Hongpeng Fu Mobile: +(1-929******* / Email: ad0mju@r.postjobfree.com

EDUCATION

• Northeastern University Sept 2023 - Jun 2025

M.S. in Computer Science (expected)

• Peking University

M.S. in Geographic Information System Sep 2019 - Jun 2022 B.E. in Urban and Rural Planning Sep 2014 - Jun 2019 SKILLS AND SELECTED COURSES

Programming Languages Java, Python, SQL, C++ R, JavaScript, HTML/CSS Framework & Tools SpringBoot, SpringCloud, Flask, MyBatis, Node.js,React, Axios, MySQL, MongoDB, Redis, Maven, Jenkins, Docker, Postman, Pandas, Numpy Jupyter Notebooks PyCharm Selected Courses Data Structures Algorithms, Computer Architecture, Big Data, Object-Oriented Design PROFESSIONAL EXPERIENCE

• JP Morgan - Software Development Virtual Internship Jan 2023 - April 2023

– Developed a Stock Trading System, including login, product collections, search, and order, using SpringCloud to build a distributed microservices architecture and React to create interactive and dynamic user interfaces.

– Utilized Redis to store stock market and user trading data, reducing database query time by 50%.

– Used RabbitMQ for asynchronous communication between services, reducing Average Response Time by 14.3% and increasing Throughput by 9.5% during testing with Apache JMeter.

– Built and containerized applications using Docker, and deployed them on the cloud with AWS.

• Geely - Software Development Engineer, Map Technology Department Jul 2022 - Dec 2022

– Designed and developed the model-based Estimated Time of Arrival (ETA) service for dynamic route planning, which considered traffic, weather, and historical data, increasing accuracy from 86.2% to 90.5%.

– Implemented the ETA engine by storing offline ETA data using Redis and updated it daily, and storing real-time data in Hive and Redis and updated it hourly, reducing the data cache miss from 1500/month to 245/month and achieving a 15% increase in accuracy.

– Deployed the LSTM model of ETA engine on Spark, achieving a latency of 0.5s for data retrieval.

– Developed the ETA Data Annotation service using SpringBoot, including information querying, metric cal- culation, and accuracy comparison with competitors, reducing over 3500 engineering hours.

• Meituan - Software Development Engineer Intern of In-store Dining Business Jan 2022 - May 2022

– Led the development of the Restaurant Recommendation System, utilizing Flask for the rating, review, and personalized recommendation features, and React for the user interface such as package information and menu display, reaching over 1000,000 daily active users.

– Used MySQL to store the information about restaurants and menus and used Hbase to store images.

– Deployed the Spark and Spark Streaming computing modules on the YARN cluster for offline and online recommendation computations, requests processing capacity reaching over 100,0000/s.

– Utilized Kafka message queue for sending online recommendation computation tasks, achieved a throughput over 10,0000/s with a latency of less than 10 ms.

PROJECT EXPERIENCE

• Classification and rating for park services - Peking Univ. Oct 2021 - Jun 2022

– Developed an AI-based park issue detection system for urban planners, saving over $10,000 annually.

– Participated in developing an automatic rating model using Python and TensorFlow to predict park visitors’ interests and preferences, resulting in 3500 increased visitors to less popular parks.

– Used the React, Echarts, and Axios to create interactive charts and maps to display data, and used Redux to manage application state and ensure consistency across components, improving user satisfaction by 30%.

• Prediction and visualization for public services -Turenscape May 2020 - Sept 2020

– Predicted and visualized public services using data mining and machine learning, achieving 81.7% accuracy.

– Developed the backend using Flask, creating RESTful APIs to handle data requests and responses.

– Implemented a MongoDB database to store and manage the data, designing the schema and optimizing queries for efficient data retrieval.

– Configured the server environment using Docker, and deployed the system to a cloud server using AliCloud.

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