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Senior Applied Science Manager @ Amazon Rufus | Alignment, LLM, GenAI
With over ten years of work experience in applied science and machine learning, I am a passionate and innovative leader who strives to create impactful solutions for complex problems. My field of expertise is large-scale multi-modal multi-task multi-lingual multi-entity multi-locale models (LLM) that serve various domains and applications. I am currently an Applied Science Manager in Search at Amazon, where I lead a team to build LLM for the e-commerce and revamp customer shopping experiences. In my previous role as the Applied Science Manager of Core Modeling at Search M5 at Amazon, I managed the M5 Core Modeling team, which built super large models that served the entire company. I was responsible for setting the vision, strategy, and roadmap for the team, as well as overseeing the research, development, and deployment of the models. I also collaborated with multiple stakeholders across different teams and organizations, such as Alexa, AWS, and Prime Video, to understand their needs and provide them with customized solutions. Some of the skills that I used and developed in this role include large model, search, tech leadership, Python, and deep learning.
Auburn University
PhD, Electrical Engineering
N/A – Present
Auburn University
Master's degree, Probability and Statistics
N/A – Present
Tsinghua University
Master
N/A – Present
University of Electronic Science and Technology of China
BS
N/A – Present
Amazon
Senior Applied Science Manager at Rufus
January 1, 2024 – Present
Amazon
Applied Science Manager at Rufus
January 1, 2023 – January 1, 2024
Amazon
Applied Science Manager of Core Modeling at Search M5
March 1, 2021 – January 1, 2023
Amazon
Applied Scientist | Senior Applied Scientist | Applied Science Manager
February 1, 2018 – March 1, 2021
Amazon
Applied Scientist
January 1, 2016 – February 1, 2018
NEC Laboratories America
Summer Intern
May 1, 2013 – August 1, 2013
Princeton, NJ
Auburn University
Research Assistant
January 1, 2011 – December 1, 2015
Tsinghua University
Research Associate
August 1, 2010 – December 1, 2010
Beijing, China
Tsinghua University
Research Assistant
August 1, 2007 – July 1, 2010
Beijing, China
Hyperparameter optimization as a service for Alexa scientists
October 1, 2019 – Present
Internal hackathon. Won first prize of usefulness and second prize of novelty.
GlobalUrbanDataFest: Smart Traffic Grid
February 1, 2015 – Present
This project was for the Global Datafest Hackathon in Auburn, AL the weekend of February 20 - 22. We used video processing algorithms to determine the traffic level at Toomer's Corner, the busiest intersection in Auburn, AL. We placed 3rd out of 8 teams. My role was the frontend website, which is mostly Javascript, and relies heavily on the Google Maps API. Code: github.com/global-urban-datafest/Auburn-smartTrafficGrid Prize: http://www.global.datafest.net/cities/auburn-us
User Clustering and Scheduling for 5G Massive MIMO System with Two-Stage Precoding
October 1, 2013 – August 1, 2014
To enhance the system throughput of future 5G massive MIMO system, we first examined K-means clustering and chordal distance. PCA was conducted as well. Then we proposed three novel similarity measures (distance measures) based on weighted likelihood, subspace projection, and Fubini-Study respectively. We also proposed to adopt hierarchical clustering and K-Medoids clustering. A greedy user scheduling algorithm was also proposed to further enhance the system throughput once the user clusters were formed.
Statistical Learning
Stanford University
June 24, 2026 – Present
An Introduction to Interactive Programming in Python
Coursera
June 24, 2026 – Present
Programming for Everybody (Python)
Coursera
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera
June 24, 2026 – Present
R Programming
Coursera
June 24, 2026 – Present
Exploratory Data Analysis
Coursera
June 24, 2026 – Present
Reproducible Research
Coursera
June 24, 2026 – Present
Statistical Inference
Coursera
June 24, 2026 – Present
Regression Models
Coursera
June 24, 2026 – Present
Linear and Integer Programming
Coursera
June 24, 2026 – Present
Convex Optimization
Stanford University
June 24, 2026 – Present
Algorithms: Design and Analysis, Part 1
Coursera
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Introduction to Computer Science and Programming Using Python
edX
June 24, 2026 – Present
The Analytics Edge
edX
June 24, 2026 – Present
Introduction to Big Data with Apache Spark
edX
June 24, 2026 – Present
Big Data XSeries
edX
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Scalable Machine Learning
edX
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background, particularly their long tenure at Amazon in various science and management roles, suggests a strong fit for a data-driven, results-oriented culture. Their involvement in hackathons and academic research projects demonstrates intellectual curiosity and a proactive approach to problem-solving. The breadth of their certifications in machine learning, deep learning, big data, and statistics aligns well with a culture that values continuous learning and technical depth. The target role of 'Data Analyst' might be a slight mismatch given their senior Applied Science Manager experience, which typically involves more advanced modeling and strategic leadership than traditional data analysis. However, their foundational skills are highly relevant.
Soft Skills & Operational Fit
The candidate's extensive experience at Amazon in various Applied Science and management roles suggests strong leadership, problem-solving, and operational skills in a fast-paced, large-scale environment. Their involvement in founding teams (Rufus, M5) indicates initiative and adaptability. The project descriptions, while brief, highlight participation in hackathons and research, suggesting a collaborative and innovative mindset. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is limited.