internship

  • Audit Intern, PwC (Hong Kong)
  • Audit Intern, RSM U.S./McGladrey (Chicago, IL)

Teaching

  • Tutorial:
    • Introduction to Modeling with Gurobi Python Interface, 2022, session for PhD Students in the Decision Sciences Dept. and Smart Grid Lab at GW

Learning

Formal Education

  • Ph.D. (in progress) Operations Research/Decision Sciences at George Washington University School of Businesses
  • Ph.D. (dropout) Operations Research, Data Sciences and Operations Department at Marshall School of Business, University of Southern California (USC)
    • Withdrew after completing the first semester to return to D.C. for family reasons
  • M.S. Data Science - Operations Research (finished part-time while working in industry), George Washington University
  • M.S. Statistics, Rutgers University - New Brunswick

Online Learning

Since my research is interdisciplinary and my formal education is only in the “math” part, I have been studying “the other” part – medical science, biology, chemistry, and physics – via massive open online courses (MOOC)