Shengyu Zhu

About Me

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I am a Research Data Scientist at Google, specializing in Generative AI (GenAI) at YouTube. I also serve as a part-time Course Developer for the Stanford GenAI professional program. I hold a Ph.D. in Business Administration from the Simon Business School at the University of Rochester (2022).

Location: YouTube San Bruno Campus, Bay Area, CA

Employment

• Stanford University, Course Developer (part-time)

  Palo Alto, CA | Dec 2025 - Present

  Course: XFM110 Technical Fundamentals of Generative AI, part of the Generative AI professional education program

• Google, Data Scientist - Research

  Bay Area, CA | May 2022 - Present

  YouTube Data Scientist - GenAI Team (Oct 2024 - Present)
  My project focuses on fairness: Ensuring fair generative experiences by mitigating bias in automated systems

  Ads Insight & Measurement Team (May 2022 - Oct 2024)
  Leveraging Chrome Privacy Sandbox APIs for privacy-preserving ad measurement.
  GitHub Link
  Leadership & Community: Planning Committee Member for the 2024, 2025, and 2026 Google Data Science Summits

Education

• Stanford University

  Graduate Certificate in Artificial Intelligence, 2025 - Present

• Simon Business School, University of Rochester

  Ph.D. in Business Administration (Quantitative Marketing), 2016 - 2022

  Advisors: Mitchell Lovett and Yufeng Huang

  M.S. in Business Administration, 2016 - 2019

• Central University of Finance and Economics

  B.S. in Economics (Quantitative Economics and Quantitative Finance), 2012 - 2016

Publications

Peer-Reviewed Publications

1. "Detecting Foreign Content in Self-Generated Text: A Recognition Study of Large Language Models" Shengyu Zhu, Tamika Bassman, Dat Tran, Aryaman Arora. NeurIPS 2025 LLM Evaluation Workshop, 2025. [workshop link] [paper link]
2. "Optimizing Hierarchical Queries for the Attribution Reporting API" Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu. Proc. of the AdKDD Workshop at the 29th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (AdKDD'23), 2023, AdKDD website link, Proceeding link

Patents

1. "Optimizing Hierarchical Queries for Digital Content Estimation". Badih Ghazi, Matthew Dawson, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu. WO2025023994, Google, published Jan 30, 2025
2. "Optimizing Configuration of the Attribution Reporting API". Matthew Dawson, Badih Ghazi, Harikesh Nair, Bo Luan, Fangzhou Sun, Nishanth Mundru, Shengyu Zhu, Kapil Kumar, Vikas Kumar Singh, Anurang Bhuta, Wesley DeVore. WO2025106133, Google, published May 22, 2025
3. "Integration of Event and Aggregated Summary Reports from the Privacy Sandbox Attribution Reporting". Tong Geng, Qing Zhang, Fan Zhang, Harikesh Nair, Adam Smith, Leo Wang, Kapil Kumar, Qing Zhang, Matt Dawson, Shengyu Zhu, Bo Luan, Nishanth Mundru, Fangzhou Sun, Henry Chen, Vikas Kumar Singh. WO2025019054A1, Google, published Jan 23, 2025

Invited Seminars

• "Surcharge Waived by Salesperson: Empirical Evidence from Automobile Oil Changes"
  College of Business, Shanghai University of Finance and Economics (Sep 2021) School of Management, Xiamen University (Sep 2021) HKUST Business School (Oct 2021) CUHK Shenzhen (Oct 2021) C. T. Bauer College of Business, University of Houston (Nov 2021) A. B. Freeman School of Business, Tulane University (Nov 2021) John Molson School of Business, Concordia University (Nov 2021) INFORMS Marketing Science Conference (June 2021)
• "Consumer Demand under Mail-in Rebate Promotion"
  INFORMS Marketing Science Conference (June 2020) Advanced Research Techniques (ART) Forum (June 2020)

Honors & Awards

• 2020 AMA-Sheth Foundation Doctoral Consortium Fellow, Indiana University Bloomington June 2020
• INFORMS Marketing Science Doctoral Consortium Fellow, Duke University June 2020
• Doctoral Fellowship, Simon Business School, University of Rochester 2016-present

Grants

• Center of Excellence in Data Science Program, funded by New York State Department of Economic Development's Division of Science, Technology, and Innovation (NYSTAR) 2020, 2021

Teaching

Simon Business School, University of Rochester

• Instructor
  CIRC Workshop for Simon Ph.D. Students

  Jan 2019, Aug 2020, Sep 2021

  Developed the curriculum from scratch, covering how to use High-Performance Computing (HPC) Linux clusters, project workflows, etc.
• Lab Instructor
  GBA 464, Programming for Analytics (Evaluation: 4.0/5.0, Enrollment: 144)

  Fall A 2020, Fall 2018
• Teaching Assistant
  MKT 440, Pricing Analytics

  Spring 2018, Spring 2019, Spring 2020

  MKT 426, Marketing Analytics Using R

  Winter 2018, Fall B 2019

  TEM 401, Economics, Marketing, and Strategy for Entrepreneurs

  Fall 2017, Fall 2018

Professional Services

• Session Chair, Marketing Science Conference 2021
  June 2021
• Session Chair, Marketing Science Conference 2020
  June 2020
• Referee, Journal of Advertising Research

Volunteer Experience

• Volunteer, 2024 Google CS Edu Week at Belle Air Elementary School, San Bruno
  November 2024