About Me

Hi there! I'm an MS student at FAST NUCES FAST Logo, Department of Data Science, advised by Dr. Rafi. I obtained my B.E. degree from the School of Engineering, NED University NED Logo. Currently, I am working as a Teaching Assistant at FAST NUCES for the course Advanced Computer Vision under Dr. Maria. I also worked as a Data Scientist at GSK GSK Logo in their Global Supply Chain Tech Department building computer vision solutions.

My research interests lie in developing reliable machine learning algorithms and uncertainty quantification for risk-aware decision-making in computer vision and natural language processing frameworks for real-world applications, with a focus on aligning Large Foundation Models (LLMs and VLMs).

Research Interests

  • Trustworthy AI: Safety, Uncertainty, Risk-aware Decision-Making.
  • Alignment of Foundation Models: LLMs, VLMs, Robust and Safe Learning.
I am very excited about potential collaboration opportunities! If you share similar research interests and find my work interesting, I warmly welcome you to contact me via email!

Latest News

Nov 2025

๐ŸŽ‰ Emmad triumphs! Thesis "Risk-Incorporated Retrieval for Facial Images Using Uncertainty Quantification" successfully defended at FAST NUCES!

Jul 2025

๐ŸŽ‰ Appointed as a Teaching Assistant at FAST NUCES!

Aug 2023

๐Ÿ“˜ Our paper 'Mango Farming Optimization With AI: Boosting Cultivation Efficiency' was accepted at an IEEE conference in Tabuk, Saudi Arabia.

Sep 2022

๐Ÿ† Received Best FYP Award 2022 at NED University of Engineering & Technology!

Feb 2022

๐Ÿ’ก Received funding from the North Carolinaโ€“NED Forum for our Soft Robotics for Healthcare project.

Publications & Preprint

MS Thesis 2025
Risk-Incorporated Retrieval for Facial Images

Risk-Incorporated Retrieval for Facial Images Using Uncertainty Quantification

Muhammad Emmad Siddiqui

TL;DR: Proposes a risk-aware facial-image retrieval framework that fuses uncertainty estimation & conformal prediction with self-supervised (DINO) feature learning to reduce risky misidentifications. Shows on SCFace that accuracy improves and confidence-driven, dynamic result-set restrictions let the system control retrieval risk.

MS Thesis, FAST NUCES 2025

IEEE 2023
Mango Farming Optimization With AI

Mango Farming Optimization With AI: Boosting Cultivation Efficiency

Syed Umaid Ahmed, Muhammad Emmad Siddiqui

TL;DR: We introduce Mango Health Detection, a cutting-edge detection model demonstrating superior performance and safety capabilities.

IEEE Explore, TABUK 2023

GitHub Repositories

Education

2023.08 - 2025.12 (Expected)

M.S. in Data Science, FAST NUCES

Advisor: Dr. Rafi

2018.08 - 2022.07

B.E., School of Engineering, NED University

Advisor: Dr. Wasif

Contact

k238020@nu.edu.pk

Karachi, Pakistan