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Personalized learning through GenAI

Do You Remember School?

Think back to your school days.

Did you ever sit in class and feel like the teacher was going too fast? Or maybe too slow?

Maybe you wanted to ask a question but felt too shy, worried that others might laugh.

Now imagine an education system that adjusts to you.

One that goes at your pace, focuses on your strengths, and helps with your struggles.

A system that explains things in your own language gives extra practice when you need it and never runs out of patience.

Sounds like a dream? It isn’t. Thanks to Generative AI (GenAI), this dream is becoming a reality.

What Exactly is Generative AI? 

Think of GenAI like a master chef. This chef has studied thousands of recipes. If you ask them for dinner, they won’t just copy an old dish. They will create something new, based on your taste and the ingredients you have.

Generative AI works in the same way, but with information instead of food. It can create explanations, examples, and practice exercises designed just for you.

Behind this are Large Language Models (LLMs), AI systems trained on enormous amounts of text. That is why they can “talk” with us in natural language, explain complex ideas simply, and even answer follow-up questions just like a real tutor.

Goodbye “One-Size-Fits-All.” Hello Personalized Learning.

For too long, education has been like a factory: one lesson, one style, for every student. But we all know that is not how real people learn.

Here is how GenAI is changing the game:

  • Learn at Your Own Speed: If you have mastered something, you move ahead. If you are stuck, the AI slows down, gives examples, and gently quizzes you until you understand.
  • Content That Fits You: Textbooks are fixed. AI lessons are flexible. They can be simplified, made more detailed, or even connected to your personal interests so learning feels engaging.
  • A Tutor That Never Sleeps: Got a question at midnight? Your AI tutor is ready, calm, and endlessly patient.
  • Deep Personalization with Topic Mapping: Every course is broken into topics and subtopics. If a lesson has prerequisites, the system looks back at how you performed in those earlier topics. If you struggled there, the AI adjusts the new lesson, fills the gaps, and makes sure you are ready before moving forward.
  • Understanding at Every Level: You will not just be tested on memorization. The AI checks your understanding at different levels, whether you can recall basics, apply knowledge, or solve real-world problems.
  • LLMs as Judges and Guides: The system uses AI not only to explain but also to evaluate. It can detect knowledge gaps, highlight weak areas, and then generate personalized exercises to help you improve step by step.

This means no more being left behind and no more being held back.

How the Ba-Ikhtiyar Jawan Project is Bringing it to Pakistan

Here in Pakistan, the Ba-Ikhtiyar Jawan Project is taking this technology and applying it to a real challenge: vocational training for our youth.

At present, NAVTTC and TEVTA are offering vocational courses of three months, six months, and one year. While this is useful, when we look at countries like India, the UK, and others, we find that their vocational systems are more advanced. They provide training in multiple levels with updated content that matches global industry standards.

Our project will bridge this gap. We will not only follow the structure of the three-month, six-month, and one-year programs, but we will also identify the advanced topics that are being taught internationally but missing in Pakistan. These missing areas will be suggested, explained, and integrated into the curriculum.

In addition, we will also highlight and strengthen important topics that are present in the curriculum but not given enough focus. This ensures that learners are not only keeping pace with the world but also gaining the in-depth understanding needed to compete globally.

Here is what we are doing differently:

  • A Smarter Curriculum: With GenAI, we have created a large library of practice questions and answers designed to test real understanding, not just memory.
  • Your Personal Learning Roadmap: The system figures out what a student already knows, checks how they performed on related topics, and then adjusts step by step so no gaps are left behind.
  • Multi-Level Assessments: Learners are tested at different depths of understanding, from simple recall to critical thinking, so the training builds strong, job-ready skills.
  • Multilingual Access: Not everyone learns best in English. That is why our platform works in both Urdu and English, so no one is left behind because of language.
  • Global Standards, Local Relevance: By integrating missing advanced topics and refocusing on overlooked but important areas, we are making sure Pakistani students are trained to meet global demands while still learning in a culturally relevant way.

This is not just about teaching. It is about building confidence, curiosity, and practical skills.

Why This Matters

The traditional classroom has played its role for decades, but it was never built to address the diverse needs of every learner especially in vocational education, where practical skills and individual guidance are critical. Too often, students are taught in a one-size-fits-all system, where some advance while many others are left behind, unable to keep pace or find relevance in what they learn. This not only limits their personal growth but also holds back Pakistan’s potential for innovation and progress.

Through digitalization and the power of personalized learning, we have the opportunity to change this. By integrating AI tutors and adaptive platforms into vocational education, we can create learning experiences that adjust to each student’s strengths, support their weaknesses, and match the pace at which they learn best. Every learner whether in a major city, a small town, or a rural village can gain access to high-quality, tailored training that equips them with the skills demanded by today’s world.

For Pakistan, this transformation is vital. Millions of young people aspire to secure meaningful jobs, build careers, and contribute to the economy, yet they lack the consistent, skill-focused education needed to get there. By digitalizing vocational learning, we can bridge this gap providing not just knowledge, but confidence, employability, and opportunity.

This is not about replacing teachers or trainers. It is about empowering them with tools that reduce repetitive work, enhance their reach, and allow them to focus on mentoring and guiding learners in ways technology cannot. Together, personalized digital education and dedicated educators can unlock the potential of every young person, ensuring that no one slips through the cracks and that Pakistan’s workforce is ready for the future.

The Future is Here

The future of learning is not about replacing teachers. It is about empowering them with the tools and support they need to reach every student effectively. No matter how skilled or dedicated a teacher may be, the limitations of time and resources make it difficult to give each learner the individualized attention they deserve. This is where technology and innovation can step in as powerful allies, extending the reach of educators and ensuring no student is left behind. Through the Ba-Ikhtiyar Jawan Project, we are proving that this vision of education is not reserved for wealthy nations or elite schools; it is possible here in Pakistan, for every student, whether they live in a bustling city, a small town, or a remote village. By making education adaptive and personalized, we do more than transfer knowledge; we give students the chance to grow in confidence, discover their potential, and build the skills they need for a brighter tomorrow. Most importantly, this approach is not limited to one field; it can be extended across all domains of vocational education, from technical trades to digital skills, ensuring that learners in every sector are equipped to succeed. When learning truly adapts to the learner, education becomes a pathway to opportunity, empowerment, and lasting change for individuals and communities alike.

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The Role of AI in Machine Translation and Bridging Language Barriers

Have you ever tried to read a manual or a guide but could not find one in your own language? There are a lot of books and educational resources in the world but a lot of them are written in languages that we cannot understand. That is where Artificial Intelligence (AI) comes in to make life easier.

What is Machine Translation?

Machine translation simply means using computers (or AI) to automatically convert text from one language to another. Think of tools like Google Translate—you type in a sentence in English, and instantly, you can see it in Urdu, Spanish, or any other language. AI does this by recognizing patterns in language and learning from huge amounts of text.

How AI Bridges Language Barriers

Translating one language into another is not an easy task. Unless you are fluent in both languages, you are unable to translate the context of a sentence from one language to another. In the past, many translation software were made to try to solve this problem, but they often gave broken translations or could not translate the context, i.e. what the sentence is about. But today, thanks to AI-powered models, translations are much more natural. This helps people from different countries and backgrounds share knowledge without needing to be fluent in another language.

For example:

  • A Japanese research paper can be read in English.
  • A mechanic in Brazil can understand a manual originally written in German.
  • Students can learn from global resources, no matter the language.

Why This Matters for Auto Electricians

Countries like Germany and Japan are famous for their educational advancements in automative technology and research. They have the most powerful and well-known companies regarding automotive vehicles like Volkswagen, BMW, Honda, Toyota, etc. One of the major problems of studying from their books is language. Almost all the major auto electrician books are written in their own languages and students who understand only English and Urdu cannot understand them. This creates a barrier to learning and growth.

This is where the Ba-Ikhtiyar Jawan Project comes in. It uses Artificial Intelligence (AI) to help students understand concepts from those non-understandable books and give translations to those that require it. It is a tool that can bridge the gap between the auto electrician students of Pakistan and the rest of the world.

The Bigger Picture

AI in machine translation is not just about convenience, it is about breaking down barriers. It allows knowledge to reach throughout the world, regardless of where someone is from or what language they speak. For auto electricians, this means better skills, better job opportunities, and ultimately, better services for their communities.

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The future of Learning with AI Tutors

Rethinking Education

Education has always been about unlocking human potential. Yet for decades, classrooms have followed the same “one-size-fits-all” model: one teacher, one lesson, one pace  for students with very different abilities and needs.

Perhaps you were the student who learned quickly but felt held back. Or maybe you needed extra time but the class had already moved on. In both cases, the rigid classroom left learners frustrated.

Now imagine a system that adjusts to you. Moving at your pace, giving explanations in your own style, and offering practice exactly when you need it. Thanks to Artificial Intelligence (AI), this is becoming reality.

The Rise of AI Tutors

AI tutors, powered by Generative AI (GenAI) and Large Language Models (LLMs), provide the kind of one-on-one support usually reserved for private tutoring. They can answer questions, simplify complex topics, and adapt lessons in real time.

What makes AI Tutors powerful?

  • 24/7 availability: Learning anytime, anywhere.
  • Endless patience: No limit on questions or repetition.
  • Adaptive pathways: Filling knowledge gaps before moving forward.
  • Personal relevance: Content shaped to interests, goals, and even preferred language.

In short, an AI tutor is like a personal coach in your pocket that is always ready to guide and encourage.

Teachers and AI: Partners, Not Rivals

AI will not replace teachers. Instead, it handles repetitive tasks such as grading, generating practice exercises, re-explaining concepts while teachers focus on mentorship, motivation, and critical thinking.

  • A teacher with 40 students can rarely give each one daily attention.
  • An AI tutor fills that gap in the background.
  • Together, they ensure no learner is left behind.

This partnership makes classrooms more engaging and effective.

Why This Matters for Pakistan

With 65% of its population under 30, Pakistan stands at a turning point. This youth bulge can be a huge strength but only if young people gain skills that match global standards.

The challenges are clear:

  • High dropout rates before secondary school.
  • Vocational training that lags behind international benchmarks.
  • Too few skilled teachers and resources.

AI tutors can bridge these gaps. Whether in Karachi, Khyber Pakhtunkhwa, or a remote village in Balochistan, learners can access the same quality of education in both Urdu and English.

The Ba-Ikhtiyar Jawan Project’s Role

The Ba-Ikhtiyar Jawan Project is putting this vision into action by applying AI tutors in vocational education.

Here’s how:

  • Adaptive curriculum: Updated to match global standards.
  • Skill mapping: Personalized practice to close gaps.
  • Multilingual learning: Urdu and English support.
  • Industry relevance: Courses aligned with new technologies like hybrid and electric vehicles.
  • Confidence building: Encouraging persistence and curiosity.

This isn’t just academic reform. It equips youth with employable skills, boosts confidence, and strengthens Pakistan’s economy.

A Glimpse of the Future

Picture a student in Multan practicing graphic design late at night, guided by an AI tutor that patiently explains concepts, suggests improvements, and offers feedback until the learner feels confident. At the same time, imagine a trainee in Gilgit working on electric vehicle repair, receiving clear, step-by-step instructions in Urdu, tailored to their pace and level of understanding. In both cases, the AI tutor does not replace the teacher; instead, it acts as an ever-available companion that reinforces classroom learning, fills gaps, and provides encouragement when challenges arise. By ensuring no learner feels stuck or left behind, this partnership between human care and technological support creates a learning environment that is flexible, inclusive, and empowering pointing toward a future of education where every student, regardless of location or background, can access guidance and opportunities to reach their full potential.

A Pathway to Opportunity

The impact extends beyond classrooms:

  • Students: Personalized learning builds confidence and ambition.
  • Teachers: More time for meaningful guidance.
  • Nation: A skilled, future-ready workforce to compete globally.

When education adapts to each learner, it becomes a pathway to empowerment and opportunity.

Conclusion: The Future is Now

The future of learning with AI tutors is already here. For Pakistan, this is not just about keeping up, it’s about leading.

Through the Ba-Ikhtiyar Jawan Project, personalized education is becoming a reality for every learner, whether in a city, a small town, or a remote village. With teachers and AI working together, education can truly unlock the potential of Pakistan’s youth.

The tools are ready. The vision is clear. The question is: are we ready to embrace it?

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What Generative AI is and why does it matter

As students, we often find ourselves rushing to write emails to professors, preparing reports, or completing assignments. Let’s be honest—it can feel boring and time-consuming. But now, tools like ChatGPT, Gemini, and DeepSeek are transforming this experience.

Just a few years ago, artificial intelligence felt like something out of science fiction—exciting to hear about, but distant from our daily lives. Today, that is no longer the case. ChatGPT, for example, has become almost like a companion—someone we can talk to, share ideas with, and seek help from whenever we need. It listens, suggests creative solutions, and even supports our studies in new ways. Learning suddenly feels easier, more engaging, and far more interactive.

Among the many terms that frequently appear in discussions about artificial intelligence, one of the most popular is Generative AI, often shortened to GenAI. While it may sound new, the truth is that most of us are already using it without realizing it. ChatGPT, Gemini, DeepSeek, and similar platforms are all examples of GenAI tools. Unlike traditional AI, which was mainly limited to simple tasks such as predicting outcomes or classifying data, Generative AI goes further. It can come up with fresh ideas, generate images, flowcharts, and diagrams, understand complex problems in depth, and provide responses even to situations it has never directly encountered before.

This ability to create, rather than just recognize, is what makes Generative AI so powerful. It is already being applied in creative fields such as art, music, and storytelling, but perhaps its most meaningful impact is being felt in education. For students, Generative AI acts like a personal tutor that is available at all times. It allows them to ask questions in text, audio, or images, break down complicated topics into simpler explanations, visualize ideas through diagrams, and receive personalized learning recommendations. This is not simply about making studies easier—it is about making education more accessible, engaging, and effective for learners of all kinds.

The Ba-Iktiyar Jawan Project is working to bring the benefits of Generative AI to learners across Pakistan. Its vision is to ensure that this powerful technology is not limited to those with expensive devices or costly training opportunities. As part of this initiative, a platform is being developed that functions much like ChatGPT, but is specifically designed for vocational learners—beginning with auto-electricians. This system will allow trainees to ask questions in Urdu and receive clear, step-by-step answers. It will offer personalized guidance tailored to each learner’s progress, while also supporting voice and image inputs for practical tasks such as interpreting wiring diagrams. Most importantly, it is being built to work smoothly even on low-end Android devices with limited internet connectivity.

In essence, this means that becoming a skilled auto-electrician will no longer require an expensive smartphone or access to costly courses. Instead, Generative AI will place knowledge, guidance, and personalized learning directly into the pockets of young people across the country, empowering them to build better futures with the tools they already have.

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Real Time Face Detection & Privacy Preservation

Real Time Face Detection & Privacy Preservation

If you are walking through a crowded plaza, and your face, along with others, always remains anonymous, yet the video footage remains insightful, wouldn’t that be amazing? That’s the power of Real Time Face Detection & Privacy Preservation. Such a system understands when a face appears in front of the camera and automatically applies a blur to that region. Rather than pulling into manual edits frame by frame or risking privacy during surveillance, the approach intelligently balances the need to keep sensitive personal data safe while still preserving the situational awareness that video footage offers. Whether it is surveillance, public displays, or research footage, the system serves as a powerful tool for maintaining anonymity in an increasingly monitored world.

The project uses high-performance computer vision: OpenCV handles video capture and processing, while Mediapipe provides lightweight, real-time face detection. Each video frame is scanned for faces using Mediapipe’s optimized detection models. Once a face is detected, the region is isolated and blurred using OpenCV, usually through a Gaussian or pixelation filter. The system then fuses the blurred region back into the frame, producing a final output that’s safe for sharing, storage, or analysis. Because it works efficiently, the entire process runs in real time on standard hardware, achieving smooth blurring at video frame rates. Due to this, hands-free, immediate, and consistent privacy protection becomes an automated reflex rather than a manual chore.

This work is a commitment to ethical, humanitarian design. In many contexts (public transit, classrooms, protests, sensitive research environments) the automatic blurring of faces protects identities without compromising utility. It empowers content creators, researchers, and platforms to collect video data responsibly, without exposing individuals to misuse or privacy violations. The technology can integrate with live-streaming, archives, or CCTV systems with minimal friction. It’s not just about blurring faces, it’s about giving people assurance that their presence remains respectful of their privacy, while still enabling analysis, monitoring, or commentary. It’s a step toward a more thoughtful future in digital vision applications.

Faculty

Students

  • Abdullah Usama
  • Usama Athar
22

Sports Footage Analysis

Sports Footage Analysis

All major sport teams have always wanted a sports coach’s keen eye and near-perfect memory. This can be embedded in a software system that watches every play, tracks every movement, and extracts meaningful patterns, all in real time. This is what this project sets out to accomplish. Designed for football, the system turns raw game footage into actionable insights for coaches, analysts, and players. It detects and tracks players, the ball, and relevant field markers like lines and goalposts, then computes metrics like player distance covered, speed, possession, heatmaps, and strategic positioning. Far beyond simply recording who’s on the field, this tool transforms video into the kind of deep, data-driven intelligence that can influence game plans, training regimens, and even talent scouting.

The technical architecture underpinning this capability is both modular and powerful. First, the system preprocesses raw footage, aligning frames, calibrating views, and preparing the video for detection. It then employs deep learning object detection to identify players, the ball, and lines. These detections are passed into a tracking module that links objects across frames, creating trajectories over time. From these paths, it computes key metrics, like a player’s total distance run or a ball’s possession time, while overlaying visualizations of movement trails and heatmaps. The results are exportable as CSVs, allowing analysts to combine quantitative data with video playback, making it seamless to correlate critical moments, like a sudden sprint, with tactical decisions. Whether you are measuring how often a striker drops back into midfield or quantifying how possession shifts during a match, the system provides high-resolution visibility into game dynamics.

What makes this project particularly valuable is the bridge it builds between cutting-edge AI and real-world sports analytics. It is not a black-box or prototype, it is a practical, deployable framework. Analysts can use it to automatically generate reports on player performance; coaches can use it to refine strategies; broadcasters can layer live insights during replays. The accuracy of object detection combined with smooth tracking means that patterns emerge clearly, whether highlighting who tended to control the wings, which players made the most high-speed runs, or where formations broke down. By sharing clean, well-structured data alongside visual outputs, the system invites further innovation: one might integrate it with GPS tracking, link it to physiological sensors, or feed its findings into performance dashboards. This project not only brings AI to bear on the complex, fast-moving world of sports, it makes that power usable, transparent, and ready to shape the next generation of athletic performance.

Faculty

Students

  • Abdullah Usama
  • Usama Athar
23

Change Detection in Remote Sensing Imagery

Change Detection in Remote Sensing Imagery

This project targets to detect, localize, and interpret changes in the Earth’s surface through an intelligent transformer-based system. It tackles a vital challenge: automatically identifying regions of change between two images taken at different times (dual-phase imagery), such as areas affected by urban expansion, deforestation, or natural disasters. Traditionally, such detection relies on convolutional methods that struggle with long-range context and subtle temporal differences. By introducing transformer-based architectures, the project brings a fresh perspective, enabling models to compare images not just pixel by pixel, but by capturing spatial and temporal “conversations” between regions across time. This method holds the promise of faster, more accurate mapping of change, key for environmental monitoring, urban planning, and disaster response.

The pipeline for this project is built in PyTorch, implementing transformer models for pixel-level change detection. We adapt and apply models like the Bitemporal Image Transformer (BIT), which first compresses dual-temporal images into meaningful tokens, then uses transformer encoders to model spatial-temporal context and decoders to project these insights back into refined pixel predictions. This approach significantly reduces the compute overhead compared to convolution-heavy systems, while maintaining or surpassing accuracy. The code supports widely-used remote sensing change detection datasets (LEVIR-CD, WHU-CD, DSIFN-CD), providing scripts for training, evaluating, and running demos. Users can recreate the entire workflow, from preprocessing paired images to generating visual change maps. Notably, BIT-based models often outperform standard convolutional baselines using just a third of the parameters and compute cost.

The modular structure of this pipeline means developers can easily experiment with new transformer variants, attention mechanisms, or dataset formats. The integration of dual temporal data opens doors to domain-specific customization. This can be adapted to detect building damage after storms, monitor vegetation loss in wildfire zones, or track glacier retreat. The use of publicly available datasets and open MIT licensing ensures the project can be extended and adapted freely. For practitioners, the ability to visualize and act on accurate change maps, without needing massive compute or complex pipelines, makes this system incredibly practical. This work shows how current AI research can move beyond lab benchmarks into environments where timely and reliable change detection can make a real difference.

Faculty

Students

  • Navaal Iqbal
  • Ayesha Siddiqa
  • Amna Ahmed
  • Usama Athar
37

Self Evolving Multi Agent Society

Self Evolving Multi Agent Society

Self-Evolving Multi-Agent Society explores the creation of a decentralized ecosystem where AI agents can discover, trust, and collaborate without depending on fragile centralized hubs. Existing protocols like Google’s A2A and similar systems often struggle with bottlenecks: they lack efficient discovery methods, rely on centralized registries, have weak incentive structures, and leave major gaps in scalability and security. This project introduces DIA, Decentralized Internet of Agents, a new architecture designed to overcome these shortcomings. Instead of forcing all agents through a single point of control, DIA organizes them into hierarchical, self-balancing communities, enabling them to find one another, negotiate, and transact directly while conserving bandwidth.

DIA is a novel communication protocol paired with a cloud-of-clouds architecture. Agents join dynamically, forming specialized clusters while also maintaining diversity for resilience. A reputation system based on micropayments ensures that trustworthy agents thrive, while low-quality or spammy behavior becomes costly to maintain. Through this combination of self-organization, efficient discovery, and economic incentives, the network can adapt as it grows, evolving into a robust Internet of Agents that mirrors the scale and dynamism of the real web. By embedding incentives and trust mechanisms directly into the protocol, the system addresses long-standing challenges in agent collaboration, making true peer-to-peer intelligence sharing possible.

The overarching objective of this work is to redefine how agents interact at scale, moving beyond incremental fixes toward an ecosystem where autonomy, security, and scalability are built in from the start. A successful implementation would not only provide a foundation for resilient multi-agent systems but also open doors to entirely new applications—from collaborative problem-solving and autonomous research to decentralized marketplaces of intelligent services. This project is both a practical engineering effort and a conceptual leap, aiming to establish the blueprint for a self-evolving society of agents that can operate as seamlessly and dynamically as the Internet itself.

Faculty

Students

  • Syed Muhammad Taha
  • Zain Ali
  • Maheen Ahmed
21

Gesture-Based Volume Control from Video Feed

Gesture-Based Volume Control from Video Feed

Imagine effortlessly adjusting your device’s volume with just a pinch, with no mouse, no buttons, no touching necessary. Through this project, everyday hand gestures become intuitive controls, enabling people to increase or decrease volume simply by moving their thumb and index finger in front of a camera. Think of it as the future of interactive interfaces: your hand becomes the dial, the pinch determines volume, and a live video feed translates your motions into real-time auditory response. It takes gesture recognition out of the science fiction realm and pins it firmly to modern human-computer interaction, making control more natural, seamless, and even fun.

The system runs on a clever combination of lightweight computer vision and core system integration. A video feed, or even a simple webcam, captures hand movements, while an on-device hand landmark detector tracks your fingertips and joints at high speed. As your thumb and index finger move closer or farther apart, the system continuously measures that distance and maps it precisely onto a volume scale. Behind the scenes, the distance between those two fingertips is normalized to correspond with minimum and maximum volume levels. The software translates that float value into a system-level volume change, so each gesture changes your audio output instantly and with smooth transitions. It is a real-time feedback loop: see the gesture, interpret it, and hear the change.

This project is more than technology. It brings together elegant interaction design and meaningful accessibility. For environments where touch isn’t convenient, like sweating in a kitchen, driving hands-free, or simply being hands-full, this gesture interface steps in with effortless control. It also opens the door to inclusive design: users with temporary mobility restrictions or certain disabilities may find this a life-changing interface. From a technical lens, the system showcases how vision models can meaningfully enhance daily tools. It’s easy to customize: change mapping curves, adjust gesture ranges, or overlay visual feedback. With a modular design, it’s not hard to imagine extending this gesture-control principle to other system functions, like brightness, media playback, or even smart home devices. This is a compelling showcase of how natural gestures, real-time perception, and simple UI integration can reimagine how we interact with digital worlds.

Faculty

Students

  • Abdullah Usama
  • Usama Athar