About

I’m an AI researcher working on autonomous vehicles, data science, and machine learning at Cruise.

Education

• B.Sc. in Mathematics, First Class Honours, The Chinese University of Hong Kong, 2015
• B.Eng. in Information Engineering, First Class Honours, The Chinese University of Hong Kong, 2015
• Ph.D. in Electrical Engineering, University of California, San Diego, 2021
  I was fortunate to be supervised by Prof. Alon Orlitsky

Work Experience

• Senior Applied Research Scientist, Cruise LLC, Jul 2021 - Present
  • Research and develop machine learning solutions and algorithms for autonomous vehicles
• Graduate Student Researcher, UC San Diego, Sep 2016 - Jun 2021
  • Research on machine learning and algorithm design

• Applied Scientist Intern, Amazon Web Services (AWS), Jun 2020 - Sep 2020

• Graduate Teaching Assistant, UC San Diego, Sep 2019 - Dec 2019
  • ECE 225A: Probability and Statistics for Data Science

• Summer Research Intern, Baidu USA, Jun 2019 - Sep 2019

• Work Placement Trainee, HSBC, Jul 2014 - Jun 2015

Interests

Statistics, Machine Learning, Optimization Theory, Randomized Algorithms, Information Theory, Data Mining

Academic Services

• Conference reviewer and/or PC member for ICML, NeurIPS, COLT, ICLR, STOC, FOCS, SODA, AIStats, AAAI, UAI, APPROX, ISIT, ITML since 2019
• Journal reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Transactions on Machine Learning Research (TMLR), IEEE Transactions on Signal Processing (TSP), Communications in Information and Systems (CIS), IEEE Transactions on Information Theory (TIT)

Selected Publications

• Yi Hao and Alon Orlitsky. Compressed Maximum Likelihood, International Conference on Machine Learning (ICML) 2021, [paper].
• Yi Hao. Competitive and Universal Learning, eScholarship - UC San Diego, 2021, [thesis].
• Yi Hao and Ping Li. Optimal Prediction of the Number of Unseen Species with Multiplicity, Neural Information Processing Systems (NeurIPS) 2020 (Oral spotlight), [paper].
• Yi Hao and Alon Orlitsky. Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions, Neural Information Processing Systems (NeurIPS) 2020, [paper].
• Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar. SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm, Neural Information Processing Systems (NeurIPS) 2020, [paper].
• Yi Hao and Alon Orlitsky. Bessel Smoothing and Multi-Distribution Property Estimation, Conference on Learning Theory 2020, [paper].
• Yi Hao and Alon Orlitsky. Data Amplification: Instance-Optimal Property Estimation, International Conference on Machine Learning (ICML) 2020, [paper].
• Yi Hao and Alon Orlitsky. The Broad Optimality of Profile Maximum Likelihood, Neural Information Processing Systems (NeurIPS) 2019 (Oral spotlight), [paper].
• Yi Hao and Alon Orlitsky. Unified Sample-Optimal Property Estimation in Near-Linear Time, Neural Information Processing Systems (NeurIPS) 2019, [paper].
• Yi Hao and Alon Orlitsky. Doubly Competitive Distribution Estimation, International Conference on Machine Learning (ICML) 2019 (Oral), [paper].
• Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu. Data Amplification: A Unified and Competitive Approach to Property Estimation, Neural Information Processing Systems (NeurIPS) 2018, [paper].
• Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati. On Learning Markov Chains, Neural Information Processing Systems (NeurIPS) 2018, [paper].
• Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar. Maxing and Ranking with Few Assumptions, Neural Information Processing Systems (NeurIPS) 2017, [paper].