Daniel Cheng

Quant @ IMC

Chicago, Illinois, United States

About

Experience

  • Quantitative Researcher at IMC Trading
    Aug 2023 - Present · 2 yrs 11 mos

    Research, model, and develop trading strategies for equity auctions, especially low-latency single-stock alpha for the Nasdaq and Nyse closing and opening auctions

  • Quantitative Research Intern at Jane Street
    Jun 2022 - Aug 2022 · 3 mos

    Run statistical studies on market data in Python. Research applications of modern NLP techniques in predicting market information. Research non-standard methods for modeling market impact.

  • Software Development Engineer Intern at Amazon
    Jun 2021 - Sep 2021 · 4 mos

    - Develop bidding algorithms and infrastructure for real-time bidding in online auctions for online advertising that operate on the scaleof hundreds of milliseconds - Create a counterfactual simulator for forecasting auction supply and demand dynamics using Spark via Pyspark with AWS EMR, processing tens of billions of datapoints

  • Software Development Engineer Intern at MileZero, a Capstone Company
    Jun 2020 - Sep 2020 · 4 mos

    - Develop a cloud-based solution for solving the “last mile” problem in a logistics network, using microservices and Java Spring in AWS with DynamoDB - Led a phased migration of configuration data from a legacy system into DynamoDB, including the addition of a REST API and scripts to automate managing configuration data - Led a data mining effort using Snowflake, SQL, and Python, analyzing data to predict optimal times for delivery success

  • Undergraduate Research Assistant at University of Washington
    Jun 2019 - Jun 2020 · 1 yr 1 mo

    - Work at University of Washingotn's "Noah's ARK" Natural Language Processing research group - Researched state of the art Natural Language Processing machine learning techniques, focusing on deep learning with transformers and neural networks - Created a corpus for text-based content-aware recommender systems, using Reddit data to examine user behavior over time - Experimented with novel techniques for automatic domain resolution and adaptation for improving zero-shot performance - Developed and trained a neural model to discover relations between scientific papers and analyze citations - Investigated techniques to enhance interpretability of the attention weights in natural language models