Muhammad Nouman Hanif

Instructor - Computer Science (DS, CYS, AI) at FAST-NUCES

Muhammad Nouman Hanif

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Results-driven Data Scientist with expertise in machine learning, data analytics, and cloud computing. Skilled in automation, AI-driven solutions, and big data technologies to drive business intelligence.

Muhammad Nouman Hanif

Python

C++

FastAPI

Next.js

React

Docker

Kubernetes

AWS

TensorFlow

PyTorch

OpenCV

PostgreSQL

MongoDB

TypeScript

Tailwind CSS

Git

Linux

RAG

LLMs

LangChain

Python

C++

FastAPI

Next.js

React

Docker

Kubernetes

AWS

TensorFlow

PyTorch

OpenCV

PostgreSQL

MongoDB

TypeScript

Tailwind CSS

Git

Linux

RAG

LLMs

LangChain

Python

C++

FastAPI

Next.js

React

Docker

Kubernetes

AWS

TensorFlow

PyTorch

OpenCV

PostgreSQL

MongoDB

TypeScript

Tailwind CSS

Git

Linux

RAG

LLMs

LangChain

Featured Work

Highlights of my research in Generative AI, Computer Vision, and Cloud Engineering.

Parameter-Efficient Fine-Tuning (PEFT) & Agentic Systems

Conducted a 42-run ablation study on DeBERTa-v3 using QLoRA, retaining 97% accuracy while reducing VRAM by 90%. Built a Multi-Agent Research Assistant using LangGraph to orchestrate agents for search, ranking, and summarization.

Multimodal Pipeline: Story Generation & E-Commerce Mockups

Engineered an end-to-end pipeline combining BLIP (Image Captioning) and FLAN-T5 (Story Generation). Built an automated product mockup system using Stable Diffusion (img2img), optimizing denoising strength for photorealistic textures.

Stable Diffusion

Brain Tumor Classification with Explainable AI (XAI)

Engineered a multi-class classification model (Glioma, Meningioma, Pituitary) achieving 99.65% accuracy. Implemented Grad-CAM and Attention Rollout to visualize decision heatmaps.

Flood Severity Prediction with Ensemble Gradient Boosting

Engineered a robust flood forecasting pipeline using CatBoost and XGBoost, trained on a massive dataset of 1.1 million+ records. Achieved a high predictive accuracy of 0.85 R².

Flood Prediction Using Machine Learning

Built an ML-powered flood forecasting system using meteorological and hydrological data. Evaluated regression models to enhance prediction accuracy and response time.

Machine Learning

Regression Models

IoT-Enabled Smart Solar Inverter

Engineered an IoT-powered solar inverter with real-time Maximum Power Point Tracking (MPPT). Integrated a solar tracking system to enhance energy efficiency and power generation.

Parameter-Efficient Fine-Tuning (PEFT) & Agentic Systems

Conducted a 42-run ablation study on DeBERTa-v3 using QLoRA, retaining 97% accuracy while reducing VRAM by 90%. Built a Multi-Agent Research Assistant using LangGraph to orchestrate agents for search, ranking, and summarization.

Multimodal Pipeline: Story Generation & E-Commerce Mockups

Engineered an end-to-end pipeline combining BLIP (Image Captioning) and FLAN-T5 (Story Generation). Built an automated product mockup system using Stable Diffusion (img2img), optimizing denoising strength for photorealistic textures.

Stable Diffusion

Brain Tumor Classification with Explainable AI (XAI)

Engineered a multi-class classification model (Glioma, Meningioma, Pituitary) achieving 99.65% accuracy. Implemented Grad-CAM and Attention Rollout to visualize decision heatmaps.

Flood Severity Prediction with Ensemble Gradient Boosting

Engineered a robust flood forecasting pipeline using CatBoost and XGBoost, trained on a massive dataset of 1.1 million+ records. Achieved a high predictive accuracy of 0.85 R².

Flood Prediction Using Machine Learning

Built an ML-powered flood forecasting system using meteorological and hydrological data. Evaluated regression models to enhance prediction accuracy and response time.

Machine Learning

Regression Models

IoT-Enabled Smart Solar Inverter

Engineered an IoT-powered solar inverter with real-time Maximum Power Point Tracking (MPPT). Integrated a solar tracking system to enhance energy efficiency and power generation.

Parameter-Efficient Fine-Tuning (PEFT) & Agentic Systems

Conducted a 42-run ablation study on DeBERTa-v3 using QLoRA, retaining 97% accuracy while reducing VRAM by 90%. Built a Multi-Agent Research Assistant using LangGraph to orchestrate agents for search, ranking, and summarization.

Multimodal Pipeline: Story Generation & E-Commerce Mockups

Engineered an end-to-end pipeline combining BLIP (Image Captioning) and FLAN-T5 (Story Generation). Built an automated product mockup system using Stable Diffusion (img2img), optimizing denoising strength for photorealistic textures.

Stable Diffusion

Brain Tumor Classification with Explainable AI (XAI)

Engineered a multi-class classification model (Glioma, Meningioma, Pituitary) achieving 99.65% accuracy. Implemented Grad-CAM and Attention Rollout to visualize decision heatmaps.

Flood Severity Prediction with Ensemble Gradient Boosting

Engineered a robust flood forecasting pipeline using CatBoost and XGBoost, trained on a massive dataset of 1.1 million+ records. Achieved a high predictive accuracy of 0.85 R².

Flood Prediction Using Machine Learning

Built an ML-powered flood forecasting system using meteorological and hydrological data. Evaluated regression models to enhance prediction accuracy and response time.

Machine Learning

Regression Models

IoT-Enabled Smart Solar Inverter

Engineered an IoT-powered solar inverter with real-time Maximum Power Point Tracking (MPPT). Integrated a solar tracking system to enhance energy efficiency and power generation.