Goals
I build high-performing ML systems to solve real-world problems.
Work Experience
2023 - Present | Senior Machine Learning Engineer, Uber
Applied AI Research on multimodality data.
Develop Uber’s in-house pre-trained Large Language Models.
2023 - Present | Part-time Research Engineer, Stanford CRFM
Training and evaluate large language models with TPU and Jax.
Contributor to RedPajama-1T dataset and models.
2018 - 2023 | Senior Machine Learning Engineer, Landing AI
Developing LandingLens, an end-to-end MLOps platform for computer vision applications. I’ve led the work on implementing object detection and semantic segmentation models, developing AutoML techniques, managing edge device deployment, and improving error analysis at evaluation.
Working directly with more than 10 enterprise customers in North America, Europe, and Asia to help them build successful ML applications. I develop playbooks and documentations to get customers set up for long-term success.
Provide technical mentorship for engineers on ML, computer vision, cameras, and software development.
Working directly with Andrew Ng to develop and advocate the Data-Centric ML methodology to the global ML community.
2018 - 2018 | Machine Learning Engineer, Shopify
Image Search and Personalization in production
End-to-End data science support for a fast-growing photo marketplace (Shopify's Burst), from setting up data infrastructure to algorithm implementation and product analytics
Machine Learning research: publication in ACM RecSys 2018
2016 - 2018 | Machine Learning Researcher, University of Toronto
Published a paper in International Joint Conference of Artificial Intelligence (IJCAI 2017) on non-linear planning with Deep Net Transition Models as a co-author.
Published an Honored Thesis on the Recommender System.
Implemented Deep Reinforcement Learning in large scale system planning.
Implemented and evaluated the performance of anomaly detectors with Deep Autoencoder, PCA, and RPCA.
Supervised by Professor Scott Sanner in the Data-Driven Decision Making Lab.
2017 | Research projects at Mozilla Firefox and 500px
Develop machine learning systems to recommend 500px users with photos that they will most likely to enjoy with.
Developed an end-to-end user feedback analytics engine for Mozilla Firefox team to process tens of thousands of user feedbacks in real time, with multiple components including translation, sentiment analysis, topic categorization, and dashboard visualization.
Education
MS Computer Science
Computer vision, generative models, robotics, and natural language processing.
BASc Industrial Engineering
Focus on Operations Research and Machine Learning.
Publication on IJCAI and ACM RecSys.
Activities and societies:
EXCITE2014 Entrepreneurship Conference
EXCITE2015 Healthcare Industry Conference
ACE2015 Career Fair