Lanxin ZHANG
Grinnell,USA
Summary
Dynamic data analyst with a strong foundation in R, statistics, and SQL, complemented by hands-on experience in big data technologies such as Hadoop and PySpark. Proven ability to leverage analytical skills to extract insights and drive data-informed decision-making. Skilled in transforming complex datasets into actionable strategies that enhance business performance. Committed to continuous learning and staying at the forefront of industry trends to deliver innovative solutions.
Overview
2
2
years of professional experience
Work History
Algorithm & Data Engineer Intern
Langchao Cooperate
Jinan, CN
07.2024 - 08.2024
- Implemented data engineering and data preprocessing pipelines for model training, including data cleaning and transformation to support business requirements
- Developed automated data cleaning processes using Python and SQL, optimizing large-scale data processing efficiency and workflow standardization
- Designed and applied simulation methods and big data analytics approaches to analyze and enhance system performance and applications
Data Analysis Intern
INSPUR Software Group Co., Ltd.
Jinan, CN
07.2022 - 08.2022
- Analyzed multiple business data sources and established standardized processing workflows; developed automated report templates to improve monthly data analysis efficiency and support management decisions
- Performed business unit data analysis using Excel for data mining and visualization; identified KPI fluctuation causes and proposed improvement solutions
- Facilitated project feasibility analysis and strategy assessment through systematic historical data analysis, assisting team in optimizing operational strategies and business processes
Education
Bachelor of Science - Physics
Grinnell College
Grinnell, IA, USA07-2025
Online Courses - undefined
Rio Salado College
Online07-2024
Master of Science - Data Science
Lehigh University
Bethlehem, PA09-2026
Skills
- Proficient in Python
- Interests: Hands-on Projects, Photography, Graphic Design
Timeline
Algorithm & Data Engineer Intern
Langchao Cooperate
07.2024 - 08.2024
Data Analysis Intern
INSPUR Software Group Co., Ltd.
07.2022 - 08.2022
Online Courses - undefined
Rio Salado College
Bachelor of Science - Physics
Grinnell College
Master of Science - Data Science
Lehigh University
PUBLICATION
Lanxin Zhang, Face Recognition and Model Analysis Using CNN Based on Self-Collected Information, CSIC 2023, ISSN: 2791-0130 (accepted)
RESEARCH EXPERIENCE
- Numerical Simulation and Visualization Analysis of Electromagnetic Field Distribution in Solenoids Based on Python
- Researcher, supervised by Prof. Tyler Grover, Grinnell College
- Grinnell, USA
- 11/2024-11/2024
- Developed a Python-based electromagnetic field numerical simulation program, achieving over 95% computational accuracy by implementing the Biot-Savart Law to calculate the magnetic field strength inside and outside the solenoid
- Implemented a 3D electromagnetic field visualization system using Matplotlib library to create an interactive interface, demonstrating the distribution and directional changes of magnetic field strength throughout the solenoid
- Analyzed the correlation between number of coil turns and electromagnetic field intensity, confirming a significant positive correlation (R^2 >0.95) through regression analysis, and discovered that internal magnetic field strength averaged 1.8 times higher than external fields
- Urban Grid Management Optimization Using Graph Structure and Machine Learning
- Shanghai, CN
- Researcher, supervised by Prof. Gregory Kesden, Carnegie Mellon University
- 07/2023-08/2023
- Implemented a Python-based urban grid management analysis system, utilizing Dijkstra’s algorithm to optimize epidemic prevention resource allocation paths, significantly improving resource deployment efficiency and area coverage
- Analyzed performance bottlenecks in existing network management systems through machine learning methods to identify key optimization points and propose improvement solutions
- Published a research paper on the application of grid management in epidemic control, utilizing data visualization techniques to demonstrate management efficiency improvements, and delivered presentations at research group meetings