Yuyan Wang's Homepage

Hi! I'm Yuyan Wang.

I am an Assistant Professor of Marketing at the Stanford Graduate School of Business. My research lies in the intersection of marketing, machine learning, and statistics. I am interested in understanding and improving long-term consumer experience on personalization platforms and designing machine learning solutions for recommender systems and personalization use cases. The insights and the solution that I developed provided managerial implications for several personalization products at Google and Uber, where multiple of my works have been deployed globally and generated significant business impact.

I received my Ph.D. in statistics from the Department of Operations Research & Financial Engineering at Princeton University, where I was fortunate to be advised by Prof. Jianqing Fan. Before Princeton, I received my Bachelor’s degree in statistics from the Special Class for the Gifted Young at University of Science and Technology of China (USTC). Please feel free to check out my CV, research, teaching/mentorship, Google Scholar, Github, blogs (in Chinese), or contact me.

Learn about what I do

Research

My research focuses on understanding and improving the long-term values and societal impacts of real-world personalization systems. I use a variety of methods including deep learning, reinforcement learning, high-dimensional statistical modeling, causal inference, online A/B test and big data analytics.

Journal Publications

Embracing the Blessing of Dimensionality in Factor Models.
Quefeng Li, Guang Cheng, Jianqing Fan, Yuyan Wang.
Journal of the American Statistical Association (JASA), 113.521 (2018): 380-389.
Top-tier journal in Statistics.
A statistical investigation of the dependence of tropical cyclone intensity change on the surrounding environment.
Ning Lin, Renzhi Jing, Yuyan Wang, Emmi Yonekura, Jianqing Fan, and Lingzhou Xue.
Monthly Weather Review - American Meteorological Society (AMS), 145 (7), 2813-2831.
Top-tier journal in Atmospheric Science.
Estimation of High-Dimensional Mean Regression in Absence of Symmetry and Light-tail Assumptions.
Jianqing Fan, Quefeng Li, Yuyan Wang (alphabetical order).
Journal of the Royal Statistical Society: Series B (Statistical Methodology) (JRSS-B) 79.1 (2017): 247-265.
Top-tier journal in Statistics.

Conference Publications

Latent User Intent Modeling for Sequential Recommenders.
Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen.
Proceedings of the ACM Web Conference 2023 (WWW / theWebConf 2023).
Full paper with oral presentation; Acceptance rate: 19.8%.
Towards Companion Recommendation Systems.
Konstantina Christakopoulou, Yuyan Wang, Ed Chi, Minmin Chen.
HCAI @ NeurIPS '22.
Surrogate for Long-Term User Experience in Recommender Systems.
Yuyan Wang, Mohit Sharma, Sriraj Badam, Can Xu, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, Minmin Chen.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2022).
Full paper with oral presentation; Acceptance rate: 15.0%. Short version accepted to BayLearn 2022.
Recommending for a Multi-Sided Marketplace with Heterogeneous Contents.
Yuyan Wang, Long Tao, Xian Xing Zhang.
Sixteenth ACM Conference on Recommender Systems (Recsys 2022).
Top conference for Recommender Systems. Extended abstract with oral presentation; Acceptance rate: 28.0%.
Can Small Heads Help? Understanding and Improving Multi-Task Generalization.
Yuyan Wang, Mohit Sharma, Sriraj Badam, Can Xu, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, Minmin Chen.
Proceedings of the ACM Web Conference 2022 (WWW / theWebConf 2022).
Full paper with oral presentation; Acceptance rate: 17.7%.
Learning to Augment for Casual User Recommendation.
Jianling Wang, Elaine Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen.
Proceedings of the ACM Web Conference 2022 (WWW / theWebConf 2022).
Full paper with oral presentation; Acceptance rate: 17.7%.
Bias-robust Integration of Observational and Experimental Estimators. [Journal draft].
Michael Oberst, Alexander D’Amour, Minmin Chen, Yuyan Wang, David Sontag, Steve Yadlowsky.
Oral presentation at the American Causal Inference Conference (ACIC 2022). Journal submission in preparation.
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.
Yuyan Wang, Xuezhi Wang, Alex Beutel, Flabien Prost, Jilin Chen, Ed H. Chi.
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2021).
Full paper with oral presentation; Acceptance rate: 15.4%. Short version accepted to BayLearn 2021.
Values of User Exploration in Recommender Systems.
Minmin Chen, Yuyan Wang, Can Xu, Elaine Le, Mohit Sharma, Lee Richardson, Su-Lin Wu, Ed Chi.
Fifteenth ACM Conference on Recommender Systems (Recsys 2021).
Top conference for Recommender Systems. Full paper with oral presentation; Acceptance rate: 18.4%.
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems.
Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed Chi, Claire Cui.
Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021).
Full paper with oral presentation; Acceptance rate: 18.6%.
Multi-layer Optimization for a Multi-sided Network Service.
Yuyan Wang, Xian Xing Zhang, Isaac Suyu Liu, Yuanchi Ning, Chen Peng.
U.S. Patent No. 11,127,066. Washington, DC: U.S. Patent and Trademark Office.
Multi-layer Optimization for a Multi-sided Network Service.
Xian Xing Zhang, Sidney Zhang, Yuyan Wang, Mihir Gogate, Yuanchi Ning, Chen Peng, Isaac Liu, Calvin Lee.
U.S. Patent No. 11,157,579. Washington, DC: U.S. Patent and Trademark Office.
Food Discovery with Uber Eats: Recommending for the Marketplace.
Yuyan Wang, Yuanchi Ning, Isaac Liu, and Xian Xing Zhang.
Uber Engineering Blog.
Selected as "Top 10 machine learning articles of the month" (0.7%) by an independent publisher.

Working Papers

Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach.
Yuyan Wang, Long Tao, Xian Xing Zhang.
Major revision (1st round) at Marketing Science. Best Paper Award at CIST 2022.
Modeling hierarchical User Exploration for Improving Long-Term Performance in Recommender Systems.
Pan Li, Yuyan Wang, Ed Chi, Minmin Chen.
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations.
Pan Li, Yuyan Wang, Ed Chi, Minmin Chen.

Invited Talks & Conference Presentations

“Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach.”
- UC Berkeley Haas School of Business. Mar 2024
- University of Arizona Wieland Speaker Series. Feb 2024.
- Warrington College of Business, University of Florida. Jan 2024.
- Peking University Guanghua School of Management. Dec 2023.
- UChicago Booth Marketing Seminar. Nov 2023.
- USC Marshall Statistics Seminar. Nov 2023.
- AIBA Workshop, Temple University (virtual). Oct 2023.
- Reading Group Sequence on Interference & Marketplace, Stanford GSB. Sept 2023.
- KDD 2023 “AI for Open Society” Day Invited Talk. Aug 2023 (scheduled).
- SICS, Haas School of Business, UC Berkeley. June 2023.
- School of Business, University of California, Riverside. Feb 2023.
- Coupang, Inc. May 2023.
- Stern School of Business, New York University. Nov 2022.
- The Wharton School, University of Pennsylvania. Nov 2022.
- Stanford Graduate School of Business. Nov 2022.
- Kellogg School of Management, Northwestern University. Oct 2022.
- 2022 INFORMS Annual Meeting. October 2022.
- Conference on Information Systems and Technology (CIST) 2022 (Best Paper Award).
- SC Johnson College of Business, Cornell University (virtual). Oct 2022.
- Naveen Jindal School of Management, UT Dallas. Oct 2022.
- 16th ACM Conference on Recommender Systems (Recsys 2022). Sept 2022.
- HKUST Business school (virtual), Sept 2022.
- CUHK Business school (virtual), Sept 2022.
- ISMS Marketing Science Conference 2022 (virtual). June 2022.
“Surrogate for Long-Term User Experience in Recommender Systems.”
- Netflix Research Seminar Talk. Oct 2023.
- DataFun Summit 2023 (virtual). August 2023.
- Bay Area Machine Learning Symposium (BayLearn) 2022. October 2022.
- Google Search Tech Talk. September 2022.
- 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. August 2022.
- Google Research Brain Dump. February 2022.
- Google Research Conference 2021. October 2021.
- Google Research Reinforcement Learning Workshop. July 2021.
“Can Small Heads Help? Understanding and Improving Multi-Task Generalization.”
- Snap Inc Tech Talks. November 2022.
- ACM The Web Conference 2022. April 2022.
“User Intent Modeling in Recommender Systems.”
- CONSEQUENCES+REVEAL '22: Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning,16th ACM Conference on Recommender Systems (Recsys 2022 Workshop). September 2022 (Invited Speaker and Panelist).
“Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.”
- Mays Business School, Texas A&M University. July 2022.
- ISMS Marketing Science Conference 2022 (virtual). June 2022.
- Bay Area Machine Learning Symposium (BayLearn) 2021. October 2021.
- 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. August 2021.
- Faire Wholesale, Inc. July 2021.
“Food Discovery with Uber Eats: Recommending for the Marketplace.”
- SigOpt. August 2019.
- Facebook Research. June 2019.
- Airbnb. June 2019.
“Uber Eats Restaurant Ranking and Recommendation.”
- Moving the World with Data Meetup. San Francisco, CA, October 2018.
- AI Applications @ Uber Eats Meetup. San Francisco, CA, October 2017.
“Robust Approximate Lasso for High-Dimensional Regression.”
- IBM Thomas J. Watson Research Center. February 2016.
- Yale University. September 2015.
- 2015 Joint Statistical Meetings (JSM), August 2015.
- NSF Workshop for Empr Process and Mod Stat Decision Theory. May 2015.
“Bayesian time series for online query frequency prediction.”
- Internet Services & Research Center, Microsoft Research, August 2015.

Teaching/Mentorship Experience

While not technically a teacher for the past few years, I was never away from teaching as I always enjoy passing on my knowledge and inspiring people whenever I can. Here is a list of my teaching and mentorship experiences.

Mentorship

Mentor for Undergraduate Consortium at KDD 2022 (KDD-UC). June 2022 - present.
CS Research Menshorship Program mentor for three grad & undergrad students from historically marginalized groups. November 2021 - June 2022.
Quote from mentee: "I was lucky to be assigned as a mentee under Yuyan. She was super enthusiastic and proactive in giving me the all the help I needed, be it offering advices on job hunting; insights into how companies work internally or tips on enhancing my resume. I can truly vouch for her great team leadership and soft skills. She was very understanding of the challenges each of us faced and guided us through them. I am very grateful to have gotten Yuyan as my mentor!"
Mentor for one student researcher and two interns at Google Brain. May 2021 – present.
Mentor for two full-time team member and one intern at Uber. June 2017 - September 2019.

Guest Lectures / Tutorials

Recommender Systems: Algorithms & Applications. School of Management, Yale University. Apr 2023.
Recommender Systems: Algorithms & Applications. USC Marshall School of Business. Feb 2023.
A Gentle Introduction to Recommender Systems. Stern School of Business, New York University. June 2022.
Received notes from the students and professors in the class: "I loved your guest lecture today." "I wanted to say thanks, on behalf of all of us in Pan's class today, for speaking with us on your works/SOTAs on Reinforcement Learning & Recommender Systems."
A Gentle Introduction to Recommender Systems. Heinz College, Carnegie Mellon University. October 2021.
Received 10+ thank-you notes from the students after the class: "Thanks for giving this great lecture; I’ve learned a lot about RecSys and insight into your work at Google." "It was really helpful and the slides were amazing!"
Experimentation and A/B Testing Best Practices. Uber Technologies. October 2018.

Assistant Instructor at Princeton University

ORF 504: Financial Econometrics. Spring 2016.
ORF 245: Fundamentals of Statistics. Spring 2014, Spring 2015, Fall 2015.
ORF 405: Regression and Applied Time Series Analysis. Fall 2013, Fall 2014.

Some comments from students:

"I think Yuyan is great! She is my favorite TA because she goes over concepts step by step and is always willing to guide me in the right direction!"

"Yuyan is a great preceptor. Clear and concise. She gave great homework help and made herself available to students."

"Yuyan makes things a lot clearer to grasp."

"Very receptive to questions, very nice, and good at explaining tricky problems."

"She was great! Her precepts were very easy to follow and she explained everything well."