Ruobin Qi

Artificial Intelligence · Deep Learning · Smart Grid Security · Anomaly Detection

AI for secure smart grids.

Assistant Professor of Computer Science

California State University, Northridge

18Peer-reviewed
publications
2Principal investigator
grants at CSUN
4Core research
directions
2024Joined the
CSUN faculty

Research profile

Detecting threats in smart grids.

My work develops data-driven security methods for advanced metering infrastructure and power-system data.

My research develops semi-supervised and unsupervised anomaly-detection methods for smart-grid security. It combines deep representation learning, feature engineering, and ensemble learning to detect electricity theft and cyber-attacks in AMI and PMU data.

01

Artificial Intelligence

02

Deep Learning

03

Smart Grid Security

04

Anomaly Detection

Academic path

Appointments & education

Academic appointments

  1. Assistant Professor of Computer Science California State University, Northridge · Northridge, California
  2. Graduate Teaching & Research Assistant New Mexico Institute of Mining and Technology · Socorro, New Mexico
  3. Graduate Mentor New Mexico EPSCoR STEM Advanced Program · Albuquerque, New Mexico

Education

  1. Ph.D. in Computer Science New Mexico Institute of Mining and Technology
  2. M.S. in Computer Science New Mexico Institute of Mining and Technology
  3. M.S. in Petroleum Engineering New Mexico Institute of Mining and Technology
  4. B.S. in Petroleum Engineering Chongqing University of Science and Technology

Scholarship

Publications

Peer-reviewed work in anomaly detection, secure energy systems, neural architecture search, trustworthy AI, and applied machine learning.

Journal articles

9 publications

  1. 2026

    MOGSM-Net: Multi-objective genetically searched multi-label deep networks for non-intrusive load monitoring

    Zhirui Luo, Qingqing Li, Ruobin Qi, and Jun Zheng · Neural Computing and Applications, 38(6), 172.

  2. 2025

    A data-driven unsupervised ensemble method for electricity theft detection in AMI systems

    Ruobin Qi, Qingqing Li, Zhirui Luo, and Jun Zheng · IEEE Access, 13, 210391-210405.

  3. 2025

    A model-agnostic approach to reveal detection basis and occurrence time of electricity theft

    Wenlong Liao, Shuyang Zhu, Leijiao Ge, Takayuki Ishizaki, Ruobin Qi, Yue Yang, and Zhe Yang · IEEE Transactions on Instrumentation and Measurement.

  4. 2025

    Designing channel attention fully convolutional networks with neural architecture search for customer socio-demographic information identification using smart meter data

    Zhirui Luo, Qingqing Li, Ruobin Qi, and Jun Zheng · AI, 6(1), 9.

  5. 2024

    Automatic searching of lightweight and high-performing CNN architectures for EEG-based driving fatigue detection

    Qingqing Li, Zhirui Luo, Ruobin Qi, and Jun Zheng · IEEE Transactions on Instrumentation and Measurement, 73, 1-11.

  6. 2023

    DeepTPA-Net: A deep triple attention network for sEMG-based hand gesture recognition

    Qingqing Li, Zhirui Luo, Ruobin Qi, and Jun Zheng · IEEE Access, 11, 96797-96807.

  7. 2022

    Deep semi-supervised electricity theft detection in AMI for sustainable and secure smart grids

    Ruobin Qi, Qingqing Li, Zhirui Luo, Jun Zheng, and Sihua Shao · Sustainable Energy, Grids and Networks, 36, 101219.

  8. 2022

    A novel unsupervised data-driven method for electricity theft detection in AMI using observer meters

    Ruobin Qi, Jun Zheng, Zhirui Luo, and Qingqing Li · IEEE Transactions on Instrumentation and Measurement, 71, 1-10, Art. 2513010. Highlighted project

  9. 2021

    Detecting cyber attacks in smart grids using semi-supervised anomaly detection and deep representation learning

    Ruobin Qi, Craig Rasband, Jun Zheng, and Raul Longoria · Information, 12(8), 328.

Conference papers

9 publications

  1. 2026

    A semi-supervised anomaly detection framework for electricity theft using optimized feature selection

    Kyungwon Shin and Ruobin Qi · 2026 IEEE World AI IoT Congress (AIIoT), 0389-0395.

  2. 2026

    Adversarial robustness of CNN-based architectures for deepfake image detection

    Dhruv Vagadiya, Vijay Srinivas Tida, Ruobin Qi, and Rashida Hasan · 2026 IEEE World AI IoT Congress (AIIoT), 0409-0415.

  3. 2026

    Normalizing flow for financial anomaly detection: The impact of loss function

    A. Rashidi, Ruobin Qi, and Rashida Hasan · 14th International Symposium on Digital Forensics and Security (ISDFS), 1-6.

  4. 2025

    Bagging or boosting? Anomalous transaction identification in financial systems

    Srushti Patel, Ruobin Qi, and Rashida Hasan · IEEE World AI IoT Congress (AIIoT), 0719-0725.

  5. 2024

    Enhancing the performance of semi-supervised electricity theft detection in smart grids with feature engineering and ensemble learning

    Ruobin Qi, Japp G. Wynter, Stephen Pan, Jun Zheng, and Sihua Shao · IEEE Kansas Power and Energy Conference (KPEC), 1-6.

  6. 2023

    Detecting cyber-attacks and power system disturbances in smart grids with deep forest

    Craig Rasband, Ruobin Qi, and Jun Zheng · 30th International Conference on Systems Engineering (ICSEng), 146-155.

  7. 2023

    Feature engineering for semi-supervised electricity theft detection in AMI

    Elijah Orozco, Ruobin Qi, and Jun Zheng · IEEE Green Technologies Conference (GreenTech), 128-133.

  8. 2022

    ABODE-Net: An attention-based deep learning model for non-intrusive building occupancy detection using smart meter data

    Zhirui Luo, Ruobin Qi, Qingqing Li, Jun Zheng, and Sihua Shao · 7th International Conference on Smart Computing and Communication (SmartCom), 152-164. Best Student Paper

  9. 2020

    Semi-supervised outlier detection and deep feature extraction for detecting cyber-attacks in smart grids using PMU data

    Ruobin Qi, Craig Rasband, Jun Zheng, and Raul Longoria · 17th International Conference on Information Technology: New Generations (ITNG), 509-515.

In the classroom

Teaching & mentoring

My teaching emphasizes conceptual clarity, applied problem-solving, thoughtful project design, and constructive feedback.

Fall 2024 - Present

Instructor

Department of Computer Science · California State University, Northridge

  • Programming for Data Science and Analytics Fall 2024 - Spring 2026
  • Data Mining Fall 2024 - Spring 2026
  • Introduction to Operating Systems Summer - Fall 2026
  • Machine Learning Fall 2026
2019 - 2023

Teaching Assistant

New Mexico Tech

Supported courses in algorithms, programming languages, systems programming, databases, computer organization, machine learning, and foundations of computer science.

Summers 2020 - 2023

Research Mentor

New Mexico EPSCoR STEMAP & New Mexico Tech REU

Mentored students in Python, machine learning, research methods, technical problem-solving, and scientific poster communication.

May 2023

Cybersecurity Mini-Course Instructor

Research Experiences for Undergraduates · New Mexico Tech

Designed and delivered a four-hour introduction to cybersecurity and common attacks using hands-on virtual machine demonstrations.

Sponsored research

Grants

$5,000

Spring 2026 - Spring 2027

Faculty Research Project Development Initiative

Principal Investigator · California State University, Northridge

$3,000

Summer 2026

Summer Research Grant

Principal Investigator · California State University, Northridge

Academic community

Service, honors & talks

Leadership & recognition

Special Session Co-Chair ICMLA 2026 · Anomaly Detection in Complex and Evolving Systems
Patrick Orr Memorial Award Department of Computer Science & Engineering, New Mexico Tech · April 2023
Best Student Paper Award SmartCom 2022 · November 2022
Certified Carpentries Instructor March 2023

Peer review

  • Applied Sciences
  • Artificial Intelligence Review
  • Computers and Electrical Engineering
  • Discover Internet of Things
  • Electrical Engineering
  • Electronics Letters
  • Electronics
  • Energies
  • Engineering Applications of Artificial Intelligence
  • IEEE IAS Publications
  • IEEE Transactions on Smart Grid
  • IEEE Transactions on Consumer Electronics
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Instrumentation and Measurement
  • IET Generation, Transmission & Distribution
  • IET Networks
  • International Journal of Computational Intelligence Systems
  • Journal of Electrical and Computer Engineering
  • Journal of Medical Systems
  • Journal of Supercomputing
  • Scientific Reports
  • Sensors
  • Sustainability
  • Sustainable Energy, Grids and Networks
  • Systems
  • IEEE WCNC 2023
  • KSEM 2023
  • ICMLA 2026
  • IEEE COMPAS 2026
Research talks & presentations 12 presentations
  1. A semi-supervised anomaly detection framework for electricity theft using optimized feature selection · 2026 IEEE World AI IoT Congress (AIIoT), Seattle, Washington.
  2. Adversarial robustness of CNN-based architectures for deepfake image detection · 2026 IEEE World AI IoT Congress (AIIoT), Seattle, Washington.
  3. A machine learning-based approach for process optimization in laser-based 3D printing of high-performance Al alloys · TMS 2025, Las Vegas, Nevada.
  4. Enhancing the performance of semi-supervised electricity theft detection in smart grids with feature engineering and ensemble learning · IEEE KPEC 2024, Manhattan, Kansas.
  5. Detecting cyber-attacks and power system disturbances in smart grids with deep forest · ICSEng 2023, Las Vegas, Nevada.
  6. Feature engineering for semi-supervised electricity theft detection in AMI · IEEE GreenTech 2023, Denver, Colorado.
  7. Deep semi-supervised electricity theft detection in AMI for sustainable and secure smart grids using observer meters · New Mexico EPSCoR All Hands Meeting, 2023.
  8. A novel unsupervised data-driven method for electricity theft detection in AMI · 27th National EPSCoR Conference, 2022.
  9. Deep feature extraction for semi-supervised electricity theft detection in AMI with observer meters · New Mexico Research Symposium, 2022.
  10. A novel unsupervised data-driven method for electricity theft detection in AMI · New Mexico EPSCoR All Hands Meeting, 2022.
  11. Semi-supervised outlier detection and deep feature extraction for detecting cyber-attacks in smart grids using PMU data · ITNG 2020.
  12. Semi-supervised outlier detection and deep feature extraction for detecting cyber-attacks in smart grids using PMU data · New Mexico Research Symposium, 2019.