Education

Graduate education in AI and systems, translated into practical backend and platform engineering judgment.

MS in Computer Science

Specialization: Artificial Intelligence

Georgia Institute of Technology · Graduated May 2026

Graduate work focused on AI, machine learning, NLP, computer vision, and responsible AI — strengthening applied judgment for backend and platform engineering with ML-aware systems.

Relevant coursework

Graduate coursework grouped by AI, systems, and responsible engineering themes.

AI / ML

Machine Learning
Deep Learning
Reinforcement Learning
Knowledge-Based AI

NLP / Computer Vision

Natural Language Processing
Computer Vision
Speech Technology
Multimodal AI

Systems / Databases

Graduate Algorithms
Database Systems
Software Architecture
High-Performance Computing

Responsible AI / Ethics

AI Ethics and Fairness
Human-AI Interaction
Research Methods

Academic projects and research

Selected graduate work demonstrating AI depth and research-oriented problem solving.

Smart-home continual learning research proposal

Research proposal exploring continual learning and human-in-the-loop feedback for adaptive smart-home systems.

Continual Learning
Human-in-the-Loop
Research

On-device learning literature review

Systematic literature review on mobile-edge training and on-device learning approaches.

Edge ML
Literature Review
Mobile AI

Transformer implementation

From-scratch transformer implementation to deepen understanding of attention-based sequence modeling.

Transformers
Deep Learning
NLP

KVMemNet NLP project

Key-value memory network project applying neural architectures to question answering workflows.

NLP
Memory Networks
PyTorch

ViT-based indoor scene classification

Vision Transformer approach for indoor scene classification and spatial understanding tasks.

Computer Vision
ViT
Classification

Engineering translation

How graduate AI education supports backend and platform work.

  • Better applied AI judgment when designing backend and platform systems.
  • Stronger research-backed problem solving for ambiguous engineering challenges.
  • Ability to build AI-assisted developer tools with practical workflow impact.
  • Broader systems and ML perspective when evaluating architecture trade-offs.