Zaid Farooq
AI Researcher & ML Engineer
Zaid Farooq is an AI Researcher specializing in foundational models, vision-language modeling, and agentic AI systems. At Chinar Quantum AI, he contributes to strategic AI research, curriculum design, and regional AI training, helping develop high-impact learning experiences for diverse learners.
His background includes research at MBZUAI's BioMedIA Lab on fetal ultrasound imaging, large-scale dataset curation, and image-language model adaptation. He also brings experience from Amazon in data-driven operational optimization and ML-based problem solving.
Experience
AI ResearcherPresent
Chinar Quantum AI
- Supports strategic AI research across foundational models and agentic AI systems.
- Leads regional AI training sessions on foundational and advanced AI concepts.
- Designs and refines curriculum content to meet industry standards and learner needs.
- Ensures seamless execution and communication between research, training, and innovation teams.
- Delivers reports and updates to leadership, ensuring high-quality program execution.
Research Assistant
BioMedIA Lab - MBZUAI
- Worked on adapting CLIP for building a foundational model for fetal ultrasound imaging.
- Curated and preprocessed a dataset of 180,000+ medical images for training and evaluation.
- Extracted expert-authored image-caption pairs from fetal echocardiography texts.
- Contributed to a research paper on congenital heart disease (CHD) detection.
Transportation Specialist
Amazon India
- Managed and analyzed large operational datasets related to shipping and logistics.
- Improved routing workflows and operational efficiency through data-driven solutions.
- Ensured accuracy of shipping records and optimized logistics pipelines.
Education
B.S. in Mechanical Engineering
University of Kashmir
Projects
Machine Learning for Material Discovery - MBZUAI
Analyzed GNNs, GFlowNets, and active learning methods for predicting stable materials and novel properties; evaluated Matbench and AFLOW benchmarks; explored inverse design using PINNs.
House Pricing Analysis
Performed EDA, feature engineering, and feature selection to build regression models for price prediction.
Chest X-ray Disease Classification
Built a deep learning model for multi-class classification using augmentation to improve generalization.
AI Agents with Hugging Face
Developed multi-agent RAG systems using smolagents, LangGraph, and LlamaIndex; integrated tool calling and LoRA-fine-tuned LLM-based agents.