Hubert Luo

AI / Investment Banking

AI/Data Science Leader with 5 years of experience managing cross-functional projects in M&A, capital markets, and geopolitical advisory. Expertise in machine learning, statistics, and LLMs, with advanced development skills in Python and cloud platforms. Outside of work, I research AI and machine learning applications to sports and have published in leading journals. I graduated summa cum laude from the University of California, Berkeley with a BA in statistics and applied math.

PublicationsPermalink

Improving NHL draft outcome predictions using scouting reports

Luo, H. Journal of Quantitative Analysis in Sports. 2024. [pdf] [preprint] [web app]

Look who’s talking: gender differences in academic job talks

Glazer, A.K., Luo, H., et al. ScienceOpen Research. 2023. DOI: 10.14293/S2199-1006.1.SOR.2023.0003.v1 [pdf]

Work ExperiencePermalink

Lazard

  • Vice President, Data Science (January 2025 - Current)
  • Associate, Data Science (January 2022 - January 2025)
  • Data Scientist (July 2020 - January 2022)
  • Data Science Intern (May 2019 - August 2019)

WellDone International

  • Data Science Intern (June 2018-October 2018)

EducationPermalink

University of Texas at Austin (Expected: 2025)

  • M.S. Computer Science (in progress)
  • Selected Coursework: Deep Learning, Natural Language Processing (NLP), Machine Learning, Optimization

University of California, Berkeley (May 2020)

  • B.A. Statistics, B.A. Applied Math. summa cum laude (highest honours)
  • Selected Coursework: Time Series, Probability, Sampling, Linear/Abstract Algebra, Real/Complex/Numerical Analysis
  • Secretary-General, Berkeley Model United Nations, 68th Session

Royal Conservatory of Music (November 2014)

  • Associate Diploma (ARCT), Piano Performance

ProjectsPermalink

Reinforcement Learning on NHL Expected Goal Models in SuperTuxKart [pdf]

Applying key factors and lessons learned from National Hockey League (NHL) expected goal models to SuperTuxKart using reinforcement, imitation, and deep learning approaches via a state-based agent.

Predicting Batting Performance with Ensemble Neural Nets and Data Augmentation in Baseball Simulator [pdf] [web app]

Developing a metric to evaluate batting performance, identifying the drivers of increased offensive ability, and using ensemble neural net architecture with data augmentation to improve prediction of batting performance for 20,868 seasons in a baseball simulator.

Yelp Review Rating Prediction with Stacked LSTMs and Data Augmentation [pdf]

Various NLP architectures including stacked LSTMs, ensemble transformers, and logistic regression were applied in conjunction with data augmentation techniques to effectively predict Yelp review ratings.

Identifying Optimal Draft Strategies in a Baseball Simulator [pdf]

Scraping and analysis of 108,488 draft picks from 23 seasons based on various draft metrics in a baseball simulator.

ResearchPermalink

Stark Research Group, UC Berkeley Statistics Department (September 2018 - November 2020)

I worked on the Look Who’s Talking project to examine differences in the number, nature, and duration of interruptions in academic job talks based on the presenter’s gender.

Family and Culture Lab, UC Berkeley Psychology Department (January 2017 - May 2018)

I worked on the Kids and Family Project, a longitudinal study that examined the risk and protective factors for mental health adjustment in Chinese-American immigrant children.

Teaching ExperiencePermalink

TA (Undergraduate Student Instructor): Data Science (Spring 2020, Fall 2019)

Group Tutor: Data Science (Spring 2019, Fall 2018), Probability Theory (Spring 2018), Linear Algebra (Fall 2017)