Prof. Dr. Muhammad Moazam Fraz has been honored by the Higher Education Commission (HEC) of Pakistan for his exceptional global research influence and valuable contribution to the advancement of knowledge in his field.
Prof Dr Muhammad Moazam Fraz, Associate Dean (Computing) SEECS, was awarded the highly prestigious Tamgha-i-Imtiaz by the President of the Islamic Republic of Pakistan on May 13, 2026.
Cultivating young leaders to engineer a sustainable, climate-resilient future. A hands-on, 5-day interactive camp dedicated to inspiring the next generation to solve climate challenges using Artificial Intelligence, creativity, and green tech.
Camp Essentials
8 – 11 June, 2026 (Daily)
12 June: Capstone Day (10AM-4:30PM)
NUST SEECS, Islamabad
Sector H-12 Campus
Young Eco-Innovators
Climate Tech Enthusiasts
No prior coding/AI needed!
Just Curiosity & Passion
Empowering future leaders with the technical literacy and strategic insight to address major ecological and cooling issues globally.
Interactively train computer vision and classification models to solve real-world smart energy efficiency and waste management problems.
Inspect, test, and break AI datasets to discover biases and understand their ecological impact on physical management systems.
Design and deploy specialized AI personas programmed to deliver localized climate action directives and energy conservation tips.
Master prompt engineering techniques to synthesize high-impact generative art, comics, and stories on environmental sustainability.
Team up to brainstorm, formulate a Concept Canvas, and pitch your custom Green Tech solution at the physical exhibition.
Throughout the 5-day lab, you won't just learn theory; you will assemble a functional, collaborative tech prototype that builds sequentially day by day, culminating in a live exhibition presentation.
Establish energy saving systems and automatic waste sorters while observing how AI algorithms see waste and power usage.
Configure intelligent climate bots and prompt generative tools to illustrate and visualize climate challenges visually.
Define training datasets, draft functional wireframes on a Concept Canvas, and assemble a prototype to pitch to judges.
Explore our detailed, activity-driven itinerary. Switch between daily modules using the dynamic controller below.
A modern toolkit of software suite parameters and AI modeling frameworks kids will master during the camp.
Google image & visual recognition dataset structuring platform.
Natural Language processing system instruction engineering (LLMs).
Pixel synthesis, storyboards, and composition prompt styling.
Interactive planning boards to design data inputs and logic paths.
Test your eco-tech intuition with our quick interactive quiz. Find out how AI assists in sustainable cooling! Click an option to test your skills live.
What is the main advantage of using AI in "Smart Cooling" grids?
Learn from prominent scholars, researchers, and technical architects leading NUST computer vision projects.
Professor & Associate Dean
NUST SEECS
Assistant Professor
NUST SEECS
Faculty Member & Scholar
NUST SEECS
Faculty Member & Researcher
NUST SEECS
This initiative is funded by the UK Government through Project: APP47457, titled “Super-efficient Sustainable Cooling Solution for All Applications (S2Cool)” under the Ayrton Challenge Programme of the UK Research and Innovation (UKRI); however, the views expressed do not necessarily reflect the UK Government’s official policies.
The Project: APP47457 is led/implemented by Northumbria University UK along with collaborating global partners.
Get in touch with the project coordination team at NUST SEECS if you need administrative information or details on seat reservations.
SEECS NUST, Sector H-12, Islamabad, Pakistan
9th October 2025 (Thursday) Venue: Seminar Hall, PG Block, SEECS
This activity is funded by the DAAD Germany , through the Project ID: 57708351 , titled “ SLUM-i: A Remote Sensing Based Intelligent Framework for Detection and Predictive Growth Analysis of Slums for Sustainable Socio-Economic Development of Pakistan “
Dr. Sheraz’s session on remote sensing for crop stress was incredibly practical. I now see how we can use water proxies to reduce post-harvest losses significantly.
The GeoAI for Informal Settlements talk by Dr. Khurram was an eye-opener. It perfectly mapped out how we can improve livability in dense areas through data.
Learning about smart farm mechanization from Dr. Sultan gave me a new perspective on energy efficiency. The S2Cool mission is exactly what the industry needs.
Ms. Quratulain bridged the gap between policy and technical execution. The session on Climate & Water Sustainability was vital for our adaptation strategies.
Dr. Zuhair’s analytics on heatwaves and floods provided the hard data we needed. This training equips you to make evidence-based infrastructure decisions.
Prof. Berns made GeoAI concepts so accessible. I can now apply cooling-demand mapping techniques directly to my thesis research on heat hotspots.
Dr. Sheraz’s session on remote sensing for crop stress was incredibly practical. I now see how we can use water proxies to reduce post-harvest losses significantly.
The GeoAI for Informal Settlements talk by Dr. Khurram was an eye-opener. It perfectly mapped out how we can improve livability in dense areas through data.
Learning about smart farm mechanization from Dr. Sultan gave me a new perspective on energy efficiency. The S2Cool mission is exactly what the industry needs.
Exploring how AI, multilingual content, and digitized curricula can accelerate vocational training outcomes in Pakistan, featuring live system demonstrations.
Share Progress: Present Ba-Ikhtiyar Jawan progress and practical approaches to curriculum digitization and multimodal learning for TVET.
Live Demonstrations: Showcase AI solutions for manual translation, personalized learning, and Urdu tutoring for underserved learners.
Collect Input: Gather stakeholder feedback to strengthen industry alignment and deployability in offline/edge settings.
Build Roadmaps: Produce actionable recommendations and a short roadmap for partnerships and strategic scaling.
Live showcases of three AI-powered learning systems developed to accelerate and localize technical training.
Adapts learning pathways via learner profiling and recommendation intelligence. Uses bilingual datasets to support automated creation of culturally relevant quizzes, tutorials, and microlearning modules across multiple delivery modes.
Designed for underserved, low-literacy Urdu speakers. Features multimodal text/speech interaction and fine-tuned language models for interactive learning, contributing curated low-resource Urdu datasets to the vocational domain.
Converts degraded, scanned industrial manuals into reliable instructional content. Uses instruction-tuned language models to preserve procedural meaning, technical terminology, and usability even under heavy OCR noise.
Theme: Digitization of TVET through AI-driven adaptive tools, multilingual tutoring, and robust machine translation pipelines.
The leadership and organizing committee behind the Ba-Ikhtiyar Jawan Seminar.
Professor & Associate Dean
SEECS, NUST
Assistant Professor
SEECS, NUST
Associate Professor & HoD Dept. of Computer Science
SEECS, NUST
Partners
This event is funded by the German Academic Exchange Service (DAAD) under the project titled Ba-Ikhtiyar Jawan: Upscaling and Digitization of Vocational Education Curriculum.
Build an LLM-powered RAG assistant and deploy Computer Vision models. A hands-on workshop dedicated to solving climate challenges.
By the end of Day 4, participants will be able to:
Build an LLM-powered RAG assistant for climate/sustainability/cooling knowledge and reporting.
Train and evaluate computer vision models for environmental monitoring (heat islands, deforestation, land cover).
Productionize models with MLOps/LLOps: Experiment tracking, model registry, CI/CD, monitoring, containerization, scalable inference.
Write clean, testable AI software: Modular pipelines, APIs, packaging, performance basics.
Detailed breakdown of lectures and hands-on labs.
| 09:00 – 10:30 | Lecture 1 (90m) LLMs, climate domain framing, RAG foundations |
| 10:45 – 12:15 | Lecture 2 (90m) Embeddings, retrieval, evaluation, safety/grounding |
| 13:30 – 16:30 |
Lab (180m)
Build Climate+Cooling RAG assistant v1
Hands-on implementation of the concepts learned. |
| 09:00 – 10:30 | Lecture 1 (90m) Remote sensing + CV tasks for sustainability |
| 10:45 – 12:15 | Lecture 2 (90m) Segmentation/classification, metrics, robustness |
| 13:30 – 16:30 | Lab (180m) Heat island / land cover / deforestation model + inference |
| 09:00 – 10:30 | Lecture 1 (90m) MLOps lifecycle, experiment tracking, reproducibility |
| 10:45 – 12:15 | Lecture 2 (90m) CI/CD, model registry, monitoring, scaling (Ray/Spark) |
| 13:30 – 16:30 | Lab (180m) Containerize + deploy APIs + MLflow registry + basic monitoring |
| 09:00 – 10:30 | Lecture 1 (90m) Clean architecture for AI systems + testing strategy |
| 10:45 – 12:15 | Lecture 2 (90m) Data pipelines, APIs, packaging, performance basics |
| 13:30 – 16:30 | Lab (180m) Refactor to production-ready repo + tests + final demo |
A deployable prototype that helps decision-makers identify heat-risk zones, understand sustainability drivers, and generate evidence-grounded cooling interventions. The project evolves across all 4 days.
/cv/predict (tile → mask + stats)
/rag/ask (question → cited answer)
Supported By
This activity is funded by the UK Government through the Project: APP47457, titled “Super-efficient Sustainable Cooling Solution for All Applications (S2Cool)” under the Ayrton Challenge Programme of the UK Research and Innovation (UKRI); however, the views expressed do not necessarily reflect the UK Government’s official policies. The Project: APP47457 is implemented/led by Northumbria University UK together with partners.
Multilingual Personalized Vocational Training Using Recommendation Intelligence for Youth Empowerment is a project that reimagines how skill development can be delivered to young learners across diverse cultural and linguistic backgrounds. In many regions, especially where resources are scarce, vocational training is still restricted by language barriers, outdated content, and rigid, one-size-fits-all teaching methods. This project directly tackles those limitations by building a platform that adapts to each learner. Using multilingual support in Urdu and English, the system makes high-quality vocational training accessible to students who might otherwise be excluded, preparing them with practical skills for employability.
The proposed system includes an intelligent recommendation engine that creates personalized learning pathways. Instead of presenting the same modules to every student, the system actively learns from each individual’s background, prior knowledge, language preferences, and engagement style. By combining learner profiling and collaborative filtering, the platform suggests the right content at the right time, whether that’s a step-by-step tutorial, a microlearning module, or an adaptive quiz. To strengthen inclusivity, the project also curates bilingual vocational materials originally in German, translating and localizing them into Urdu and English. This approach not only ensures cultural and linguistic relevance but also makes previously inaccessible resources usable for learners in underrepresented communities.
Beyond being a technical prototype, the project is designed with real-world impact in mind. Its adaptive architecture means that as learners progress, the system automatically adjusts difficulty, pacing, and even content format (text, audio, video) to fit their needs. The result is a dynamic and empowering experience that helps learners stay engaged and retain skills more effectively. By aligning training modules with national vocational standards (TEVTA & NAVTTC) and incorporating industry-relevant updates like hybrid and electric vehicle repair, the platform equips students with knowledge that matches labor market demands. More importantly, it levels the playing field, giving marginalized youth an equal opportunity to acquire skills and pursue livelihoods, while also opening new avenues for research into human-centered AI and the future of personalized learning technologies.
Publication Success
Dr. Muhammad Naseer Bajwa delivered a compelling keynote address at the 5th UW-POFs Joint Conference on Academia’s Collaboration with Industrial Organisation (CACIO-2025) held on December 17th at the University of Wah.
The talk focused on the critical application of Trustworthy AI in medical diagnosis, with a particular emphasis on advancements in ophthalmology.
The keynote highlighted how explainability, reliability, and uncertainty awareness in AI systems can enhance clinical decision-making and build trust among healthcare professionals. The session also discussed challenges and opportunities in translating AI research into real-world medical practice through academia–industry collaboration.
The conference provided a valuable platform for dialogue between researchers, clinicians, and industry representatives on leveraging emerging technologies for societal impact.
This research was conducted at MachVis as part of the project:
From Drones to Decisions: LLM-Based Crop Health Insights from RGB and Multispectral Data ↗