Andy Cui Email & Phone Number

Waltham, Ma, Us

Southborough, Massachusetts, United States

- Built and maintained a predictive price forecast system for vehicles on CarGurus using AWS serverless architecture, enhancing market compatibility. Utilized AWS S3, Lambda, DynamoDB, Kinesis, CloudWatch, and SNS with Python and AWS CloudFormation for Infrastructure as Code (IaC).- Enabled real-time data monitoring with AWS Kinesis to detect market price discrepancies and trigger alerts, improving operational efficiency by 80% and enhancing real-time price adjustments by 50%.- Developed an AWS data lake pipeline in S3 with raw, cleansed, and application data layers, improving data access for the BI team by 50%.- Orchestrated workflows with AWS Step Functions and Glue Crawlers for ETL processes, integrating Glue Data Catalog and Athena for continuous data quality checks.- Automated alerts for pipeline failures with SNS and Lambda, reducing downtime by 30%. Created a schema change detection system to prevent data job errors, saving 60% in troubleshooting time.- Participated in Agile development, using Git for version control and Jira for task tracking, and implemented CI/CD with Bamboo.

Beijing, China

- Supported the lead instructor by documenting key course content and processes, organizing and tutoring on topics not fully covered in lectures to enhance students' understanding of interdisciplinary knowledge and learning frameworks.- Crafted 400-word summaries for each class on the course platform to aid in student review and engaged in follow-up office hours with students needing extra assistance to ensure their academic success.

Woburn, Massachusetts, United States

- Built and managed a robust AWS streaming data pipeline to automate biomedical data ingestion from multiple public databases into Kinesis Data Stream. This system processed over 500,000 data entries daily, using Lambda Functions for real-time data transfer and S3 and DynamoDB for efficient, scalable storage and retrieval. This architecture allowed seamless handling of high-volume data with minimized latency and reduced operational costs.- Configured and optimized a structured SQL database to integrate and process data from diverse biomedical sources. Implemented an efficient tag-processing system for enhanced search and retrieval operations, reducing data retrieval time by 80%. This improvement significantly boosted operational efficiency, making it easier to access and analyze critical information for downstream applications.- Applied advanced data matching algorithms and TF-IDF (Term Frequency-Inverse Document Frequency) techniques to perform data comparison and accurately identify potential client profiles with a 95% match rate. This methodology directly supported targeted marketing strategies by enabling precise identification of high-value clients within the biomedical field.- Enabled real-time notifications for data updates by setting up DynamoDB Streams to capture table modifications. Configured Lambda Functions to process these events and send automated alert emails through Amazon SES, enhancing response times by 30% and streamlining the data update workflow.- Utilized Python and graphical web scraping tools to gather and preprocess extensive biomedical datasets from various online resources. This preprocessing step ensured data quality and consistency, enabling deeper analysis and accurate results for research teams.

Omaha, Nebraska, United States

- Developed a customer demand analysis pipeline during the Covid-19 pandemic using AWS S3 for secure data storage and Lambda for serverless processing, combined with SQL and Python. Built and deployed a Random Forest model on AWS SageMaker to identify high-potential user profiles for conversion, achieving an accuracy of 98%. This automated approach saved considerable processing time and improved prediction accuracy for strategic marketing efforts.- Established a data ingestion and processing framework using AWS Glue to automate data ETL processes, enabling seamless integration of monthly customer market data from S3 into DynamoDB for structured storage. Employed SQL and Python to preprocess data and built a multiple regression model on SageMaker to predict revenue trends, achieving an 85% accuracy rate. This solution provided valuable insights for strategic decision-making regarding resource allocation and customer segmentation.- Analyzed user behavior patterns to inform UX design by employing Human-Computer Interaction (HCI) principles. Utilized Axure RP to create interactive software prototypes aligned with user behavior insights. Additionally, used AWS Amplify to deploy and test front-end prototypes rapidly, ensuring seamless integration with back-end data and enhancing user experience.- Constructed real-time monitoring dashboards using Tableau to track essential investment metrics, such as ROI (Return on Investment), growth rate, and portfolio balance. Integrated AWS Kinesis for real-time data streaming, enabling prompt anomaly detection and root cause analysis. This system provided stakeholders with actionable insights on data trends, supporting a 30% faster decision-making process for adjusting business strategies.- Set up AWS CloudFormation templates to manage and automate infrastructure provisioning, ensuring efficient resource management and scalability of the analysis system.

Zhengzhou, Henan, China

- Analyzed customer requirements to draft an activity diagram and outlined preliminary UML page functionality scenarios. Initiated the development of customized software (Web & App) tailored to customer needs.- Employed advanced SQL queries and Python to analyze over 50GB of market data on similar products, identifying customer usage patterns and business requirements.- Conducted exploratory data analysis on numerical, categorical, and time-series data using Matplotlib and Seaborn. Constructed multivariance Time Series Clustering model, DTW (Dynamic Time Warping) with Hierarchical clustering, on 120 features to group customers, segment market and described the clustering centroid by DBA (DTW Barycenter Averaging). - Trained market demand models, including Isolation Forest and Robust PCA (Principal Component Analysis), as well as Linear Models and Random Forests, to establish a system for identifying and responding to customers' potential requirements.- Constructed dashboards with Tableau to depict customer usage activities and submitted analytical reports to clients.

Lincoln, Nebraska, United States

- Aided freshmen in adjusting to introductory Computer Science courses and tutored current students in departmental material.- Supported the professor with teaching assignments, corrections, and lab work, guiding new computer science students through lab exercises and software usage.- Systematically addressed student inquiries, providing solutions and advice.

Yingkou, Liaoning

- Developed a curriculum plan in line with the level of local students according to the curriculum outline and content. - Analyzed the students' mastery of the course through the feedback of students in class and after class; and make corresponding changes to the course teaching plan.- Trained relevant lecturers and unified the teaching mode.

China, Dalian

- Organized on-campus after sales technology support events for Dell products.- Improved reports procedures and saved five hours of manual work every week.- Managed an average of ten requests daily, creating and updating data elements on the Dell Campus platform.

Computer Science, Artificial Intelligence, 3.2

Bachelor'S Degree, Computer Science, 3.0

Activities and Societies: The members of Chinese Scholars and Students Association of UNL

Bachelor'S Degree, Electrical And Electronics Engineering, 3.4