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MARK WU
Software Engineer | Data Engineer | ML Engineer
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WorkExperience

Software Development Engineer Intern

Kaishin Industrial Co. Ltd
May 2024 - Sep 2024
Taiwan

Developed a comprehensive logistics system using Spring Boot and Spring Cloud microservices, covering the full business cycle. Implemented RabbitMQ for asynchronous messaging, optimized geospatial queries with MongoDB, and managed transactional data in MySQL. Enhanced search capabilities with Elasticsearch and streamlined deployment pipelines using Jenkins, Docker, and Kubernetes.

Technologies
Spring BootSpring CloudRabbitMQMongoDBNeo4jMySQLElasticsearchJenkinsCI/CDDockerKubernetes

Software Engineer Intern

InQuartik
Feb 2022 - Aug 2022
USA

Engineered full-stack web applications using Vue.js and Spring Boot, designing RESTful APIs and architecting PostgreSQL databases. Significantly improved test coverage by 80% through BDD scenarios with Selenium and Cucumber, while employing Agile methodologies for iterative development.

Technologies
VueSpring BootPostgreSQLSeleniumCucumberJava
OpenSource

Open Source Contributor

Ray Project / KubeRay
2026 - Present
Remote

Active contributor to KubeRay and the Ray kubectl plugin — History Server, Helm charts, TPU worker groups, and CI reliability.

  • •History Server: context-aware HTTP/exec, unit tests, flaky-test fixes, and naming cleanups.
  • •kubectl plugin: validate TPU accelerator, topology, and numOfHosts; add TPU worker-group docs.
  • •Helm: expose RayCluster authOptions and upgradeStrategy; retry flaky helm-unittest installs.
  • •Operator: remove no-op WorkersToDelete resets; detect hosted operator when no in-cluster Deployment exists.
kuberayrayView PRs
Technologies
GoKubernetesHelmKubeRayRayTPUCI
Projects

FOMC Sentiment Analysis & U.S. Treasury Yield Prediction

Dec 2025

NLP + ML pipeline for Treasury yield prediction integrating FOMC textual analysis.

NLPMachine LearningLSTMXGBoostRoBERTaFin-LLaMAPython
•Built an end-to-end NLP + ML pipeline predicting 2Y/10Y Treasury yield changes using FOMC data.
•Extracted sentiment using Loughran–McDonald, fine-tuned FOMC-RoBERTa, and Fin-LLaMA (Fin-o1-8B).
•Achieved ~70% directional accuracy with LSTM and ensemble models (XGBoost, Random Forest).
•Established financial variables as primary drivers with sentiment providing incremental value.
TechnicalArsenal

AcademicBackground

University of Southern California

Master of Financial Engineering
Los Angeles, CA

Columbia University

Master of Computer ScienceMachine Learning Track
New York, NY

University of Wisconsin-Madison

Bachelor of ScienceComputer Science & Statistics
Madison, WI
Honors&Publications

Honors & Awards

Bronze Medal

KaggleMay 2021

Publications

Financial Transaction Forecasting using Neural Network and Bayesian Optimization

Published in IEEE 2021 International Conference on Computer Science.

IEEE XploreSep 2021

COVID-19 detection based on Computer Vision and Big Data

Published in IEEE 2022 International Conference on Intelligent Computing and Signal Processing.

IEEE XploreJuly 2022

Connect With Me

I'm actively exploring new opportunities and open to any challenges and collaboration. If you have a question or would like to connect, feel free to reach out.
I’ll get back to you as soon as I can!

Los Angeles, CA