Graph Neural Networks

I am deeply interested in Graph Neural Networks (GNNs) due to their powerful ability to model complex relationships and interactions within data. GNNs have vast applications in various fields, including drug discovery, social network analysis, and recommendation systems. My work in this area includes:

  • Developing models for drug response prediction using GNNs to forecast the efficacy of pharmaceuticals based on molecular structures and biological interactions.
  • Engaging in self-study courses like CS224w to deepen my understanding of GNNs and their applications.

Drug Discovery

Drug discovery fascinates me because of its potential to significantly impact human health and wellbeing. I am particularly interested in how computational methods and machine learning can streamline the drug discovery process. My relevant experiences include:

  • Conducting my PharmD thesis on drug response prediction, focusing on preclinical studies where responses were previously unknown.
  • Leveraging bioinformatics and computational biology tools to identify potential drug targets and repurposed drugs.

Protein Design

Proteins play a crucial role in numerous biological processes, and designing proteins with specific functions can lead to breakthroughs in medicine and biotechnology. My interest in protein design includes:

  • Using computational methods to predict protein structures and functions.
  • Participating in projects that explore the intersection of protein design and precision medicine.

Deep Learning

Deep learning is a cornerstone of modern AI and has revolutionized many aspects of science and technology. My passion for deep learning is reflected in my projects and self-directed learning, including:

  • Applying deep learning techniques to medical image processing and pharmaceutical sciences.
  • Completing courses in deep learning to continuously enhance my skills and stay updated with the latest advancements in the field.

Precision Medicine

Precision medicine aims to tailor medical treatments to individual patients based on their genetic, environmental, and lifestyle factors. I am motivated by the potential of precision medicine to improve treatment outcomes and reduce adverse effects. My work in this domain includes:

  • Integrating genomic features and drug-response data to develop personalized treatment plans.
  • Conducting research on pharmacogenomics to understand how genetic differences influence drug responses.

Pharmacogenomics

Pharmacogenomics is the study of how genes affect a person’s response to drugs. This field is crucial for the development of personalized medicine. My engagement in pharmacogenomics involves:

  • Exploring the genetic factors that contribute to variability in drug responses.
  • Utilizing computational tools to analyze genomic data and predict drug efficacy and safety.

Computational Biology

Computational biology combines biology, computer science, and mathematics to solve complex biological problems. My interest in this field is driven by its potential to uncover new insights into biological processes and disease mechanisms. My relevant experiences include:

  • Working as a researcher in the Bioinformatics and Computational Biology Laboratory (BCB), where I collaborated with experts to advance the understanding of biological systems.
  • Applying computational models to analyze biological data and predict outcomes in various research projects.

Chemoinformatics

Chemoinformatics involves the use of computer and informational techniques applied to chemical problems. This interdisciplinary field is crucial for drug discovery and development. My interest in chemoinformatics includes:

  • Utilizing chemoinformatics tools to analyze chemical data and predict molecular properties.
  • Developing computational models to identify potential drug candidates and optimize their properties for better efficacy and safety.

Computer Vision for Medical Images

Computer vision in medical imaging involves the use of algorithms and techniques to process and analyze medical images. This field is essential for improving diagnostic accuracy and treatment planning. My work in this area includes:

  • Developing deep learning models for medical image segmentation and classification to aid in disease diagnosis.
  • Working on projects involving echocardiographic image analysis to detect heart regions and estimate clinical information such as ejection fraction.

Molecular Dynamics

Molecular dynamics (MD) simulations are a powerful tool for studying the physical movements of atoms and molecules. This technique is widely used in the study of biological systems and drug design. My interest in molecular dynamics includes:

  • Performing MD simulations to understand the behavior of biomolecules at an atomic level.
  • Using MD to study protein-ligand interactions and predict the stability and binding affinity of drug candidates.

Large Language Models in Healthcare

Large language models (LLMs) have shown great promise in transforming healthcare by enabling advanced natural language processing capabilities. My interest in LLMs in healthcare includes:

  • Utilizing LLMs to improve clinical decision support systems by providing accurate and context-aware recommendations based on patient data and medical literature.
  • Developing applications that leverage LLMs to assist in medical documentation, reducing the administrative burden on healthcare professionals.
  • Exploring the potential of LLMs in personalized medicine, such as tailoring treatment plans based on a comprehensive analysis of patient histories and clinical notes.