Cynthia Chen

Email	cynthiachen@college.harvard.edu
LinkedIn	@chen-cynthia
Github	@chenxcynthia

Education

• 2020 - 2024

  Harvard University

  M.S. Computer Science, B.A. Computer Science and Secondary in Statistics.

• 2016 - 2020

  The Harker School

  High School

Experience

• Summer 2023

  Scale AI

  Software Engineering Intern

  • One of five engineers building Scale’s Enterprise Generative AI Platform for enterprise LLM applications.
  • Built end-to-end REST APIs to enable Confluence, Google Drive, and S3 integrations and the ingestion of unstructured data into LLM vector stores.
  • Added critical features for sync observability, shipping + integrating for an internal customer in < 2 weeks.
  • Drove and owned the creation of the EGP API documentation, released to enterprise customers.
• Spring 2023

  Harvard Insight and Interaction Lab

  Undergraduate Research Assistant

  • Performed scaling and correlation experiments to optimize and enhance the visualization of transformer attention via joint key + query embeddings.
  • Explored in-context learning behavior and induction head behavior in BERT and bidirectional models.
  • Co-authored paper "AttentionViz: A Global View of Transformer Attention".
• Summer 2022

  IMC Financial Markets

  Quantitative Trading Intern

  • Worked on the F1 desk trading ES/NQ futures. Created a time-series prediction model to predict short-term ES/NQ movements.
• Winter 2022

  Hudson River Trading

  Winter Intern

  • Developed order book infrastructure (C++) and algorithms to identify high-frequency trading (Python).
• Summer 2021

  LinkedIn

  Software Engineering Intern

  • Team: AI and Data Infrastructure.
  • Developed an end-to-end integration to run Tensorflow Extended (TFX) pipelines on Apache Beam using SparkRunner.
  • Tested integration for improvements in scalability and performance of LinkedIn’s ML production pipelines.
• Spring 2021

  MIT CSAIL

  Undergraduate Research Assistant

  • Part-time research at the Torralba Lab.
  • Investigated computer vision model interpretability by visualizing interactions between activations during model training. Supervised by David Bau and Antonio Torralba.
• 2018-2020

  Harvard T.H. Chan School of Public Health

  Research Intern

  • Conducted a two-year project at the Shirley Liu Lab, developing a computational framework to interpret the sequence patterns in deep learning models. Identified 65 protein sequence motifs for cancer drug synthesis.
  • Co-authored paper in Molecular Cell. Presented research at 2019 & 2020 AACR conferences.

Skills

• Languages: Python • C/C++ • Java • SQL • R • TypeScript • JavaScript • HTML/CSS • MATLAB
• Tools: TensorFlow • PyTorch • scikit-learn • numpy • pandas • Flask • D3 • ReactJS • OpenProcessing • p5.js • Jupyter • Linux • Git

Honors and Awards

2023	Roberts Family Technology Innovation Fellow
2023	Excellence in Academic Advising Awards, Harvard University
2022	Neo Scholar
2020	Regeneron Science Talent Search Finalist
2019	Davidson Fellows Laureate ($50,000 Scholarship Recipient)
2019	Research Science Institute Scholar
2019	Intel International Science and Engineering Fair 3rd Award
2016, 2018	2x USA Junior Math Olympiad (USAJMO) Qualifier
2015 - 2019	5x American Invitational Mathematics Exam (AIME) Qualifier
2017	USA Computing Olympiad (USACO) Gold Division