cv

Basics

Name Zoher Kachwala
Label PhD Candidate & Student Researcher in AI/ML
Url https://zoher15.github.io/
Summary PhD candidate specializing in Large Language Models with focus on evaluation, interpretability, and production deployment. Research expertise spans rigorous model assessment, transparent AI systems, and real-world applications for semantic search and safe content generation. Experienced in research-to-production deployment, rigorous experimental design, and translating complex AI research into practical tools and frameworks.

Education

Publications

Work

  • 2022.08 - 2024.12
    Teaching Assistant
    Introduction to Network Science
    Led collaborative Python-based tutorials on large-scale network analysis; guided students through graph neural network concepts and dynamic analytics for real-world applications.
  • 2020.08 - 2020.12
    Teaching Assistant
    Applied Machine Learning
    Collaborated on PyTorch-based model development curriculum; emphasized reproducible research practices, scalable inference validation, and deployment-ready system design.
  • 2019.08 - 2021.12
    Teaching Assistant
    Elements of Artificial Intelligence
    Designed scalable autograding systems and provided collaborative support on foundational AI concepts including search, logic, and reasoning for 300+ students annually.
  • 2018.05 - 2018.08
    Technology Consultant Intern
    PricewaterhouseCoopers
    Collaborated on enterprise-scale data integration projects, focusing on automation of validation pipelines and scalable audit-ready logic modeling for large-scale systems.

Skills

Research Areas
AI Interpretability
Model Evaluation
Large Language Models
Multimodal Systems
AI Safety
Programming
Python (expert)
JavaScript
C++
SQL
Bash
CUDA
ML Frameworks
PyTorch
Hugging Face
TensorFlow
Scikit-learn
Distributed Training
Data Visualization
matplotlib
seaborn
pandas visualization
interactive notebooks
Systems & Infrastructure
Git
Docker
Linux
Google Cloud Platform
Large-scale Computing
Research Methods
Model Evaluation
Rigorous Experimentation
Benchmark Development
Reproducible Science