Biography

YaGuang Li is a Senior Staff Research Scientist at Google DeepMind (formerly Google Brain). He co-led the finetuning effort of Gemini 1.5 and Gemini 1.0 for Gemini Advanced. He is also core contributor of LaMDA, PaLM-2 working on pre-training, instruction tuning and improving serving efficiency. He earned his Ph.D. degree in Computer Science at the University of Southern California advised by Prof. Cyrus Shahabi and Prof. Yan Liu in 2019. Before that, He received his master's degree in Computer Science from Institute of Software in University of Chinese Academy of Sciences in 2014. His primary research interests lie in machine learning, spatiotemporal prediction and recommendation, deep learning on graphs with applications in transportation. More information is available in his curriculum vitae.

Publications

Preprints

• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
  Gemini Team, Google, YaGuang Li (Lead, Finetuning - Text)
  Preprint, 2024
  
• Gemini: A Family of Highly Capable Multimodal Models
  Gemini Team, Google, YaGuang Li (Co-Lead, Gemini App)
  Preprint, 2023
  
• PaLM-2 Technical Report
  Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen,
  Eric Chu, Jonathan H Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder,
  Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan Botha, James Bradbury, Siddhartha Brahma,
  Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani,
  Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vlad Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann,
  Lucas Gonzalez, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, Andrea Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah,
  Matthew Jagielski, Wenhao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Music Li, Wei Li,
  YaGuang Li, Jian Li, Hyeontaek Lim, Hanzhao Lin, Zhongtao Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem,
  Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter,
  Parker Riley, Alex Castro Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose Slone, Daniel Smilkov, David R So, Daniel Sohn, Simon Tokumine,
  Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Kelvin Xu, Yunhan Xu, Linting Xue,
  Pengcheng Yin, Jiahui Yu, Qiao Zhang, Steven Zheng, Ce Zheng, Weikang Zhou, Denny Zhou, Slav Petrov, Yonghui Wu
  arXiv Preprint, 2023
  
• LaMDA: Language models for dialog applications
  Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du,
  YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin,
  Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch,
  Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke,
  Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee,
  Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas,
  Claire Cui, Marian Croak, Ed Chi, Quoc Le
  arXiv Preprint, 2022
  

Refereed Conference Papers

• HyperPrompt: Prompt-based Task-Conditioning of Transformers
  Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H Chi
  International Conference on Machine Learning, (ICML) 2022
  
• Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
  Hongzhi Shi, Quanming Yao, Qi Guo, YaGuang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu
  IEEE International Conference on Data Engineering, (ICDE), 2020 (Research Track, Short Paper)
  
• AViC: A Cache for Adaptive Bitrate Video
  Zahaib Akhtar, YaGuang Li, Ramesh Govindan, Emir Halepovic, Shuai Hao, Yan Liu, Subhabrata Sen,
  The 15th International Conference on emerging Networking EXperiments and Technologies, (CoNEXT), 2019
  
• DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis
  Mingxuan Yue, YaGuang Li, Haoze Yang, Ritesh Ahuja, Yao-yi Chiang, and Cyrus Shahabi
  IEEE International Conference on Big Data, (IEEE Bigdata), 2019
  
• CoSTCo: A Neural Tensor Completion Model for Sparse Tensors
  Hanpeng Liu, YaGuang Li, Michael Tsang, Yan Liu
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (KDD), 2019 (Research Track, Oral Presentation)
  [ Paper] [ Video] [ Code]
• Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
  YaGuang Li*, Xu Geng*, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye and Yan Liu (*Equal Contribution)
  The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019 (Oral Presentation)
  [ Paper] [ Poster] [ Slides]
• Multi-task Representation Learning for Travel Time Estimation
  YaGuang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye and Yan Liu
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (KDD), 2018 (Research Track)
  [ Paper] [ Video] [ Poster]
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
  YaGuang Li, Rose Yu, Cyrus Shahabi and Yan Liu
  International Conference on Learning Representations, (ICLR), 2018
  [ Paper] [ Code] [ Slides] [ Poster]
• Exploiting Spatiotemporal Patterns for Accurate Air Quality Forecasting using Deep Learning
  Yijun Lin, Nikhit Mago, Yu Gao, YaGuang Li, Yao-Yi Chiang, José Luis Ambite and Cyrus Shahabi.
  ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), 2018
  [ Paper]
• Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting
  YaGuang Li*, Rose Yu*, Cyrus Shahabi, Ugur Demiryurek and Yan Liu (*Equal Contribution)
  Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), 2017 (Best paper Nomination)
  [ Paper]
• PaRE: A System for Personalized Route Guidance
  YaGuang Li, Han Su, Ugur Demiryurek, Bolong Zheng, Tieke He, and Cyrus Shahabi.
  Proceedings of the 26th International Conference on World Wide Web (WWW), 2017
  [ Paper]
• PerNav: A Route Summarization Framework for Personalized Navigation
  YaGuang Li, Han Su, Ugur Demiryurek, Bolong Zheng, Kai Zeng, and Cyrus Shahabi.
  ACM SIGMOD International Conference on Management of Data (demonstration) (SIGMOD), 2016
  [ Paper]
• Price-aware Real-time Ridesharing at Scale - An Auction-based Approach
  Mohammad Asghari, Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, YaGuang Li.
  International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS), 2016
  [ Paper]
• Towards Fast and Accurate Solutions to Vehicle Routing in a Large-Scale and Dynamic Environment
  YaGuang Li, Dingxiong Deng, Ugur Demiryurek, Cyrus Shahabi, Siva Ravada.
  14th International Symposium on Spatial and Temporal Databases (SSTD), 2015
  [ Paper] [ Demonstration] [ Slides]
• On Efficient Map-matching According to Intersections You Pass By
  YaGuang Li, Chengfei Liu, Kuien Liu, JiaJie Xu, Fengcheng He, Zhiming Ding.
  International Conference on Database and Expert Systems Applications (DEXA), 2013
  [ Paper] [ ACM Dataset]
• MOIR/uots: Trip recommendation with user oriented trajectory search
  Kuien Liu, Bin Yang, Shuo Shang, YaGuang Li, Zhiming Ding.
  International Conference on Mobile Data Management (MDM), 2013
  [ Paper]
• Effective Map-matching on the Most Simplified Road Network
  Kuien Liu, YaGuang Li, Fengcheng He, Jiajie Xu, Zhiming Ding.
  International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS) 2012
  [ Paper] [ Code] [ Slides] [ Poster]

Journal Papers

• A Brief Overview of Machine Learning Methods for Short-term Traffic Forecasting and Future Directions
  YaGuang Li, Cyrus Shahabi
  SIGSPATIAL Special, 2018
  [ Paper]
• Network-Matched Trajectory-Based Moving-Object Database: Models and Applications
  Zhiming Ding, Bin Yang, Ralf Hartmut Guting, YaGuang Li
  IEEE Transactions on Intelligent Transportation Systems (TITS), 2015
  [ Paper]

Patent

• Regression and time series forecasting
  Rajat Sen, Shuxin NIE, Yaguang LI, Abhimanyu Das, Nicolas LOEFF, Ananda Theertha Suresh, Pranjal AWASTHI, Biswajit PARIA
  Patent, WO2022251857A1, Issued 2022
  
• Adaptive bitrate video cache
  Shuai HAO, Subhabrata Sen, Emir Halepovic, Zahaib Akhtar, Ramesh Govindan, Yaguang Li.
  US Patent, US20210185368A1, Issued 2021
  
• Parallel Data Processing Method based on Distributed Structure
  Haoming Guo, Zhiming Ding, Kuien Liu, Jiajie Xu, Huaiye Xu, YaGuang Li, Tianwei Zhang.
  CN Patent, CN103412897B, Issued 2017
  

Workshop Papers

• Structure-informed Graph Auto-encoder for Relational Inference and Simulation
  YaGuang Li, Chuizheng Meng, Cyrus Shahabi and Yan Liu
  ICML Workshops on Learning and Reasoning with Graph-Structured Representations, 2019
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
  YaGuang Li, Rose Yu, Cyrus Shahabi and Yan Liu
  NIPS Time Series Workshop, 2017 (Oral Presentation)

Honors and Awards

• Annenberg Graduate Ph.D. Fellowship
  University of Southern California, 2014 - 2019
  
• Outstanding Graduate of University of Chinese Academy of Sciences
  Beijing, China, 2014
  
• Pivot of Merit Student of University of Chinese Academy of Sciences
  Beijing, China, 2014
  
• National Scholarship
  Beijing, China, 2011, 2014
  
• Outstanding University Graduate of Beijing Province
  Beijing, China, 2011
  

Working Experience

• Google DeepMind, Brain team
  Staff Research Engineer, Mountain View, CA, US, 2019.08 - Present
  
• Facebook Inc. Applied Machine Learning
  Software Engineering Intern, Menlo Park, US, 2017.5 - 2017.8
  
• Google Inc.
  Software Engineering Intern, New York, US, 2016.5 - 2016.8
  
• Google Inc.
  Software Engineering Intern, Montreal, CA, 2015.5 - 2015.8
  

Academic Service

• Web Chair: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
• Co-organizer: SIGKDD Workshop on Mining and Learning from Time Series (MiLeTS), 2018, 2019, 2020, 2021
• Web Chair: International Conference on Web Search and Data Mining (WSDM), 2018
• Conference Reviewer: ICML 2019, ICLR 2020, IJCAI 2018, VLDB 2016-2017, SIGMOD 2015
• Journal Reviewer: Transportation Research Part C, IEEE TKDE 2019, IEEE TITS 2016-2019, IEEE TIST 2019, IEEE ToWC 2019, GeoInformatica 2019, JCST 2019

Miscellaneous Links

• Google Scholar
  
• LinkedIn