I am a Ph.D. Candidate at the Department of Information Science at the
University of Arkansas at Little Rock. My Ph.D. work focuses on automating
data integration and curation for knowledge discovery. Information from
multiple sources is identified, and the underlying entities represented
in the integrated data are extracted using Natural Language Processing
and Machine Learning techniques. I am interested in general in the idea
of knowledge discovery from large datasets. These datasets could be a
relational database, text documents, or graph and network data.
The process of knowledge discovery usually passes by several steps from
data preprocessing and integration, data mining, information extraction,
and finally, knowledge discovery. I am also interested in the applications
of automated knowledge discovery in biomedicine and machine learning for
societal good and computational social science. In addition, I have a minor
interest in Human-Computer Interaction, User Experience, Eye Tracking and
Information Visualization, and the applications of Computer Graphics in Science.
Information Science has evolved to combine computer science, library science,
data science, psychology, and social science. It can be seen as an
interdisciplinary and collaborative applied branch of computer science,
which drew me to that field, especially coming from my Electrical
and Computer Engineering background. Please feel free to connect
through e-mail or any of the social networks links at the bottom. I am a PhD Student at the School of Information at the University of Texas at Austin. I research innovative ways of visualizing information
especially with the emergence of new new media. I also experiment with data science, data analytics and machine learning techniques to study
Human Computer Interaction through fusing sensors. I focus on research topics that lie in the intersection between human computer interaction,
information visualization and data analytics. Shaped by a systematic education in Mathematics, Systems Engineering and Computer & Information Science
combined with several years of industry experience in data modelling, database and user interfaces, I strive to push the boundaries in HCI research.
I recently graduated from the University of Arkansas at Little Rock with a Ph.D. in Computer and Information Science, focusing on Data and Information Science. I worked under the guidance and mentorship of John Talburt and Elizabeth Pierce. Prior to that, I was a Ph.D. student at the University of Texas at Austin, where I worked with Ying Ding.
Mission
My primary goal is to contribute to advancing scientific discovery by fostering a more accessible, ethical, and artificially intelligent (AI) data and information ecosystem. I am passionate about driving the automatic integration and harmonization of all scientific information, ensuring efficient organization, cleaning, archival, and practical mining for new insights. I believe in the transformative power of interdisciplinary collaboration, particularly between data science, artificial intelligence, and the core scientific disciplines. I am committed to advancing responsible and sustainable scientific computing and AI practices, minimizing their environmental impact, and ensuring ethical considerations are paramount in all research endeavors. Finally, I am dedicated to empowering underrepresented groups in computational, information, and data sciences, including women, refugees, and financially challenged populations, by creating inclusive and equitable opportunities for all.
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Vision
My research interests and experiences are centered around the role of data quality, curation, and engineering in artificial intelligence and their applications in the sciences. I develop and adapt methods and frameworks rooted in graph theory, natural language processing, machine learning, and deep learning. I apply the developed methods in information retrieval (computer science), data mining of digital libraries and information networks (information science), biomedical informatics (biology), non-profit studies (social sciences), and human-computer interaction (psychology). My vision is to aid and automate the process of scientific discovery and inquiry by developing ethical and artificially intelligent systems capable of integrating, cleaning, organizing, and mining scientific information automatically and efficiently.
Current Research Interests
Data Quality in AI: I investigate the critical role of data quality in training artificial intelligence
models, particularly in scientific domains where data integrity is essential.
Biomedical Data Integration: I develop efficient techniques for integrating large biomedical
datasets extracted from digital libraries with omics datasets using the Knowledge Graph data
model.
Graph-Based Representation Learning: I develop scalable graph-based representation
learning techniques to leverage the power of integrated datasets for applications in information
retrieval, search engines, and entity matching.
Main Research Experience:
- Entity resolution and matching with graph theory and natural language processing
- Biomedical information retrieval with graph representation learning
Secondary Research Experience:
- Human factors in computing and information seeking with eye tracking
- Scientific visualization of biological data
Current Skillset (Python, C\C++, Unix):
- Graph theory and network mining algorithms
- Applied linear algebra algorithms
- Natural language processing algorithms
- Machine learning and deep learning models and experimentation with TensorFlow
- Setting up high-performance computing environments and data management systems
Older Skillset (C\C++, Java, SQL):
- Experimental design and statistical analysis in eye-tracking studies
- Image processing with OpenCV, mainly for filtering, localization, and mapping
- Computer graphics with OpenGL and WebGL for creating scientific visualizations
- Software engineering, especially web applications and services based on the MVC design pattern and backend ETL operations
