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

Prospective Students. Openings and Timeline for PhD Offer. I am peacefully looking for Ph.D. students (from Fall 25) and interns (no earlier than Fall 2024). I prepared a procedure for the USC CS Ph.D. offer process and funding. See more at my homepage.

🌱 Research Interests. I build reproducible, automated, and scalable machine learning (ML) and data mining (DM) benchmarks, algorithms, and systems, with a focus on but not limited to anomaly detection, graph neural networks, ML systems, and AI for healthcare, security, and finance.

  1. Benchmark various learning algorithms for fair evaluation and new insights.
  2. Automate ML by model selection and hyperparameter optimization.
  3. Design large-scale ML systems for real-world applications.
  4. Develop open-source ML tools to support applications in healthcare, finance, security, and more.

Open-source Contribution: I created PyOD (used by NASA, Tesla, Morgan Stanley, and more) - the most popular library for anomaly detection in 2017. Also, I have led more than 10 ML open-source initiatives, receiving 20,000 GitHub stars (top 0.002%) and >20M downloads. Popular ones: PyOD, PyGOD, TDC, ADBench

📫 Contact me by:


Yue Zhao's Projects

adbench icon adbench

Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.

combo icon combo

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

datastructure_cpp icon datastructure_cpp

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

dcso icon dcso

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

elect icon elect

Toward Unsupervised Outlier Model Selection (ICDM 2022)

hpod icon hpod

AutoML 2024: HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection

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 Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

pytod icon pytod

TOD: GPU-accelerated Outlier Detection via Tensor Operations

siml icon siml

SImilarity Measure Library: an extended python library for measuring similarities

smartwatch_unlock icon smartwatch_unlock

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)

wsad icon wsad

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

xgbod icon xgbod

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

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