Data Science and Computational Biology Lab

Research

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Dr. Vanessa Aguiar-Pulido is an Assistant Professor at the University of Miami. She holds an appointment in the Dept. of Computer Science and the Dept. of Informatics and Health Data Science, and is a member of the Sylvester Comprehensive Cancer Center. She is also an Adjunct Assistant Prof. of Neuroscience at Weill Cornell Medicine. Dr. Aguiar-Pulido is passionate about problem solving in biomedical research, with a focus on developing AI-based approaches that will impact diagnosis and therapeutics of rare and complex genetic disorders. Her background and interests include machine learning, artificial intelligence, genetics, neuroscience, cancer, computational biology and omics in general.

Her lab is at the interface of Computer Science and Biomedical Sciences, and her research focuses on studying the role of the dark genome in disease, understanding the underlying genetic causes of monogenic and complex disorders, and how these findings can be applied to clinical practice.

The advent of high-throughput sequencing technologies has ushered in a new era of genetic inquiry, decreasing the economic costs and turnaround time notably. However, with this growth of 'omic' data, computational challenges arise. Not only the volume of data is increasing but also the dimensionality, expanding the search space exponentially. As a result, many problems involve such a large set of possible solutions that finding the optimal one in a reasonable amount of time is not feasible. Therefore, developing new approaches for 'intelligent' data analysis is necessary. The work of Dr. Aguiar-Pulido shows how artificial intelligence can be applied to identify potential genetic risk factors in complex genetic disorders (i.e., those that require genetic predisposition and environmental factors for the patient to develop the disease). As part of her research, she has developed methods based on evolutionary computation and strategies using machine learning for candidate gene analysis and identification. In her most recent work, she devised a comprehensive approach that uses machine learning (more specifically, embedded feature selection) to pinpoint biological pathways affected in structural birth defects employing ethnically diverse cohorts. Approaches such as the latter are fundamental to further human disease research in the age of precision medicine.

Funded projects:

• Poison exons in epilepsy and neurodevelopment (2024-2029). NIH (Subaward PI)
• When AI Sees and Says: Making Multimodal AI Understandable and Aligned with Humans (2026-2027). University of Miami (Co-PI)
• Feasibility of Self-Applied Vagus Nerve Stimulation for Chemotherapy Induced Peripheral Neuropathy (2025-2026). Sylvester Comprehensive Cancer Center, University of Miami (Co-PI)
• Understanding Alzheimer disease: Exploring genetic and non-genetic factors in risk and protection (2024-2026). COINS (Co-Investigator)
• Using AI to Predict Real Time Speech and Dis/Ability in Diverse Inclusive Preschool Classrooms (2024-2026). University of Miami (PI)
• Devising interpretable biologically-inspired AI techniques for medical imaging (2024-2025). University of Miami (PI)
• Center for Accelerated Real Time Analytics (CARTA) (2021-2022). NSF (Project Leader)
• Epilepsy Multiplatform Variant Prediction (2020-2025). NIH (Subaward PI)
• Progenitor regulation underlying cortical interneuron specification (2018-2023). NIH (Key Personnel)
• Risk Genes and Environmental Interactions in Neural Tube Defects (2018-2023). NIH (Researcher)
• Hybrid sequencing for improved genetic diagnosis in clinical settings (2019-2020). Sackler Research Grant (Principal Investigator)

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