Climate Change and Health Intelligence Lab

Long Nguyen

Long Nguyen, Ph.D.
Assistant Professor, Computer Science and Data Science

The focus of the Climate Change and Health Intelligence Lab is applying data mining and machine learning to climate change resilience and health intelligence. Specific applications include, but are not limited to, computer vision, natural language processing, time-series analysis and anomaly detection.

We enjoy advancing AI techniques for climate change resilience and health care, with special attention to interpretable methods, ethical AI/ML, fairness-aware, and privacy-preserving integration to make the predictive models responsible and improve their accountability. Potential applications of our research include developing models and algorithms that incorporate community knowledge for disaster event causality, strategic planning, or preventive health care through early detection and monitoring of patient health.

Research interests

Data mining and machine learning | Responsible AI/ML | Interpretable methods | Climate Change Resilience | Health Intelligence

Student Research Assistant Opportunities

There are scholarship opportunities for students interested in applying cutting-edge machine learning and data mining to climate change resilience and health intelligence. Students must be U.S. citizens or hold a green card.

Interested students should send their CV to Dr. Nguyen.

COVID-19 human-body response stages

We explore physiological signs in COVID-19 patients and see human responses in each symptom stage of COVID-19 such as mild, medium, or severe. This will lead to greatly resources saving for hospitals as they can hospitalize severe patients while the mild patients can still be taken care of remotely.

Opiod Relapse Prediction

Opioid addiction poses severe threats to public health, causing many deaths and massive social disruption. It is therefore critical to be able to predict whether or not a recovering opioid addict will relapse. Our goal is to model Opioid relapse prediction and then design a personalized intervention plan.

Grant Support

Quantitating Biological Cells from Microscopy Images using Deep Learning
Nguyen (PI)
2023, $15,000,
Meharry Medical College

Supplement: Administrative Supplements for Ethical Considerations in Artificial Intelligence and Machine Learning (AI/ML) at NIMHD-Funded Research Centers in Minority Institutions (RCMI)
Nguyen (Co-PI)
2023 – 2024, $218,250
National Institutes of Health

Student Research

Cyruss Tsurgeon
M.S. Biomedical Data Science
School of Applied Computational Sciences

Fuxue Xin
M.S. Biomedical Data Science
School of Applied Computational Sciences

Javeia Johnson-Mccoy
M.S. Biomedical Data Science
School of Applied Computational Sciences

Brittany City
M.S. Data Science
School of Applied Computational Sciences
Class of 2023


Long Nguyen, Muzhe Guo, Hongfei Du, and Fang Jin. When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies, Frontiers in Big Data, 2022. Accepted.

H Abu-gellban, L Nguyen, F Jin Gfdlecg: Pac classification for ecg signals using gradient features and deep learning, Advances in Data Science and Information Engineering

HL Nguyen, Z Pan, H Abu-Gellban, Y Zhang. Google trends analysis of covid-19 pandemic, 2020 IEEE International Conference on Big Data (Big Data)

Zhou Yang, Long Nguyen, and Fang Jin. Opioid Relapse Prediction with GAN, the 2019 IEEE/ACM International Conference on Social Networks Analysis and Mining, ASONAM 2019. August, 2019. Vancouver, Canada. Acceptance rate 14%.

A complete list of publications is available at Google Scholar.