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Biography

Dr. Debarshi Datta is known as a data scientist and has experience developing AI-driven decision support systems in healthcare data that include understanding problem statements, handling data disputes, exploratory data analysis, building models, data visualization, and data storytelling. Dr. Datta’s current research focuses on data-driven domains like AI/ML to understand a population-based disease prognosis. His research involves building predictive models identifying important features for patients with COVID-19 who are susceptible to dying. In other research, he is building a model to identify early prediction of Dementia. Dr. Datta received many intramural grants, including Early Prediction of Alzheimer's Disease and Related Dementias on Preclinical Assessment Data using Machine Learning tools, Seed Funding from Smart Health for COVID-19 research, NSF I-Corps Customer Discovery Funding, ALL of US Institutional Champion Award, and recently Seed Funding for the project titled “Machine Learning Analysis & Assessment of a Non-invasive Intervention in Long COVID."

Teaching

  • NGR 7846: Essential Statistic Methods for Nursing Science: 2024 (Spring)
  • NGR7818: Advanced Nursing Research: Applied Quantitative Design and Methods: 2023 (Spring)
  • PSY 3234: Experimental Design and Statistical Inference: 2016 (Fall), 2017 (Fall), 2017 (Spring), 2017 (Summer), 2019 (Summer)

Grants Awarded

  • Co-I, Center for SMART Health SEED Fund Competition Winner ($25,000), USA, for the project “Machine Learning Analysis & Assessment of a Non-invasive Intervention in Long COVID.” 2024
  • The All of Us Researcher Academy Institutional Champion Award 2024
  • Co-I, “Early Prediction of Alzheimer's Disease and Related Dementias (ADRD) on Preclinical Assessment Data using Machine Learning (ML) tools.” FAU New Horizons Alzheimer’s Disease and Related Dementias (ADRD) pilot funding program, funded by I-Health, College of Nursing ($37,500). 2023
  • MedAI and SmartBioSense, FAU Tech Runway Venture Program Competition Winner. 2022
  • PI, COECS/ISENSE Seed Funding Competition Winner ($21,000), USA. Center for SMART Health SEED Fund - Datta, Martinez, George, Khoshgoftaar, and Newman for the “Development of a Multi-Scale Predictive Model for COVID-19 Patient Outcomes and Long-Term Health Effects” project. 2022
  • NSF I-Corps Customer Discovery Funding Competition Winer ($18,000 for two teams COVID-19 prediction Tool, Medical Algorithm), USA. 2022
  • Received Graduate Fellowship for Academic Excellence ($5000) for the 2019-2020 academic year, Florida Atlantic University, USA. 2019
  • NSF Student Travel Support for attending Big Data Neuroscience Workshop 2019, Organized by the Advanced Computational Neuroscience Network (ACNN) at the University of Michigan, USA. 2019
  • NSF Student Travel Support for attending Big Data Neuroscience Workshop 2018, Organized by the Advanced Computational Neuroscience Network (ACNN) at the Case Western Reserve University, USA. 2018
  • The 2018 Young Stars Estes Awards are supported by the Association for Psychological Science and the Psychonomic Society to support travel to the Workshop Deep, fast, and shallow learning in humans and machines at Indiana University, USA. 2018
  • Travel Grants for Neuroscience Student Organization (NSO), Florida Atlantic University, for attending the 58th Annual Conference of Psychonomic Society, Vancouver, Canada. 2017
  • Thesis and Dissertation Writing Scholarship ($500) at Florida Atlantic University, USA. 2018, 2017
  • NSF Student Travel Support for attending Big Data Neuroscience Workshop 2017, organized by the Advanced Computational Neuroscience Network (ACNN) at Indiana University, USA. 2017
  • Graduate and Professional Student Association (FAU) Travel Grants for attending Vision Science Society meeting at St. Pete Beach, USA. 2018, 2017,2016,2015
  • Travel Grants to attend RoadMap Scholar UAB Neural 2017 Program at the University of Alabama, USA. 2017
  • Travel Grants for attending the Eighth International Workshop Statistical Analysis of Neuronal Data (SAND8) at the University of Pittsburgh, USA. 2017
  • Second Best Poster in Behavioral Science, Graduate and Professional Students’ Association’s 8th Annual Research Day at Florida Atlantic University, USA. 2017
  • Graduate Teaching Assistantship, Department of Psychology at Florida Atlantic University, USA. 2014-2020
  • Graduate Assistantship, College of Optometry at the University of Missouri-St. Louis, USA. 2011-2013
  • First in the eastern region of India, The Bausch & Lomb Master Mind course (6 months duration) on the contact lens, India. 2009

Recent Publications

    • Datta, D., Ray, S., Martinez, L., Newman, D., Dalmida, S. G., Hashemi, J., ... & Eckardt, P. (2024). Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics, 14(17), 1866. https://doi.org/10.3390/diagnostics14171866 
    • Datta, D. , George Dalmida, S., Ray, S., Martinez, L. (2024). Caring Data Science. In-review. International Journal for Human Caring. Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckardt, P. Using Machine Learning to Identify Patient Characteristics to Predict Mortality of In-Patients with COVID-19 in South Florida. Frontiers in Digital Health, 5, 1193467. https://doi.org/10.3389/fdgth.2023.1193467 
    • Datta, D., & Hock, H. S. (2018). Measuring the perceptual grouping of non-adjacent surfaces that are invisibly (amodally) or visibly connected. PloS One, 13(11), e0208000. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208000 
    • Datta, D., Ray, S., Newman, D., Martinez, L., George Dalmida, S., Hashemi, J., Sareli, C, Eckard, P. (2024). Evaluating Multi-label and Multiclass-Classifier Performance in Predicting Disease Severity and Mortality of In-Patients with COVID-19 in South Florida. In-preparation
    • Datta, D., George Dalmida, S., Ray, S., Martinez, L. (2024). Caring Data Science. In-review. International Journal for Human Caring.
    • Datta, D., Ray, S., Martinez, L., Newman, D., George Dalmida, S., Hashemi, J., Sareli, C, Eckard, P. (2024). Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity Patients with COVID-19 in South Florida. In-review. Diagnostics
    • Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckardt, P. Using Machine Learning to Identify Patient Characteristics to Predict Mortality of In-Patients with COVID-19 in South Florida. Frontiers in Digital Health, 5, 1193467. https://doi.org/10.3389/fdgth.2023.1193467
    • Datta, D., & Hock, H. S. (2018). Measuring the perceptual grouping of non-adjacent surfaces that are invisibly (amodally) or visibly connected. PloS One, 13(11), e0208000.

Peer-Reviewed Conference Proceedings

    • Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckardt, P. Using random forest classifier to identify important COVID-19 patient characteristics predicting mortality in South Florida, Proceedings of the 4th National Big Data Health Science Conference. BMC Proc 17 (Suppl 19), 32 (2023). https://doi.org/10.1186/s12919-023-00281-y 
    • Shorten, C., Cardenas, E., Khoshgoftaar, T.M., Hashemi, J., George Dalmida, S., Newman, D., Datta, D., Martinez, L., Sareli, C, Eckard, P. (2022). Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification. The 34th IEEE International Conference on Tools with Artificial Intelligence.
    • Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckardt, P. Using random forest classifier to identify important COVID-19 patient characteristics predicting mortality in South Florida, Proceedings of the 4th National Big Data Health Science Conference. BMC Proc 17 (Suppl 19), 32 (2023). https://doi.org/10.1186/s12919-023-00281-y
    • Shorten, C., Khoshgoftaar, T. M., Hashemi, J., George Dalmida, S., Newman, D., Datta, D., Martinez, L., Sareli, C., & Eckard, P. (2022). Predicting the severity of COVID-19 respiratory illness with deep learning. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130670  
    • Shorten, C., Khoshgoftaar, T. M., Hashemi, J., George Dalmida, S., Newman, D., Datta, D., Martinez, L., Sareli, C., & Eckard, P. (2022). Predicting the severity of COVID-19 respiratory illness with deep learning. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130670
    • Shorten, C., Cardenas, E., Khoshgoftaar, T.M., Hashemi, J., George Dalmida, S., Newman, D., Datta, D., Martinez, L., Sareli, C, Eckard, P. (2022). Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification. The 34th IEEE International Conference on Tools with Artificial Intelligence.
    • Datta, D., & Hock, H. (2017). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-Adjacent but Connected Surfaces. Journal of Vision, 17 (10), 168-168. doi: https://doi.org/10.1167/17.10.168    
    • Datta, D., & Hock. S. H. (2017). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-Adjacent but Connected Surfaces, Conference Proceedings, Vision Science Society (VSS)-2017, (pp 49), St. Pete Beach, USA.
    • Datta, D., Mani, R., Swaminathan, M., & Thandalam, T. S. (2009). Fresnel Membrane prisms: Clinical Experience and Pearls of Dispensing, Conference Proceedings, ASIA-ARVO 2009 (pp 58-59), India.
    • Datta, D., & Srinivansan, K. (2009). Clinical Profile of Pseudomyopia – A Retrospective Study, Conference Proceedings, ASIA-ARVO 2009 (pp 128), India.
    • Datta, D., Mani, R., Swaminathan, M., & Thandalam, T. S. (2007). Fresnel Membrane Prisms: Clinical Experience and Pearls of Dispensing, Conference Proceedings, Silver Jubilee Scientific Meeting of Strabismological Society of India (pp 64), India.
    • Negiloni, K., & Datta, D. (2007). A Case Series of Pseudo-Convergence Insufficiency- A Rare Entity of Binocular Disorder, Conference Proceedings, Silver Jubilee Scientific Meeting of Strabismological Society of India (pp 68), India.
    • Datta, D., & Srinivansan, S. (2007). Clinical Profile of Pseudomyopia–A Retrospective Study, Insight, (pp 5) XXV (2). Scientific journal of Medical & Vision Research Foundation, India. Non-Academic Publication
    • Datta, D. (2006). Pediatric Concomitant Strabismus and Their Relationship with Different Ametropias, The Indian Optician (New Delhi), 142-150.
    • Datta, D. (2003). Accommodation, Convergence and Their Relation, Part- II (Nov-Dec), The Indian Optician (New Delhi), 102-108.
    • Datta, D. (2003). Accommodation, Convergence, and Their Relation, Part I (Sep-Oct)), The Indian Optician (New Delhi), 90-94.

Conference Presentation

  • Datta, D., Ray, S., Martinez, L., Newman, D., George Dalmida, S., Hashemi, J., Sareli, C., & Eckard, P. (2024, June 4–7). Boosting Multiclass-classifier Performance with Borderline-SMOTE Augmentation for Predicting Disease Severity and Mortality of In-Patients with COVID-19 in South Florida, [Lightening Talk: Paper presentation followed by Poster Presentation]. 2024 Symposium on Data Science & Statistics, Omni Richmond Hotel, Richmond, VA. https://ww2.amstat.org/meetings/sdss/2024/
  • Ray, S., Datta, D., Newman, D., Bamdas, J.A., Ortega, M., George Dalmida, S., Barenholtz, E. Early Prediction of Alzheimer's Disease and Related Dementia (ADRD) on Preclinical Assessments Data using Machine Learning (ML) tools. (2024 April 15). Division of Research’s 2024 Research Renewal: Share – Network – Collaborate. Florida Atlantic University, Boca Raton, FL, United States. 
  • Martinez, L., George Dalmida, S., Datta, D., Ray, S., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckard, P. (2024, February 21–23). Using Random Forest Classifier to Identify Important COVID-19 Patient Characteristics Predicting Mechanical Ventilation in South Florida. [Paper presentation]. SNRS 38th Annual Conference: Collaborating to Advance Nursing Research, Education, Leadership, and Practice, Le Méridien-Sheraton Hotel Complex, Charlotte, NC, United States. https://snrs.org/events/2024-annual-conference/
  • Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckard, P. (2023, May 23–26). Traditional Machine Learning Model: Identifying Patient Characteristics for Predicting Mortality of Patients with COVID-19 in South Florida, [Lightening Talk: Paper presentation followed by Poster Presentation]. 2023 Symposium on Data Science & Statistics, Union Hotel, St. Louis, MO. https://ww2.amstat.org/meetings/sdss/2023/
  • Thurlow, M., George Dalmida, S., Datta, D. (2023 April 7). Investigating the Influence of Case Management and Housing Stability on ART Medication Adherence for Black/African American Women. Summer Undergraduate Research Fellowship Sponsored by the Office of Undergraduate Research and Inquiry, Florida Atlantic University, Boca Raton, FL, United States.
  • George Dalmida, S., Martinez, L., Datta, D., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckard, P. (2023, March 1–3). A machine learning model for predicting ICU Admission, Mechanical Ventilation, and Mortality of COVID-19 patients in South Florida. [Paper presentation]. SNRS 37th Annual Conference: Building Partnerships in Population Health through Research, Education, and Practice, Rosen Centre, Orlando, FL, United States. https://snrs.org/events/2023-annual-conference/
  • Datta, D., George Dalmida, S., Martinez, L., Newman, D., Hashemi, J., Khoshgoftaar, T. M., Shorten, C., Sareli, C., & Eckard, P. (2023, February 10–11). Using Random Forest Classifier to Identify Important COVID-19 Patient Characteristics Predicting Mortality in South Florida [Paper presentation]. 4th Annual National Big Data Health Science Conference, The Pastides Alumni Center, University of South Carolina, Orlando, Columbia, SC. https://www.sc-bdhs-conference.org/program-2023/
  • Datta, D., George Dalmida, S. (2022 July 21-25). Development of a Caring Data Science Framework to Predict COVID-19 Outcomes and Drive Healthcare Transformation [Paper presentation]. 33rd International Nursing Research Congress 2022, The Edinburgh International Conference Centre, Edinburgh, Scotland. https://www.sigmanursing.org/connect-engage/meetings-events/congress
  • Datta, D., Hock, H. (2022 July 11-15). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-adjacent but Connected Surfaces [Paper presentation]. BMVA Computer Vision Summer School 2022, Thomas Paine Study Center, Norwich, UK. https://cvss2022-uea.uk/
  • Thurlow, M., George Dalmida, S., Datta, D. (2022 August 18). Using Traditional Machine Learning to Identify Important COVID-19 Patient Characteristics to Help Predict Disease Outcome in South Florida. Summer Undergraduate Research Fellowship Sponsored by the Office of Undergraduate Research and Inquiry, Florida Atlantic University, Boca Raton, FL, United States.
  • Datta, D., & Hock, H. (2019 April 5). Grouping of Adjacent and Non-Adjacent Surface when an Object Partially Occludes Other Objects in the 2 and 3-Dimensional Retinal Projection of a Scene, 2019 Graduate and Professional Research Day, Florida Atlantic University, Boca Raton, USA.
  • Datta, D., & Hock, H. (2018). Dynamic Grouping Motion and Amodal Completion, Neural Conference 2018, University of Alabama at Birmingham, USA. https://www.uab.edu/medicine/rms/neural-conference/neural-conference-2018
  • Datta, D., & Hock, H. (2017). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-Adjacent but Connected Surfaces, 58th Annual Meeting of Psychonomic Society, Vancouver, Canada.
  • Datta, D., & Hock, H. (2017). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-Adjacent but Connected Surfaces, Vision Science Society (VSS)-2017, St. Pete Beach, USA.
  • Datta, D., & Hock, H. (2017). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-Adjacent but Connected Surfaces, Neural Conference 2017, University of Alabama at Birmingham, USA. https://www.uab.edu/medicine/rms/neural-conference/neural-conference-2017
  • Datta, D., & Hock, H. (2017 March 24). Solving the Complexity of Object Occlusions in Scenes: The Grouping of Adjacent Surfaces and Non-Adjacent but Connected Surfaces, 2017 Graduate and Professional Research Day, Florida Atlantic University, Boca Raton, USA.

 

Honors/Awards

Invited Speaker

Education