Research

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For each faculty member below, review their research areas, specific contact etiquette, and previous work in the field before reaching out. Following the guidelines provided in each callout ensures respectful and effective communication.

Dr. Sarah Cassie Burnett

Research areas - Machine Learning (ML), Data Assimilation, Numerical Methods.

Contact etiquette -Email me at sarah [dot] burnettemail [dot] gwu [dot] edu (sarah[dot]burnett[at]email[dot]gwu[dot]edu), keeping your message brief (2-3 sentences). I'm most responsive Monday through Friday, 8:00am - 5:00pm, and will try to respond within one day. Drop-in advising hours are held on Wednesdays from 11:00am - 12:00pm.

Links - Google Scholar - ORCID

Dr. Yuxiao Huang

Research areas - Big Data Analytics (BDA), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Generative AI (GenAI), Graph-based Neural Networks (GNN), Time Series Modeling (TS), Computer Vision (CV), Reinforcement Learning (RL), Speech.

Contact etiquette - send an email to yuxiaohuanggwu [dot] edu (yuxiaohuang[at]gwu[dot]edu)

Published research

Yongqi Huang, Jitao Zhao, Dongxiao He, Xiaobao Wang, Yawen Li, Yuxiao Huang, Di Jin, Zhiyong Feng. One Prompt Fits All: Universal Graph Adaptation for Pretrained Models. NeurIPS, 2026

Links - Personal Website - ORCID

Dr. Amir Jafari

Research areas - Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Generative AI (GenAI), Graph-based Neural Networks (GNN).

Contact etiquette - send an email to ajafarigwu [dot] edu (ajafari[at]gwu[dot]edu)

Published research

Huang, G., Jafari, A.H. Enhanced balancing GAN: minority-class image generation. Neural Comput & Applic 35, 5145–5154 (2023). https://doi.org/10.1007/s00521-021-06163-8
Lin, H., Chen, C., Huang, G. & Jafari, A. (2021). Stock price prediction using Generative Adversarial Networks. Journal of Computer Science, 17(3), 188-196. https://doi.org/10.3844/jcssp.2021.188.196
Jafari, A.H., Hagan, M.T. Application of new training methods for neural model reference control. Engineering Applications of Artificial Intelligence 74, 312–321 (2018). https://doi.org/10.1016/j.engappai.2018.07.005

Links - Google Scholar

Dr. Sushovan Majhi

Contact etiquette - send an email to s [dot] majhigwu [dot] edu (s[dot]majhi[at]gwu[dot]edu). You can expect a response time within 48 hours, with office hours on Mondays 1:00pm-3:00pm

Dr. Hazim Shatnawi

Research areas - Big Data Analytics (BDA), Deep Learning (DL), Graph-based Neural Networks (GNN), Geographical Information Systems (GIS), Computer Vision (CV).

Contact etiquette - Send an email to hazim [dot] shatnawigwu [dot] edu (hazim[dot]shatnawi[at]gwu[dot]edu) with the subject line "Research: <your name> Research Topic". You can expect a response within 24 hours on weekdays. Office hours are held on Tuesdays from 2:30pm - 5:50pm.

Published research

Mahmoud Abusaqer, Jamil Saquer, & Hazim Shatnawi. Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers. In Proceedings of the 2025 ACM Southeast Conference (ACMSE ’25), Harrison College of Business and Computing, Southeast Missouri State University, Cape Girardeau, MO, USA, April 24–26, 2025. Association for Computing Machinery, New York, NY, USA.
Hazim Shatnawi, Mahmoud Abusaqer & Jamil Saquer. From Baselines to DenseNet: A Deep Learning Framework for CNN Optimization and Augmentation. In Proceedings of the 2025 ACM Southeast Conference (ACMSE ’25), Harrison College of Business and Computing, Southeast Missouri State University, Cape Girardeau, MO, USA, April 24–26, 2025. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3696673.3723060
Hazim Shatnawi & Jamil Saquer. Leveraging Neo4j for Data Science: Evaluating Traversal Efficiency in GDS and APOC for Directed Acyclic Graphs. In Proceedings of the 2024 7th International Conference on Data Science and Information Technology (DSIT ’24), Nanjing Institute of Technology, Nanjing, China, December 20–22, 2024. IEEE, Piscataway, NJ, USA. https://doi.org/10.1109/DSIT61374.2024.10880896
Hazim Shatnawi & Jamil Saquer. Neo4j’s BFS and DFS Evaluation in GDS and APOC Libraries with SPL Feature Models. In Proceedings of the 2024 International Conference on Software Engineering and Knowledge Engineering (SEKE ’24), Larkspur Landing South San Francisco All-Suite Hotel, South San Francisco, CA, USA, October 26–28, 2024. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.18293/SEKE2024-110
Hazim Shatnawi & Jamil Saquer. Encoding Feature Models in Neo4j Graph Database. In Proceedings of the 2024 ACM Southeast Conference (ACMSE ’24), Harrison College of Business and Computing, Southeast Missouri State University, Cape Girardeau, MO, USA, April 2024. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3603287.3651199
Nabil Shawkat, Jamil Saquer & Hazim Shatnawi. Evaluation of Different Machine Learning and Deep Learning Techniques for Hate Speech Detection. In Proceedings of the 2024 ACM Southeast Conference (ACMSE ’24), Harrison College of Business and Computing, Southeast Missouri State University, Cape Girardeau, MO, USA, April 2024. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3603287.3651218

Dr. Angelica M. Walker

Research areas - Big Data Analytics (BDA), Machine Learning (ML), Bioinformatics.

Contact etiquette - Please reach out via email at angelica [dot] walkergwu [dot] edu (angelica[dot]walker[at]gwu[dot]edu), though I also welcome students to drop in during my Undergraduate Advising Office Hours on Tuesdays from 10:00am - 11:00am in Samson 307.

Published research

Kainer, et al.,RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species, GigaScience, Volume 14, 2025, giaf028, https://doi.org/10.1093/gigascience/giaf028
Ladau, et al., Estimating geographic variation of infection fatality ratios during epidemics, Infectious Disease Modelling, Volume 9, Issue 2, 2024, https://doi.org/10.1016/j.idm.2024.02.009
Noshay, et al., Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering, Nucleic Acids Research, Volume 51, 2023, https://doi.org/10.1093/nar/gkad736
Walker, et al., Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts, Fronties in psychiatry, Volume 14, 2023, https://doi.org/10.3389/fpsyt.2023.1178633
Walker, et al., Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data, Computational and Structural Biotechnology Journal, Volume 20, 2022, https://doi.org/10.1016/j.csbj.2022.06.037
Wolf, et al., Reusability First: Toward FAIR Workflows, 2021 IEEE International Conference on Cluster Computing (CLUSTER), Portland, OR, USA, 2021, pp. 444-455, https://doi.org/10.1109/Cluster48925.2021.00053
Garvin, et al., A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm, eLife, Volume 9, 2020, https://doi.org/10.7554/eLife.59177 2019 Cliff, et al., High-Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks, Genes, Volume 10, 2019, https://doi.org/10.3390/genes10120996

Links - Google Scholar

Dr. Junjun Yin

Research areas - Big Data Analytics (BDA), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Graph-based Neural Networks (GNN), Geographical Information Systems (GIS).

Contact etiquette - Send an email to j [dot] yin

email [dot] gwu [dot] edu (j[dot]yin[at]email[dot]gwu[dot]edu) with the subject line "Research: Course Number - Research Topic". You can expect a response within 24-48 hours on weekdays. Office hours are listed in the syllabus, or appointments are available by request.

Published research

Yin, J., Brooks, M., Wang, D. and Chi, G., 2025. Characterizing climate change sentiments in Alaska on social media. Digital geography and society, 8, p.100110. Yin, J., Chi, G. and Jiang, B., 2025. Unlocking the secrets of scenic beauty: a quantitative analysis of object variety and connections in scenic images. Journal of the Royal Society Interface, 22(225), p.20250045. https://doi.org/10.1098/rsif.2025.0045
Sundar, S.S., Snyder, E.C., Liao, M., Yin, J., Wang, J. and Chi, G., 2025. Sharing without clicking on news in social media. Nature Human Behaviour, 9(1), pp.156-168. https://doi.org/10.1038/s41562-024-02067-4
Yin, J. and Chi, G., 2022. A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data. Urban Informatics, 1(1), p.20. https://doi.org/10.1007/s44212-022-00020-2
Yin, J. and Chi, G., 2021. Characterizing people’s daily activity patterns in the urban environment: A mobility network approach with geographic context-aware twitter data. Annals of the American Association of Geographers, 111(7), pp.1967-1987. https://doi.org/10.1080/24694452.2020.1867498

Links - Personal Website - Google Scholar - ORCID

Big Data Analytics (BDA)

This research area focuses on methods and technologies for processing, analyzing, and extracting insights from large-scale datasets. Faculty and students explore topics such as distributed computing, data mining, scalable machine learning, and real-time analytics, using modern frameworks including Apache Spark, Hadoop, Dask, cloud platforms (AWS, Azure, GCP), and visualization tools to handle massive datasets across various domains.

Faculty: Dr. Yuxiao Huang - Dr. Hazim Shatnawi - Dr. Junjun Yin - Dr. Angelica M. Walker

Machine Learning (ML)

This research area focuses on the development and application of machine learning methods for prediction, pattern recognition, and decision-making across diverse domains. Faculty and students leverage modern libraries and frameworks such as scikit-learn, XGBoost, LightGBM, and other statistical learning tools to build scalable models and analyze large, complex datasets.

Faculty: Dr. Amir Jafari - Dr. Yuxiao Huang - Dr. Junjun Yin - Dr. Angelica M. Walker - Dr. Sarah Burnett

Deep Learning (DL)

This research area focuses on the development and application of deep learning methods for complex pattern recognition, representation learning, and decision-making across diverse domains. Faculty and students leverage modern frameworks such as PyTorch, TensorFlow/Keras, and Hugging Face Transformers to build neural network architectures and train models on large-scale datasets.

Faculty: Dr. Amir Jafari - Dr. Yuxiao Huang - Dr. Hazim Shatnawi - Dr. Junjun Yin

Natural Language Processing (NLP)

This research area focuses on computational methods for understanding, analyzing, and processing human language. Faculty and students explore topics such as language modeling, information retrieval, text classification, named entity recognition, sentiment analysis, and machine translation, using modern libraries and frameworks including Hugging Face Transformers, spaCy, NLTK, and PyTorch to develop and evaluate language understanding systems.

Faculty: Dr. Amir Jafari - Dr. Yuxiao Huang - Dr. Junjun Yin

Generative AI (GenAI)

This research area focuses on the development and application of generative models for creating text, images, code, and other content. Faculty and students explore topics such as large language models, prompt engineering, retrieval-augmented generation, multi-modal generation, and human-AI interaction, using modern frameworks and APIs including OpenAI APIs, Hugging Face Transformers, LangChain, and PyTorch to build and evaluate state-of-the-art generative systems.

Faculty: Dr. Amir Jafari - Dr. Yuxiao Huang

Graph Neural Networks (GNN)

This research area focuses on learning from relational and structured data using graph-based representations. Faculty and students study methods for node, edge, and graph-level prediction, as well as applications in social networks, recommender systems, biological networks, and knowledge graphs. Research leverages modern libraries and frameworks such as PyTorch Geometric, NetworkX, PyTorch, and Neo4j to model complex dependencies and scalable graph-based systems.

Faculty: Dr. Amir Jafari - Dr. Yuxiao Huang - Dr. Hazim Shatnawi - Dr. Junjun Yin

Time Series Modeling and Analysis (TS)

This research area focuses on the analysis, modeling, and forecasting of temporal data across diverse applications. Faculty and students explore topics such as time series forecasting, anomaly detection, trend analysis, and sequential pattern mining, using modern libraries and frameworks including Prophet, statsmodels, PyTorch, TensorFlow/Keras, scikit-learn, and ARIMA-based models to develop predictive systems for financial, environmental, and operational data.

Faculty: Dr. Yuxiao Huang

Computer Vision (CV)

This research area focuses on enabling machines to interpret and understand visual information from images and videos. Faculty and students explore topics such as object detection, image segmentation, image classification, facial recognition, and visual tracking, using modern frameworks including PyTorch, TensorFlow/Keras, OpenCV, and Hugging Face Transformers to develop state-of-the-art vision systems.

Faculty: Dr. Yuxiao Huang - Dr. Hazim Shatnawi

Geographical Information Systems (GIS)

This research area focuses on the analysis, visualization, and modeling of spatial and geographic data. Faculty and students explore topics such as spatial analysis, geospatial data processing, location-based modeling, and cartographic visualization, using modern libraries and frameworks including GeoPandas, ArcPy, QGIS, and Folium to develop solutions for urban planning, environmental monitoring, and location intelligence applications.

Faculty: Dr. Hazim Shatnawi - Dr. Junjun Yin

Reinforcement Learning (RL)

This research area focuses on the development of agents that learn optimal decision-making strategies through interaction with their environment. Faculty and students explore topics such as policy optimization, multi-agent systems, deep reinforcement learning, and reward shaping, using modern frameworks including OpenAI Gym, Stable Baselines3, and PyTorch to develop intelligent agents for robotics, game playing, and autonomous systems.

Faculty: Dr. Yuxiao Huang

Speech

This research area focuses on computational methods for analyzing, understanding, and generating speech and audio signals. Faculty and students explore topics such as speech recognition, speaker identification, audio classification, speech synthesis, and voice activity detection, using modern libraries and frameworks including Whisper, Wav2Vec, PyTorch, librosa, and Hugging Face Transformers to develop systems for voice interfaces and audio intelligence applications.

Faculty: Dr. Yuxiao Huang

Bioinformatics

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Faculty: Dr. Angelica M. Walker

Data Assimilation

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Faculty: Dr. Sarah Burnett

Numerical Methods

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Faculty: Dr. Sarah Burnett