Qaiser Profile Picture

Experienced Deep Learning and Computer Vision Engineer with 3+ years of industrial experience. Skilled in Deep Learning, Computer Vision, NLP, and Data Science. Strong R&D professional currently working as a Sr. Machine Learning Engineer at SDSol Technologies. I am a passionate developer who thrives to design and develop efficient A.I solutons to industrial problems.

I got my bachelors from the department of CS & IT, University of Sargodha with a gold medal. My BS was financially supported by University of Sargodha's merit scholarship & Fauji Foundation scholarhip. My Masters is in Deep Learning and Computer Vision from one of the oldest and prestigious engineering universities in Pakistan — University of Engineering and Technology, Lahore.

I have worked in diverse fields of Artificial Intelligence including deep learnig, computer vision, natural language processing and data science. My main area of expertise is in industrial problem solving using state of the art AI tools and technologies. I like to design and develop AI solutions for various business problems involving textual, visual and time series data.

I am learning and practicing MLOps and Cloud technologies for Machine Learning development. I also love to do researach and read about the latest technologies in the field. I regularly follow top notch researchers and labs in the field so I can use thier algorithms in my problems. I am a medium pacer and love to bowl. I am also a casual photographer and a universal foodie.

Interests

Software Engineering

Machine Learning

Computer Vision

Natural Language Processing

Computational Neuroscience

Medical Image Analysis

Data Science

Image Processing

MS in Computer Science

University of Engineering and Technology, Lahore

Sep 2018 - Oct 2020

Thesis: Detection and Prediction of Acral Lentiginous Melanoma in Dermoscopic Images using Deep Learning

Summary: Acral melanoma is life threatning cancer. Due to acral melanoma infrequent occurrences, limited data is available so its early diagnosis is hard. To overcome this problem, we applied data centric techniques to develop a large(comparatively) dataset to train a deep learning models. Our proposed convolutional neural network achieved an accuracy of 91% on test set.

Relevant Coursework
  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Research Methods
  • Software Quality Assurance

UET Lahore
BS in Information Technology

University of Sargodha, Sargodha

Oct 2014 - May 2018

FYP: IoT based Low Cost Intelligent Surveillance System for Smart Home Security

Summary: I worked on IOT based smart solution for smart home security. We utilized a RaspberryPi and a Pi Camera and PIR motion sensors to develop a prototype of security system. Final system was able to capture suspicious movements and notify the home owner via push and email notifications.

Relevant Coursework
  • Network Security
  • Cloud Computing
  • Artificial Intelligence
  • Human Computer Interaction
  • Information Technology Infrastructure

UET Lahore

Online Certifications

Maths for Machine Learning

Deep Learning

Python 3 Programming Specialization

Intel OpenVino Toolkit

AI for Medical Diagnosis

Computer Vision Basics

SDSol Technologies

November 2022 - Present
Machine Learning Engineer
  • Designed and developed an end-to-end recommendation system for a food related company using unsupervised association rule mining and deployed using FastAPI on cloud.
  • Developed a Chatbot for question answering for multiple websites and PDF documents using LangChain and Llama-index (Used GPT-4, BERT, LongFormer for generating answers)
  • Developed an end-to-end query classification pipeline. Collected data from various resources, used ChatGPT API for generating synthetic data and used Logistic Regression, SVM and BERT for query classification, deployed the model using FastAPI.
  • Developed a video subtitle generation pipeline using OpenAI's Whisper and deployed as a custom software solution.
  • Developed a tennis analytics system using a fine-tuned Yolo-v8 model.
  • Developed an audio to video lip movement synchronization script leveraging a state of the art Wav2Lip Model.
  • Developed an insect classification model and deployed on Azure using Container Registry and App service.
  • Developed an API for sentence's semantic similarity calculation using Sentence Transformers.
  • Developed multiple APIs in FastAPI for object recognition and text classification.
  • Designed and developed multiple UIs for Machine Learning model demos using Streamlit.
  • Worked with backend developers to deploy and integrate ML models with existing systems in production.

Wortel AI

November 2021 - January 2022
Software Engineer (Deep Learning and Computer Vision)
  • Worked on Agricultre related projects such as weed detection using YOLOv5 algorithm
  • Developed a medical speech recognition system by fine‑tuning an Nvidia QuartzNet model via NeMo library
  • Worked on AWS S3 and MLFlow platform for deployment and maintenance of deep learning models.
  • Worked on researching various methods for converting deep learning models to work with different ML libraries.

University of Engineering & Technology, Lahore

March 2021 - September 2022
AI Instructor
  • I was previously assoiated with UET Lahore.
  • Taught undergraduate AI courses and conducted practical labs.
  • Delivered practical lectures on Machine Learning and Deep Learning.
  • Assisted the senior faculty in designing course contents and writing proposals for research grants.
  • Helped and guided final-year students in their ML based FYPs.
  • Worked on researching the latest AI technologies to solve real-world problems.
  • Achievements
  • A research proposal entitled "Tea disease detection using Machine Learning and Remote Sensing" that I wrote won a grant of PKR 3.5 Million from Higher Education Commission's National Research Program for Universities.
  • Published a journal paper entitled "Detection and Classification of Malignant Melanoma Using Deep Features of NASNet"

UpWork

November 2020 - March 2021
Freelance Deep Learning Engineer
  • Developed an image captioning algorithm for image retrieval using image’s natural language description.
  • Designed and developed a GAN model for Covid detection in CT Scans.
  • Worked on various object detection projects using YOLO models.

Al-Khawarizmi Institute of Computer Science

January 2020 - October 2020
Research Assistant (Deep Learning)
    Worked on Melanoma Diagnosis in Bioinformatics Research Lab at KICS UET Lahore where I used Computer-Based Diagnosis (Deep Learning and Computer Vision) to detect and classify a rare type of skin cancer. Two research papers has been published from the outcomes of this research.
  • Worked with Prof. Dr. Muhammad Usman Ghani Khan on detection of rare and lethal Acral Lentiginous Melanoma.
  • Developed a detection system for acral melanoma in dermoscopic images using proposed CNN architecture.
  • Worked on Plant Disease Detection datasets and deployed classification models as REST APIs
  • Worked on Intelligent crop disease detection system using deep transfer learning.
  • Developed a deep learning algorithms for steel defect classification

Wizdojo Technologies

August 2019 - December 2019
Computer Vision Engineer
    Assisted in design and development of a Video Analytics System for cars at parking station which stores information related to cars, where it was parked, it's number plate and will inform user whether the specific place is available for parking or not. My responsibilities were to:
  • Research and develop best vehicle registration plate segmentation model.
  • Develop a large dataset from videos and annotation of images dataset.
  • Training, Testing and evaluation of deep learning model (Mask R-CNN) using test dataset.
  • Research Python code optimization techniques.
  • Developed a small website using HTML, CSS and Bootstrap.

Al-Khawarizmi Institute of Computer Science

October 2018 - December 2018
Computer Vision Intern
    Worked as a Computer Vision and Deep Learning Intern. I worked on following project
  • Learnt the basics of ANNs, CNNs and deep learning.
  • Developed small CNNs for object recognition.
  • Developed GUIs for Object Recognition models deployment (HTML/CSS/Bootstrap).
Development Projects
  • YouTube Visual Data Collection

    This is a small CLI tool to download YouTube frames without downloading and manually extracting frames from YouTube videos. Just provide a YouTube video link and the CLI tool will automatically download the frames in your target directory.
    Tech Stack & Libraries : Python, OpenCV, VidGear, ArgrParse
    GitHub Link

UOS

  • Video Subtitles Generation

    A python program for generating video subtitles. The program utilizes OpenAI's Whisper model for converting speech to text and then this data is used to generate subtitle files. The program currently can generate .srt and .vtt foramts for subtitles.
    Tech Stack & Libraries : Python, whisper
    GitHub Link

UOS
  • DeepDS: A python cli tool for dataset creation

    This is a useful cli tool currently under development. It accepts a video (local video file or YouTube link) and utilized optimized deep learning model to generate datasets for high level computer vision tasks. It reads video, runs deep learning model on individual frames and classified and sorts output frames into seprate directory based on predicted class.
    Tech Stack & Libraries : Python, OpenVino, Inception V3, VidGear, ArgreParse
    Github Link

UOS
  • Python Automation Scripts

    This repo has multiple automation scripts using Python. The scripts can be used in any computer vision projects. It has scripts to read, write, resize and perform multiple operations on images.
    Tech Stack & Libraries : Python, OpenCV, Augmentor, PyTorch, NumPy, Pandas
    GitHub Link

UOS
  • Metal Surface Defect Classification

    In this project a dataset for steel defect detection by NEU was used classification of steel surface defects. Dataset has total six classes. A ResNet-18 was finetuned to classify the input images into one of the six classes. The model achieved 97% accuracy (approx.)
    Tech Stack & Libraries : Python, FastAI, PyTorch, OpenCV, Pandas, Scikit-learn, Matplotlib
    GitHub Link

UOS
Research Projects
  • Skin Lesion Analysis for Skin Cancer Detection

    This was my MS thesis project. I worked on skin cancer (Acral Melanoma) detection and classification from Dermoscopy images. A data centric approach was used to enhance limited training data. Various deep learning models were trained to select the best model. Our AMNet model was a modified ResNet-18 architecture that performed better to diagnose Acral Melanoma. In addition, a NASNet model was also used for binary classification of Melanoma skin cancer.
    Tech Stack & Libraries : Python, OpenCV, Augmentor, PyTorch, Keras, TensorFlow, ResNets, MobileNets, NASNet
    GitHub Link | AM Detection | Melanoma Detection

UOS
  • Punjabi Named Entity Recognition

    This project tackles the problem of NER for low resource languages. We worked on data generation for Punjabi (Shahmukhi Script). A multilingual BERT model was trained for NER task. We developed a large training corpora using a simple and novel PoW augmentation technique. The publication is accepted in the prestigious ACM TALLIP.
    Tech Stack & Libraries : Python, BERT, NLTK, SpaCy
    GitHub Link | Paper

UOS
  • Efficient Network Intrusion Detection for Secure Systems

    We experimented with various machine learning models for recent intrusion detection datasets. NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB-15 datasets were used in the study. Five major ML algorithms were trained on preprocessed datasets. We proposed a hybrid, cost and compute efficient ensemble model for development of secure IDS systems.
    Tech Stack & Libraries : Python, scikit-learn, Pandas, NumPy, Keras
    GitHub Link | Paper

UOS
  • Lung Cancer Classification from Limited Data

    We used a recent dataset named as LC-25000 for lungs cancer classification. The dataset contains 15000 images of lungs histopathology. We trained two ResNets (18 and 50), MobileNet and AlexNet model for classification. A maximum accuracy of 98% was achived using ResNet-18 algorithm.
    Tech Stack & Libraries : Python, PyTorch, FastAI, OpenCV
    GitHub Link | Report

UOS
  • Detection & Classification of Firearms

    This was a term project for my Machine Learning course @UET during my MS. We collected and anotated a large datasets of firearms divided into five categories. Data was collected from YouTube and other video files from web. A YOLOv3 model was trained for detection and classification while a Mask-RCNN model was trained for instance segmentation of firearms. We achieved a 97% accuracy for detection and classification.
    Tech Stack & Libraries : Python, PyTorch, FastAI, OpenCV, yt_downloader
    GitHub Link | Report

UOS

Languages and Databases

Python3
Python 3
C
C
JavaScript
JavaScript
C++
C++
MATLAB
MATLAB
MySQL
MySQL

Machine Learning

Pandas
Pandas
NumPy
NumPy
OpenCV
OpenCV
sklearn
Scikit-learn
Matplotlib
Matplotlib
Plotly
Plotly
Seaborn
Seaborn

Deep Learning Frameworks

TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch
OpenVino
OpenVino

Web Engineering

HTML5
HTML5
CSS3
CSS3
Bootstrap
Bootstrap
FastAPI
FastAPI
Flask
Flask

Tools

Git
Git
Docker
Docker
Jupyter Notebook
Jupyter Notebook
Postman
Postman
VSCode
VSCode
Azure
Azure
AWS
AWS