Current Academic Work

Ongoing research, coursework, and academic projects as part of my Master's degree in Data Science.

Master's Thesis Research

Advanced Machine Learning Techniques for Predictive Analytics in Complex Healthcare Datasets

My thesis research focuses on developing novel machine learning approaches to improve predictive accuracy in healthcare analytics. The work combines deep learning architectures with traditional statistical methods to create robust models that can handle the complexity and heterogeneity of medical data.

Research Objectives:

  • • Develop hybrid ML models combining neural networks with statistical approaches
  • • Improve prediction accuracy for patient outcome forecasting
  • • Address data quality and missing value challenges in healthcare datasets
  • • Create interpretable models for clinical decision support
  • • Validate approaches across multiple healthcare institutions

Methodology:

The research employs a multi-phase approach combining literature review, algorithm development, experimental validation, and real-world testing. I'm working with anonymized patient data from three major healthcare systems to ensure generalizability of results.

Deep Learning Healthcare Analytics Predictive Modeling Statistical Methods Data Quality

Thesis Details

Advisor:

Dr. Sarah Johnson, PhD

Co-Advisor:

Dr. Michael Chen, MD, PhD

Expected Defense:

Spring 2025

Status:

Data Collection Complete

Progress
Literature Review 100%
Data Collection 100%
Model Development 75%
Validation 45%
Writing 25%

Current Coursework (Fall 2024)

Advanced Deep Learning (CS 7643)

Exploring cutting-edge deep learning architectures including Transformers, GANs, and reinforcement learning. Focus on both theoretical foundations and practical implementations.

Key Topics:

  • • Attention mechanisms and Transformer architectures
  • • Generative Adversarial Networks
  • • Deep Reinforcement Learning
  • • Neural Architecture Search
Professor: Dr. Emily Rodriguez A

Statistical Learning Theory (STAT 8803)

Mathematical foundations of machine learning, covering PAC learning, VC theory, and generalization bounds. Emphasis on theoretical understanding of learning algorithms.

Key Topics:

  • • PAC learning framework
  • • VC dimension and Rademacher complexity
  • • Generalization bounds
  • • Online learning algorithms
Professor: Dr. David Park A-

Research Methods in Data Science (CS 8001)

Comprehensive course on research methodology, experimental design, and scientific writing for data science research. Includes ethics and reproducibility considerations.

Key Topics:

  • • Experimental design and hypothesis testing
  • • Research ethics and bias mitigation
  • • Reproducible research practices
  • • Scientific communication
Professor: Dr. Lisa Chen A

Advanced Data Visualization (CS 7450)

Theory and practice of data visualization, covering perception, interaction design, and advanced visualization techniques for complex datasets.

Key Topics:

  • • Visual perception and cognition
  • • Interactive visualization design
  • • High-dimensional data visualization
  • • Visualization evaluation methods
Professor: Dr. Alex Thompson A

Current Research Projects

Federated Learning for Healthcare Data Privacy

Collaborative research project exploring federated learning approaches to train machine learning models on distributed healthcare datasets while preserving patient privacy. Working with industry partners and multiple healthcare institutions.

Collaborators:

Dr. Sarah Johnson (Advisor), Dr. Michael Chen (Co-Advisor), Healthcare Analytics Lab, TechCorp Research Division

Federated Learning Privacy Healthcare

Status

Active Research

Expected completion: Spring 2025

Interpretable AI for Financial Risk Assessment

Developing explainable machine learning models for financial risk assessment that provide both high accuracy and interpretable insights for regulatory compliance and decision-making.

Collaborators:

Dr. David Park, Financial Analytics Research Group, FinTech Innovation Lab

Explainable AI Finance Risk Assessment

Status

Paper Submitted

Under review at ICML 2025

Academic Activities & Service

Teaching & Mentoring

Graduate Teaching Assistant

Introduction to Data Science (CS 4803)

Fall 2024

Undergraduate Research Mentor

Supervising 3 undergraduate students on ML projects

2024 - Present

Professional Service

Reviewer

ICML 2025, NeurIPS 2024 Workshop

2024

Student Representative

Graduate Student Council, Data Science Program

2023 - Present

Academic Resources

Access to my research materials, course notes, and academic collaborations.

View Publications Full CV Research Gate Google Scholar