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😄 I am an Assistant Professor at USC Computer Science; see more information at my homepage.

Prospective Students. I am peacefully welcoming prospective Ph.D. students (apply by Dec 15th for Fall 24 admission; full financial support) and research interns. You are expected to have one published top paper on my research topics (current focus includes anomaly/outlier/OOD detection, Auto ML, and Multimodal Learning) and strong programming skills (such as (ML) System papers and/or open-source experience) for open-source ML and/or systems. See more at my homepage

🌱 My research: I build fast, automated, and open machine learning (ML) and data mining (DM) systems, with a focus on but not limited to anomaly detection, graph neural networks, and healthcare for AI.

  1. Accelerate large-scale learning tasks by leveraging ML systems techniques.
  2. Automate unsupervised ML by model selection and hyperparameter optimization.
  3. Develop open-source ML tools to support applications in healthcare, finance, and security.

Ph.D. time. At CMU, I work with Prof. Leman Akoglu for automated ML, Prof. Zhihao Jia for machine learning systems, and Prof. George H. Chen for general ML. I am a member of CMU automated learning systems group (Catalyst) and Data Analytics Techniques Algorithms (DATA) Lab. I have collaborated with Prof. Jure Leskovec at Stanford and Prof. Philip S. Yu at UIC.

Open-source Contribution: I have led or contributed as a core member to more than 10 ML open-source initiatives, receiving 15,000 GitHub stars (top 0.002%: ranked 800 out of 40M GitHub users) and >20,000,000 total downloads.

📫 Contact me by:


Yue Zhao's Projects

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(AAAI' 20) A Python Toolbox for Machine Learning Model Combination

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It is a repository to store multiple implementation of data structures and algorithms in C++ written by me in the past several years.

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Supplementary material for KDD 2018 workshop "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles"

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Toward Unsupervised Outlier Model Selection (ICDM 2022)

lscp icon lscp

Supplementary material for SDM 19 paper "LSCP: Locally Selective Combination in Parallel Outlier Ensembles"

metaod icon metaod

Automating Outlier Detection via Meta-Learning (Code, API, and Contribution Instructions)

mlmm icon mlmm

A Monitoring framework to track Machine Learning Model training processes

mmad icon mmad

multimodal anomaly detection

pyod icon pyod

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

pytod icon pytod

TOD: GPU-accelerated Outlier Detection via Tensor Operations

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SImilarity Measure Library: an extended python library for measuring similarities

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Supplementary materials for ISWC paper "An empirical study of touch-based authentication methods on smartwatches"

suod icon suod

(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)

uoms icon uoms

Resources and environment for unsupervised outlier model selection (UOMS)

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A Collection of Resources for Weakly-supervised Anomaly Detection (WSAD)

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Supplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"

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