Data Intelligence Systems Lab (DISL)
|DISL’s interests are in the areas of data science and machine learning, with particular focus on biomedical modelling. Our research activities include both theoretical and applied studies aimed at developing prediction models of future health and life aided by technology and data intelligence. Furthermore, we are innovating research in data science by hybridizing theory-based approaches with hypothesis-free, bottom-up data mining. We are committed to fostering an environment of diversity and inclusion both through research, education and scholarly activities. DISL’s vision is pursuing ideas in ‘humanly ethical’ artificial intelligence for health and beyond, working to ameliorate recent discomforting results in social and welfare modelling, as “biased algorithms are everywhere, and no one seems to care”.|
|Mattia Prosperi, MEng, PhD
Dr. Marco Salemi
Professor in Department of Pathology
Emerging Pathogens Institute
Dr. Ilaria Capua
- Antimicrobial Resistance
Our latest project studies the acquisition of antibiotic resistance in pathogens through clinical and agricultural surveillance and development of novel methods to analyze metagenomics data. This project is supported by National Institute of Allergy and Infectious Diseases.
We are currently pursuing several projects on HIV transmmission: 1) Developing novel theoretical and technical framework to model HIV transmission clusters, 2) geospatial modeling of HIV drug resistance in Florida, and 3) investigating the role of viral evolution in HIV-associated neurocognitive disorders. These projects are supported by National Institute of Allergy and Infectious Diseases, National Institute of Neurological Disorders and Stroke, and Florida Department of Health.
- Job Loss
Using deep learning and natural language processing, we are studying individual and network determinants of dramatic life events like job loss using social media. This project is supported by National Science Foundation.
- Virus Discovery
Supported by the European Union, we are developing novel bioinformatics models, methods, and tools for virome analysis epidemic tracing.
Our recent projects on microbiomes involve genome methylation profiling of the gut and breast microbiota. These projects have been supported by National Cancer Institute and University of Florida Health Cancer Center.
- Dr. Prosperi with Dr. Natalie Dean (UF Biostatistics) and Dr. Marco Salemi (UF Pathology) have been awarded a new NSF project in response to the COVID-19 epidemic, titled RAPID: Dynamic Identification of SARS-COV-2 Transmission Epicenters in Presence of Spatial Heterogeneity (COV-DYNAMITE)
- Read our new comment on the importance of developing causal AI
- Check out our latest commentary on the never-ending rivalry between statistics and machine learning
- Dr. Prosperi and Dr. Bian (HOBI Dept) published a correspondence in Nature Machine Intelligence, titled “Is it time to rethink institutional review boards for the era of big data?”
- Kai Wang earned his PhD and will pursue a postdoctoral opportunity at Harvard. Congratulations to Dr. Wang!
- Dr. Prosperi and Dr. Boucher receive an R01 grant from NIH on “Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents”
- Zhaoyi Chen, a PhD student in the DISL lab and UF Informatics Institute fellow, presented at the Journal Club/Fellows luncheon this Wednesday, November 28. He presented his work on prediction modeling of rheumatoid arthritis using machine learning.
- Dr. Prosperi and collaborators Shannan Rich and Veronica Richards won best poster for Phylogenetic Inference at the 23rd International Workshop on Virus Evolution and Molecular Epidemiology (VEME2018) in Berlin, Germany
- Our HIV-DYNAMITE grant award notice was shared on the Association of Schools & Programs of Public Health newsletter
- Zhaoyi received the UF Informatics Institute’s Graduate Student Fellowship to build prediction models for rheumatoid arthritis and other rheumatoid diseases
- Update on our MiniSeq Project
For the most up-to-date list, please visit: Google Scholar