About
I’m a Data Scientist at Lazard, where I apply machine learning, quantitative analysis, and statistics to finance. I graduated summa cum laude from the University of California, Berkeley with a BA in statistics and applied math, and I’m currently pursuing a MS in computer science (part-time) from the University of Texas at Austin.
Publications
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]
Projects
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.
Reinforcement Learning, Deep Learning (pytorch, optuna), Python
Predicting Batting Performance with Ensemble Neural Nets and Data Augmentation in Baseball Simulator [pdf]
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.
Deep Learning (pytorch), Machine Learning (sklearn), Web Scraping (beautifulsoup, selenium, requests), Python, SQL
Quantitative Analysis for Baseball Simulator [Web Application]
Web application to present deep learning model that predicts batter performance in a baseball simulator.
Deep Learning (pytorch), Machine Learning, Dashboards (streamlit), Python, SQL
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.
Natural Language Processing (NLP) (nltk, transformers), Deep Learning (pytorch), Machine Learning (sklearn, optuna), Python
A Statistical Approach to Analyzing and Mitigating Question-Answering Artifacts in SQuAD [pdf]
A statistical approach was used to analyze the behaviour of pre-trained question-answering models, identifying areas where it demonstrates poor textual and logical understanding. This was improved using an adversarial challenge, showing some improvement in questions that require a higher degree of logical understanding and extrapolation.
Natural Language Processing (NLP), Statistical Inference, Python
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.
Web Scraping (beautifulsoup, selenium, requests), Python, SQL
Writing profiles and updates on hockey players of Asian descent. November 2018-Present.
Work Experience
Lazard (Investment Bank/Asset Management)
- Senior Data Scientist (January 2022 - Present)
- Data Scientist (July 2020 - January 2022)
- Data Science Intern (May 2019 - August 2019)
WellDone International (NGO)
- Data Science Intern (June 2018-October 2018)
Berkeley Model United Nations
- CEO (March 2019 - March 2020)
- Board Member (March 2019 - March 2023)
- VP of Organizational Partnerships (April 2017 - April 2019)
Education
University of Texas at Austin (Expected: 2024)
- 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
Royal Conservatory of Music (November 2014)
- Associate Diploma (ARCT), Piano Performance
Research
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 Experience
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)