About

Welcome! I am a Machine Learning Engineer at Rezunate AI, where I design and deploy Computer Vision systems that solve real-world problems at scale. During my BSCS at FAST National University of Computer and Emerging Sciences, I worked as a Research Assistant across Computer Vision,Time Series Analysis, Natural Language Processing, and Recommendation System Projects, gaining a breadth of exposure that taught me how to move fluidly between research rigor and production engineering.

Research

My research interests span Computer Vision, Generative Models, Reinforcement Learning, and Robotics. I am especially drawn to the intersection of these fields, where perception, generation, learning, and decision-making converge to solve genuinely hard problems in AI. I believe the most meaningful breakthroughs happen at these seams, and that is where I aim to focus my work.

Most of my projects, research code, and open-source contributions live on GitHub. Feel free to explore, or reach out if something here sparks a conversation.

Education

Information Technology University (ITU), Lahore

Master of Science in Data Science

August 2026 - September 2028 | Pakistan

Coursework:

• Advanced Mathematics
• Machine Learning
• Tools and Techniques for Data Science

Lahore University of Management Sciences (LUMS), Lahore

Master of Science in Artificial Intelligence

July 2024 - September 2024 | Pakistan

Coursework: N/A

• Paused after one term to gain hands-on industry experience in applied ML before resuming graduate study.

National University of Computer and Emerging Sciences (FAST NUCES)

Bachelor of Science in Computer Science

September 2020 - September 2024 | Pakistan

Specialization:

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Digital Image Processing
• Data Science
• Computer Vision
• Natural Language Processing

Experience

Rezunate AI

Machine Learning Engineer

November 2025 – Continue | Lahore, Pakistan (On-site)

• Developing computer vision systems using state-of-the-art architectures for image and video understanding and generation.
• Experience spanning segmentation, object detection, multi-object tracking, point tracking, video matting, depth estimation, and diffusion-based models.
• Building and optimizing end-to-end training and inference pipelines , including multi-GPU and distributed workloads for computationally intensive vision models.

OrbytLabs

Co-Founder | Machine Learning Engineer

June 2024 – November 2025 | Remote

• Developed a vehicle price prediction system, applying data validation, feature engineering, and model optimization. Iteratively improved the model to achieve 98% prediction accuracy, enabling accurate and reliable price estimations.
• Built object detection and tracking systems using YOLOv10/YOLOv11, integrating tracking algorithms for real-time visual understanding in smart security solutions. Applied post-training quantization (PTQ) and Quantization-Aware Training (QAT) to optimize model performance and ensure efficient, real-time inference on edge devices.
• Implemented intrusion detection systems by combining motion-based region analysis and AI-driven object localization to automatically detect and flag unauthorized entries or restricted zone violations in real time.
• Implemented OCR pipelines using Tesseract and EasyOCR to extract and structure information from scanned documents and ID cards, enabling automated data extraction and document digitization.
• Created and deployed LLM-based chatbots and document understanding systems using OpenAI, Claude, and Gemini APIs with RAG pipelines to automate customer support and business operations. Fine-tuned large language models (LLMs) for domain-specific use cases, enhancing contextual understanding, accuracy, and efficiency.
• Optimized deep learning pipelines using transfer learning, quantization, pruning, and model optimization techniques to improve accuracy and efficiency during experimental evaluations.

Deutics Global

Associate Machine Learning Engineer

Jan 2025 – July 2025 | Lahore, Pakistan (On-site)

• Developed and optimized a real-time video analytics system by converting RTSP streams to WebRTC for efficient live video processing and reduced latency.
• Implemented advanced tracking algorithms for real-time object tracking across frames, enabling movement monitoring and direction estimation.
• Built and integrated OCR pipelines using Tesseract, EasyOCR, and custom deep learning models to extract license plate numbers from images and video streams.
• Designed and optimized algorithms for wait time estimation, queue detection, speed calculation, and traffic light violation detection, along with advanced modules for eye state detection (drowsiness monitoring), seat belt detection, and driver attention monitoring to enhance traffic efficiency and enforce road safety regulations.
• Implemented line and zone intrusion detection systems to monitor restricted areas, strengthen surveillance, and enforce access control.

FAST NUCES

Research Assistant

Sep 2023 – Dec 2024 | Pakistan (On-site)

• Project: Personalized Recommendation System Development
• Developed a multi-class skin cancer classification system using the HAM10000 dataset to classify various types of skin lesions.
• Custom CNN achieved 92% accuracy, precision 92%, recall 92%, F1 score 92%.
• NasNet achieved 93% accuracy with precision 94%, recall 93%, F1 score 93%.
• ShuffleNet enhanced computational efficiency using grouped convolutions and channel shuffling; accuracy 87%, precision 87%, recall 87%, F1 score 87%.
• Optimized data pipelines and applied augmentation strategies to improve robustness and reduce over-fitting.

Anonymous Tree

Machine Learning Engineer (Intern)

Jun 2023 – Aug 2023 | Remote, Pakistan

• Project: Personalized Recommendation System Development
• Collected and processed user interaction data (e.g., purchases, ratings) along with item metadata (e.g., product categories, descriptions, attributes) to build a robust dataset. The data was cleaned, normalized, and structured for efficient use in model training. Additionally, techniques such as data imputation and feature scaling were implemented to handle missing values and enhance the accuracy and reliability of model predictions.
• Developed a content-based recommendation system utilizing item metadata (e.g., text-based features) to recommend similar products to users. Implemented collaborative filtering techniques such as user-based and item-based approaches combined both into a hybrid recommendation system for improved accuracy.
• Tuned hyper-parameters using Grid Search and Randomized Search for optimal performance. Evaluated models using Precision, Recall, F1 Score, and Mean Squared Error (MSE). Applied cross-validation to ensure generalization and prevent over-fitting.
• Integrated Matrix Factorization methods (e.g., SVD, KNN) for collaborative filtering. Utilized TF-IDF and Cosine Similarity for content-based filtering to identify similar items.
• Monitored real-time recommendation performance and ensured alignment with business goals. Incorporated user feedback into model retraining and improvement cycles. Analyzed system performance over time and tested new features for better accuracy.

Projects

I believe the best way to learn is by doing. I have completed many projects, and here is a list of the most notable ones in reverse chronological order. For more information, please visit GitHub.

Highlighted Projects

1. Skin Cancer Classification using NasNet and ShuffleNet

   Dawood Sarfraz

   Pytorch

   [ Paper | Supplementary | Project | Bibtex | Code | Website | Video ]

2. Product Recommender System

   [ Project Report | Code ]

3. Duplicate Question Pairs

   Pytorch

   [ Code ]

4. Text Generation using LSTMs

   Python, NumPy, Pandas, TensorFlow, Keras

   [ Code ]

5. Collaborative AI System for Task Research and Analysis

   Python, Ollama, Llama 3.2, CrewAI, and Serper

   [ Code ]

6. Stock Market Prediction using LSTM

   Python, NumPy, Pandas, Keras, TensorFlow

   [ Code ]

7. AI Research Assistant for Real-Time Information Discovery

   Python, Ollama, Llama 3.2, CrewAI, and Serper

   [ Code ]

8. Twittet Sentiment Analysis using Machine Learning

   Python (version 3.7+), Pandas, NumPy, Scikit-learn, SVM, KNN, DT, XGBoost, Random Forest, Logistic Regression

   [ Code ]

9. Text Classification using Classical NLP and RoBERTa

   Dawood Sarfraz

   Python, Tensorflow, RoBERTa, VADER lexicon, NLTK

   [ Code ]

10. Intelligent Academic Assistant for Text and Image Tasks

    Python, Llama 3.2 3B, LangChain, Streamlit, Ollama

    [ Code ]

11. Smart Literature Review and Research Analysis Assistant

    Python, RAG, Mistral 7B, Ollama

    [ Code ]

12. Russian Language Sentiment Analysis

    Python (version 3.7+), Pandas, NumPy, NLTK ('punkt', stopwords, SnowballStemmer), TfidfVectorizer, scikit-learn (MultinomialNB)

    [ Code ]

13. Text, Image, Audio and Video Steganography

    Python 3, OpenCV, NumPy, Matplotlib, ImageIO, SciPy, tqdm, pypng, wave, Linux

    [ Code ]

14. Cyber Attacks Classification using Machine Learning

    Dawood Sarfraz

    Python (version 3.7+), Pandas, NumPy, Scikit-learn, SVM, KNN, DT, XGBoost, Random Forest, Logistic Regression

    [ Project Report | Code ]

15. Pakistan Food Price Analysis

    Python 3.7+, NumPy, Pandas, Matplotlib, Plotly, Seaborn, scikit-learn SVM, MLPRegressor, RandomForestRegressor, AdaBoostRegressor, DecisionTreeRegressor

    [ Code ]

16. Mart Sales Prediction using XGBoost

    Python (version 3.7+), Pandas, NumPy, Scikit-learn, XGBoost

    [ Code ]

17. Gold Price Prediction using Random Forest Regressor

    Python (version 3.7+), Pandas, NumPy, Scikit-learn

    [ Code ]

Publications

List of papers is in reverse chronological order. For more information, please visit Google Scholar.

Papers

1. Skin Cancer Classification using Deep Learning

   Dawood Sarfraz

   arXiv, 2025

   [ Paper | Supplementary | Project | Bibtex | Code | Website | Video ]

Skills

Miscellaneous

Blogs

Read all Blogs →

1. 3D Gaussian Splatting
2. Transformers from Scratch
3. Diffusion Models Explained
4. Understanding RLHF
5. Neural Radiance Fields (NeRF)
6. Coming Soon

Notes

Read all Notes →

Machine Learning

1. Linear Regression
2. Classification
3. Generative Learning Algorithms
4. Kernel Methods
5. Learning Theory
6. Clustering
7. Principal Component Analysis
8. Independent Component Analysis
9. Expectation Maximization Algorithm
10. Gaussian Mixture Models
11. Factor Analysis
12. Variational Autoencoders
13. Decision Trees
14. Reinforcement Learning

Deep Learning

1. Deep Feedforward Networks
2. Regularization for Deep Learning
3. Optimization for Training Deep Models
4. Convolutional Networks
5. Sequence Modeling: Recurrent and Recursive Nets
6. Large-Scale Deep Learning
7. Practical Methodology
8. Autoencoders
9. Representation Learning
10. Deep Generative Models

Reinforcement Learning

1. Introduction to Reinforcement Learning
2. Markov Decision Processes
3. Planning by Dynamic Programming
4. Model-Free Prediction
5. Model-Free Control

Deep Reinforcement Learning

1. Introduction
2. Behavioral Cloning
3. Behavioral Cloning Part 2
4. RL Basics
5. Policy Gradients

Computer Vision

1. LeNet
2. AlexNet
3. VGGNet
4. GoogLeNet (Inception)
5. ResNet

3D Computer Vision

1. Structure from Motion (SfM)
2. Multi-View Stereo (MVS)
3. SLAM (Simultaneous Localization and Mapping)
4. Monocular Depth Estimation (MiDaS, DPT)
5. Stereo Depth Estimation

Generative Models

1. Boltzmann Machines
2. Restricted Boltzmann Machines
3. Deep Belief Networks
4. Deep Boltzmann Machines
5. Boltzmann Machines for Real-Valued Data

Robotics

1. Introduction
2. Biomechanics of Walking and Running
3. Robot Mechanisms: Kinematics and Dynamics
4. Introduction to Diffusion Models
5. The Human Hand and Dexterous Object Manipulation, Robot Hands

Natural Language Processing

1. Text Preprocessing and Tokenization
2. Bag of Words and TF-IDF
3. Word Embeddings (Word2Vec, GloVe)
4. Recurrent Neural Networks for NLP
5. LSTM and GRU for NLP

TinyML and Efficient Deep Learning Computing

1. Introduction
2. Basics of Deep Learning
3. Pruning and Sparsity (Part I)
4. Pruning and Sparsity (Part II)
5. Quantization (Part I)

Linear Algebra

1. Linear Equation System
2. Basic Matrix Algebra
3. Determinant
4. LU Factorization
5. Vector Addition, Subtraction and Scalar Multiplication
6. Linear Combination
7. Linear Independence
8. Vector Space and Subspace
9. Basis and Dimension
10. Null Space vs Col Space, Row Space and Rank
11. Linear Transformation
12. Eigenvalues and Eigenvectors
13. Eigenvalue Decomposition
14. Diagonalization
15. Principal Component Analysis
16. Applications to Dynamic System
17. Inner Product and Orthogonality
18. Gram-Schmidt Process and QR Decomposition
19. Symmetric Matrices, Quadratic Form and Cholesky Decomposition
20. Singular Value Decomposition
21. Multivariate Normal Distribution

Calculus

1. Functions and Limits
2. Continuity
3. Derivatives and Differentiation Rules
4. Chain Rule
5. Implicit Differentiation

Probability and Statistics

1. Sample Spaces and Events
2. Axioms of Probability
3. Conditional Probability
4. Bayes' Theorem
5. Independence of Events

Numerical Optimization

1. Introduction to Optimization
2. Convexity and Convex Sets
3. Unconstrained Optimization Basics
4. Gradient Descent
5. Line Search Methods