Research
The George Washington University's Data Science program brings together faculty whose research spans the theoretical foundations and applied frontiers of modern data science. Our researchers are organized across three interconnected areas — Statistical Learning, Neural Networks, and Data Science Applications — reflecting the program's commitment to rigorous, interdisciplinary inquiry.
Whether at the undergraduate or graduate level, students have the opportunity to engage directly with faculty whose work bridges foundational methodology and cutting-edge innovation. These research areas collectively reflect our mission to develop the next generation of data scientists equipped to address complex, real-world challenges.
Undergraduate Research
Graduate Research
Faculty by Research Area
The George Washington University's Data Science program brings together faculty and students working at the intersection of statistical theory, computational innovation, and real-world application. Research is organized into three interconnected areas that collectively span the full breadth of modern data science — from foundational methods to cutting-edge neural architectures to domain-specific applications.
Statistical Learning (Big Data Analytics, Machine Learning, Natural Language Processing, Time Series Modeling and Analysis) - This area develops and applies statistical and computational methods for extracting knowledge from complex, large-scale data. Research spans machine learning for prediction and pattern recognition, natural language processing for understanding human language, time series modeling and forecasting, and big data analytics for processing and deriving insights from massive datasets. Faculty and students work with a rich toolkit including scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, spaCy, NLTK, Prophet, statsmodels, PyTorch, TensorFlow/Keras, Apache Spark, and cloud platforms such as AWS, Azure, and GCP — building scalable, data-driven solutions across finance, healthcare, environmental science, and industry.
Neural Networks (Deep Learning, GenAI, Graph Neural Networks, Reinforcement Learning) - This area advances the design, training, and deployment of neural network architectures for problems involving perception, reasoning, generation, and decision-making. Research encompasses deep learning for representation learning, generative AI for producing text, images, and code via large language models and retrieval-augmented generation, graph neural networks for learning from relational and structured data in social, biological, and knowledge graph settings, and reinforcement learning for building intelligent agents in robotics and autonomous systems. Work leverages PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Geometric, NetworkX, LangChain, OpenAI APIs, Stable Baselines3, OpenAI Gym, and Neo4j.
Data Science Applications (Computer Vision, Geographic Information Systems, Speech, Bioinformatics, Data Assimilation, Numerical Methods) - This area applies computational and machine learning methods to high-impact problems across visual, spatial, auditory, and scientific domains. Research spans computer vision for object detection, segmentation, and visual tracking; geographic information systems for spatial analysis and location intelligence in urban planning and environmental monitoring; speech and audio processing for recognition, synthesis, and classification in voice interface applications; and bioinformatics, data assimilation, and numerical methods for biological data analysis, model-observation integration, and scientific computing. Faculty and students draw on PyTorch, TensorFlow/Keras, OpenCV, Hugging Face Transformers, Whisper, Wav2Vec, librosa, GeoPandas, ArcPy, QGIS, and Folium to develop rigorous, domain-driven solutions.