Ecosyste.ms sponsors
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An open API service aggregating public data about GitHub Sponsors.
Assistant Professor of Computer Science @ University of Southern California (USC); Ph.D. @ CMU. Anomaly/Outlier Detection | ML Systems | AutoML
Funding Links: https://github.com/sponsors/yzhao062
Devoting effort to open-source projects is a significant endeavor, and your support plays a crucial role in advancing open-source machine learning (ML), particularly in the realms of anomaly and outlier detection. Your sponsorship can make a real difference!
Why Your Sponsorship Matters
Your support enables me to push the boundaries of ML research and contribute more effectively to open-source projects. Together, we can drive innovation and create tools that benefit everyone in the ML community.
Contributions to Outlier Detection Systems, Benchmarks, and Applications
My work focuses on developing automated, scalable, and accelerated machine learning systems (MLSys) essential for large-scale, real-world outlier detection applications in sectors like security, finance, and healthcare. These systems have already had a substantial impact, evidenced by millions of downloads.
Key Projects and Systems:
CPU-Based Systems (PyOD)
GPU-Based Systems (TOD)
Distributed Detection Systems (SUOD)
Specialized Handling for Different Data Types:
Tabular Data (PyOD)
Time-Series Data (TODS)
Graph Data (PyGOD)
Benchmarks for Outlier Detection:
ADBench for Tabular Data
Time-Series OD Benchmark (Paper)
Graph Data OD (UNOD)
Anomaly-Detection-Resources Repository
I also maintain the anomaly-detection-resources, a vital resource with over 7.6k stars. It's a treasure trove of Anomaly Detection related books, papers, videos, and toolboxes.
How Your Support Helps
Advance Research: Funding supports my ongoing research, allowing for deeper exploration into uncharted territories of ML.
Enhance Open-Source Tools: Your contributions help improve and maintain high-quality, accessible ML tools.
Empower Education: Sponsorship aids in developing resources and opportunities for upcoming ML enthusiasts and researchers.
Join me in shaping the future of Machine Learning! Your sponsorship is not just financial support; it's a partnership in fostering a vibrant, innovative ML community.
Together, we can unlock new possibilities and make a lasting impact. Be a part of this exciting journey!
Anomaly detection related books, papers, videos, and toolboxes
Language: Python - Stars: 8408A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Language: Python - Stars: 8611A Python Library for Graph Outlier Detection (Anomaly Detection)
Language: Python - Stars: 1339(AAAI' 20) A Python Toolbox for Machine Learning Model Combination
Language: Python - Stars: 642Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.
Language: Python - Stars: 883TOD: GPU-accelerated Outlier Detection via Tensor Operations
Language: Python - Stars: 177