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My name is Jiajing Huang (also go by Horatio). I am currently an assistant professor of data science and analytics at Kennesaw State University. I hold a Ph.D. degree in Data Science, Analytics and Engineering, under supervision of Prof. Teresa Wu from School of Computing and Augmented Intelligence at Arizona State University. I have many years' experience in machine learning, deep learning, statistics and biostatistics with data analysis and modeling, and statistical inferences.

My research directions include the applications of information-theoretical framework, causal learning (e.g. causal inferences, causal modeling, causal reasoning), and developments of machine learning algorithms/big data tools. My research projects are primarily sponsored by National Science Foundation, U.S. Department of Energy and National Institute of Health.

Education

Ph.D. in Data Science, Analytics and Engineering

Arizona State University, Tempe, Arizona, U.S.A.

Dissertation: An Information-theoretical Framework for Data-driven Building Automatic Fault Detection and Diagnosis Support

Advisor: Prof. Teresa Wu

08/2024

M.S. in Industrial Engineering

Arizona State University, Tempe, Arizona, U.S.A.

12/2020

M.S. in Statistics

Rutgers, the State University of New Jersey, New Brunswick, New Jersey, U.S.A.

05/2018

B.E. in Materials Science and Engineering

Shenzhen University, Shenzhen, Guangdong, China

07/2013

Skills

Programming

Python, Scikit-learn, Keras/Tensorflow, PyTorch, Matlab, SAS, R, HTML/CSS/JavaScript, Git

Software

MySQL, PostgreSQL, SPSS, Spark, Hadoop, Map/Reduce, Google Cloud Platform, Tableau, Azure

Data Science

Machine Learning (Supervised/Unsupervised/Semi-supervised/Active Learning), Deep learning (CNN, GNN), Causal Learning (Causal Inference, Causal Reasoning, Causal Modeling), Statistical Inference (Parametric/Nonparametric Hypothesis Testing), Data Visualization (D3, matplotlib)

Languages

English, Mandarin Chinese, Cantonese

Professional Experience

Assistant Professor of Data Science and Analytics

School of Data Science and Analytics, Kennesaw State University, Marietta, GA

01/2025 - Present

Graduate Research Associate / Visiting Scholar

ASU-Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ

08/2018 - 12/2024

Ph.D. Intern - Buildings Research

Pacific Northwest National Laboratory (PNNL), Richland, WA

06/2022 - 08/2022

Research Assistant

The Cardiovascular Institute of New Jersey, Rutgers University, New Brunswick, NJ

09/2017 - 05/2018

Trainee Consultant

Productivity (Shenzhen) Consulting Co., Ltd., Shenzhen, Guangdong, China

10/2013 - 10/2014

Publications

Journals

J. Huang, N. Ghalamsiah, A. Patharkar, O. Pradhan, M. Chu, T. Wu, J. Wen, Z. O'Neill and K. S. Candan, “An entropy-based causality framework for cross-level fault diagnosis and isolation in building HVAC systems,” Energy and Buildings (IF 6.7), vol. 317, pp. 114378, 2024.

A. Patharkar, J. Huang, T. Wu, E. Forzani, L. Thomas, M. Lind, N. Gades, “Eigen-Entropy based Time Series Signatures to Support Multivariate Time Series Classification,” Scientific Reports (IF 3.8), vol 14, no. 1, pp. 16076, 2024.

G. Li, L. Ren, O. Pradhan, J. Wen, Z. Yang, Y. Fu, M. Chu, J. Huang, T. Wu, K. S. Candan, V. Adetola, and Q. Zhu, “Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments,” Energy and Buildings (IF 6.7), vol. 320, pp. 114596, 2024.

O. Pradhan, D. Halleberg, Z. Chen, J. Wen, N. Varman, J. Huang, T. Wu, K.S. Candan, and Z. O’Neill, “Evaluation of Data Imputation Approaches for Multi-Stream Building Systems Data,” Science and Technology for the Built Environment , vol. 30, no. 8, pp. 10351048, 2024.

J. Huang, H. Yoon, T. Wu, K.S. Candan, O. Pradhan, J. Wen and Z. O’Neill, “Eigen-Entropy: A metric for multivariate sampling decisions,” Information Sciences (IF: 8.1), vol. 619, pp. 84-97, 2023.

J. Huang, H. Yoon, O. Pradhan, T. Wu, J. Wen, Z. O’Neill and K.S. Candan, “A Cosine-based Correlation Information Entropy Approach for Building Automatic Fault Detection Baseline Construction,” Science and Technology for the Built Environment, vol. 28, no.9, pp. 1138-1149, 2022.

J. Huang , J. Wen, H. Yoon, O. Pradhan, T. Wu, Z. O’Neill and K.S. Candan, “Real vs. Simulated: questions on the capability of simulated datasets on building fault detection for energy efficiency from a data-driven perspective,” Energy and Buildings (IF 6.7), vol.259, pp.111872, 2022.

Conference Proceedings

J. Huang, A. Patharkar, T. Wu, J. Wen, Z. O'Neill and K. S. Candan, “A feature extraction framework with entropy on graphs for cross-dataset building fault detection,” accepted at IEEE CASE 2024. (This paper is awarded Finalist in 2024 IEEE CASE Best Student Paper Award .)

J. Huang, Z. Yang, G. Li, T. Wu, Z. O’Neill, J. Wen and K. S. Candan, “A Data-driven AFDD Approach Using Acoustic Emission In Building HVAC Systems,” accepted at the 8th International High Performance Buildings Conference.

J. Huang, T. Li, Y. Xu, T. Wu, H. Yoon, J.R. Charlton and K.M.Bennett, “EE-SMOTE: An oversampling method in conjunction with information entropy for imbalanced learning”, In Proceedings of 2022 IISE Annual Conference, pp. 1-6, 2022.

J. Huang, T. Wu, H. Yoon, O. Pradhan, J. Wen and Z. O’Neill, “Automatic Fault Detection Baseline Construction for Building HVAC Systems using Joint Entropy and Enthalpy,” In Proceedings of 2021 IISE Annual Conference , pp.536-541, 2021. (This paper is awarded Finalist in 2021 IISE Annual Conference (DAIS Track) Best Paper Award .)

Technical Reports

Z. O'Neill, J. Wen, T. Wu, K.S. Candan, L. Ren, Q. Zhu, G. Li, J. Huang, and O. Pradhan, “Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES),” USDOE Office of Energy Efficiency and Renewable Energy (EERE), Washington, DC, USA, Tech. Rep. DOE-TAMU-EE9150_Final, 2024.

Dissertation

J. Huang, “An Information-theoretical Framework for Data-driven Building Automatic Fault Detection and Diagnosis Support," Ph.D. Dissertation, SCAI, Arizona State Univ., Tempe, AZ, USA, 2024.

Working Paper

“EE-GFE: An information-entropy graph-based feature extraction approach for cross-datasets fault detection in building HVAC systems,” to be submitted to Building and Environment.

“An entropy-based feature extraction on fMRI graphs for cognitive disease classification,” TBD

Teaching Experience

DS 7140 Python for Data Science, (Instructor, KSU graduate course, Spring 2025)

DATA 4140 Python for Data Science, (Instructor, KSU undergraduate course, Spring 2025)

IEE 505 Information Systems Engineering, (Teaching Assistant, ASU graduate course, Spring 2024)

IEE 305 Information Systems Engineering , (Teaching Assistant, ASU undergraduate course, Fall 2020)

IEE 475 Simulating Stochastic Systems, (Teaching Assistant, ASU undergraduate course, Fall 2018)

960:540 Statistical Quality Control I, (Teaching Assistant, Rutgers Graduate Course, Spring 2017)

Jiajing (Horatio) Huang