Arbisoft Job - Senior ML Engineer

About Me

I am an AI Engineer, Data Scientist, Web3 Developer, and a Ph.D. candidate in Computer Science & Engineering with a focus on AI, and privacy-preserving technologies. Skilled in Python, SQL, JavaScript, and Solidity, I have a proven record of implementing machine learning and deep learning algorithms using frameworks like TensorFlow, PyTorch, and Scikit-learn. Additionally, my solid foundation in object-oriented programming supports my ability to design efficient and scalable solutions.

Throughout my academic journey, I’ve published research in international journals and conferences on decentralized learning and federated systems, which has sharpened my technical expertise and collaborative skills.I thrive in multidisciplinary teams, working closely with data scientists and engineers to develop tailored AI-driven solutions.

I’m excited to bring my skills to Arbisoft, contributing to impactful projects and advancing their mission to enhance customer experiences with advanced AI technologies.

Skills

Object-Oriented Programming Concepts

Class Object Inheritance Polymorphism Encapsulation Abstraction

Programming

Artificial Intilligence

Other Skills

Familar IDEs and text Editors

Familar OS

Generative AI

Publications

International Journals

1. Umer Majeed, Sheikh Salman Hassan, Zhu Han, and Choong Seon Hong, “DAO-FL: Enabling Decentralized Input and Output Verification in Federated Learning with Decentralized Autonomous Organizations,” TechRxiv. Preprint, Dec 2023.

2. Umer Majeed, L. U. Khan, Sheikh Salman Hassan, Zhu Han, and Choong Seon Hong, “FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training,” IEEE Access, Jan 2023.

3. Umer Majeed, L. U. Khan, Abdullah Yousafzai, Zhu Han, Bang Ju Park and Choong Seon Hong, “ST-BFL: A Structured Transparency empowered cross-silo Federated Learning on the Blockchain framework,” IEEE Access, Nov 2021. (DOI: 10.1109/ACCESS.2021.3128622)

International Conferences

1. Umer Majeed, Sheikh Salman Hassan, Choong Seon Hong, “Cross-Silo Model-Based Secure Federated Transfer Learning for Flow-Based Traffic Classification,” International Conference on Information Networking (ICOIN 2021), Jan 13 - 16, 2021, Jeju Island, Korea (South). (DOI: 10.1109/ICOIN50884.2021.9333905)

2. Umer Majeed, Latif U. Khan, Choong Seon Hong, “Cross-Silo Horizontal Federated Learning for Flow-based Time-related-Features Oriented Traffic Classification,” 21st Asia-Pacific Network Operations and Management Symposium (APNOMS 2020), September 22 - 25, 2020, Daegu, Korea (South). (DOI: 10.23919/APNOMS50412.2020.9236971)

Domestic Conferences (Korean)

1. Umer Majeed, Sheikh Salman Hassan, Choong Seon Hong, “Vanilla Split Learning for Transportation Mode Detection using Diverse Smartphone Sensors”, 2021년 한국컴퓨터종합학술대회(KCC 2021), 2021.06.23.

2. Umer Majeed, Choong Seon Hong, “A Transfer Learning Approach for Rapid Classification of Networks Traffic,” 2020년 한국소프트웨어종합학술대회(KSC 2020), 2020.12.21~23.

3. Umer Majeed, Choong Seon Hong, “Blockchain-assisted Ensemble Federated Learning for Automatic Modulation Classification in Wireless Networks,” 2020년 한국컴퓨터종합학술대회(KCC 2020), 2020.07.02~04.

MOOCS Completed

Deep Learning Specialization - Coursera

Deep Learning Specialization - Coursera by deeplearning.ai

• Neural Networks and Deep Learning - Completed with certificate - July 2021 -
  Gain a deep understanding of neural networks, implement architectures, and optimize through hyperparameter tuning and regularization.

• Improving Deep Neural Networks - Completed with certificate - August 2021 -
  Explore advanced techniques like hyperparameter tuning, optimization algorithms (Adam, RMSprop), regularization methods (dropout, batch normalization), and implement models using TensorFlow.

• Structuring Machine Learning Projects - Completed with certificate - Oct. 2021 - Diagnose errors in ML systems, implement strategies like end-to-end learning and transfer learning, and set human-level performance benchmarks for complex tasks.

• Convolutional Neural Networks - Completed with certificate - Oct. 2021 -

  Explore CNN layers, advanced architectures like ResNet, apply object detection techniques (YOLO, U-Net), and create models for applications like face recognition and neural style transfer.

IBM Data Science Professional Certificate - Coursera

IBM Data Science Professional Certificate - Coursera - Audit Completed with Labs - Aug. 2024

• What is Data Science? - April, 2024 - Audit Completed with Labs
  Understand data science fundamentals, career paths, big data processing, ETL, and data pipelines. Gain insights into data science applications and cloud computing.

• Tools for Data Science - April, 2024 - Audit Completed with Labs
  Explore tools for data management, integration, visualization, model building, and deployment. Learn about popular open-source and cloud-based tools.

• Data Science Methodology - April, 2024 - Audit Completed with Labs
  Apply CRISP-DM methodology to structure projects, prepare data, build and evaluate models, and understand iterative improvements.

• Python for Data Science, AI & Development - April, 2024 - Audit Completed with Labs
  Learn Python basics, data structures, Pandas, Numpy, web scraping, REST APIs, and data collection methods.

• Python Project for Data Science - April, 2024 - Audit Completed with Labs
  Extract and analyze stock data using Python, build dashboards to visualize trends, and demonstrate proficiency in data analysis projects.

• Databases and SQL for Data Science with Python - May, 2024 - Audit Completed with Labs
  Learn SQL from basics to advanced, integrate with Python, and work with real-world datasets. Explore relational and cloud databases.

• Data Analysis with Python - May, 2024 - Audit Completed with Labs
  Develop skills in data cleaning, exploratory data analysis, and visualization. Build and evaluate ML models, and create efficient data pipelines.

• Data Visualization with Python - June, 2024 - Audit Completed with Labs
  Implement data visualization techniques with libraries such as Matplotlib and Plotly, build interactive dashboards, and apply skills through hands-on projects.

• Machine Learning with Python - June, 2024 - Audit Completed with Labs
  Study machine learning fundamentals, including regression, classification, and clustering methods. Gain practical experience with Python libraries and complete a final project to showcase your skills.

• Applied Data Science Capstone - August, 2024 - Audit Completed with Labs
  Perform data collection, wrangling, exploratory analysis, and model evaluation using real-world datasets, specifically predicting Falcon 9 rocket landings.

• Generative AI: Elevate Your Data Science Career - July, 2024 - Audit Completed with Labs
  Learn generative AI tools for data preparation and querying, engage in hands-on labs, explore real-world use cases, and understand ethical considerations in data science.

• Data Scientist Career Guide and Interview Preparation - August, 2024 - Audit Completed with Labs
  Prepare for a career in data science by learning about the role of a data scientist, building a job search foundation, creating essential job-seeking materials, and mastering interview preparation techniques.

IBM Data Analyst Professional Certificate - Coursera

IBM Data Analyst Professional Certificate - Coursera

• Introduction to Data Analytics - Sep, 2024 - Audit Completed with Labs
  Learn the fundamentals of Data Analytics, different data roles, data structures, the analysis process, and tools for data gathering and visualization.

• Excel Basics for Data Analysis - Sep, 2024 - Audit Completed with Labs
  Gain insight into Excel for data analysis, including data cleaning, filtering, sorting, and pivot tables.

• Data Visualization and Dashboards with Excel and Cognos - Sep, 2024 - Audit Completed with Labs
  Create basic and advanced visualizations using Excel and Cognos Analytics, including interactive dashboards, to effectively communicate data-driven stories.

• Generative AI: Enhance your Data Analytics Career - Sep, 2024 - Audit Completed with Labs
  Describe how to use Generative AI tools in data analytics, implement data analytic processes, evaluate case studies, and analyze ethical considerations.

• Data Analyst Career Guide and Interview Preparation - Oct, 2024 - Audit Completed with Labs
  Prepare for data analyst job applications, build portfolios, resumes, and cover letters, practice interviews, coding challenges, and hands-on labs (SQL and Python).

AI For Everyone - Course - Coursera

• AI For Everyone by deeplearning.ai - Coursera - Completed with certificate - Dec. 2019 -
  This course provides a comprehensive introduction to artificial intelligence (AI), focusing on its applications, impact on society, and essential concepts such as machine learning, deep learning, and data science. Participants will gain an understanding of how AI can be utilized in various industries and its relevance in the modern world.

Python for Everybody - Course - Coursera

• Python for Everybody - Coursera - Completed with certificate - March 2020 -
  This course provides a thorough introduction to programming and Python basics for automation tasks in IT roles. It covers key topics including syntax, data types, loops, and advanced string manipulation with practical, hands-on exercises.

Fundamentals of Reinforcement Learning - Course - Coursera

• Fundamentals of Reinforcement Learning by UNIVERSITY OF ALBERTA, ALBERTA MACHINE INTELLIGENCE INSTITUTE - from Reinforcement Learning Specialization- Coursera Completed with certificate - Dec. 2020 -
  This course introduces the core principles of reinforcement learning, covering key topics such as Markov decision processes, value functions, and policy gradients. Learners will explore fundamental algorithms and techniques used in reinforcement learning, gaining practical experience through programming assignments and projects.

Generative AI and LLMs: Architecture and Data Preparation

• Generative AI and LLMs: Architecture and Data Preparation - from Generative AI Engineering with LLMs Specialization - Oct, 2024 - Audit Completed with Labs

  Learn about generative AI architectures like RNNs, Transformers, VAEs, GANs, and Diffusion Models, and explore LLMs such as GPT, BERT, BART, and T5 for language processing. Implement tokenization with NLP libraries (NLTK, spaCy, BertTokenizer) and create NLP data loaders using PyTorch.

Datacamp

https://www.datacamp.com/portfolio/umermajeed

• Introduction to Python - Completed with certificate - 2017 -

• Intermediate Python - Completed with certificate - 2017 -

• Data Types for Data Science in Python - Completed with certificate - 2017 -

• Python Data Science Toolbox (Part 1) - Completed with certificate - 2017 -

• Python Data Science Toolbox (Part 2) - Completed with certificate - 2017 -

• Statistical Thinking in Python (Part 1) - Completed with certificate - 2017 -

• Statistical Thinking in Python (Part 2) - Completed with certificate - 2017 -

• Introduction to Version Control with Git - Completed with certificate - 2018 -

• Intermediate SQL Queries - Completed with certificate - 2017 -

• Introduction to Shell - Completed with certificate - 2018 -

• Introduction to Data Visualization in Python - Completed with certificate - 2017 -

• Intermediate Data Visualization with Seaborn - Completed with certificate - 2018 -

MOOCS in Progress

• Sequence Models - from Deep Learning Specialization - Coursera - In progress
  Implement RNNs, GRUs, LSTMs, and transformers for NLP tasks like machine translation and named entity recognition, and apply attention mechanisms for enhanced performance.

• IBM Data Analyst Capstone Project - from IBM Data Analyst Professional Certificate - Coursera - In progress

  Apply data analysis techniques such as data collection, wrangling, visualization, and dashboard creation using Python, SQL, Cognos, and various libraries to solve real-world business challenges.

MOOCS Planned in future

IBM DevOps and Software Engineering Professional Certificate - Coursera

IBM DevOps and Software Engineering Professional Certificate - Coursera

• Introduction to DevOps
  Learn the fundamentals of DevOps, including building a culture of shared responsibility, transparency, and embracing failure. Explore key concepts like Continuous Integration, Continuous Delivery, Infrastructure as Code, Test Driven Development, and Behavior Driven Development. Understand how to implement cloud native microservices, automated continuous deployments, and how to break down silos for better organizational collaboration.

• Introduction to Cloud Computing
  Learn the fundamentals of cloud computing, including cloud service models (IaaS, PaaS, SaaS), deployment models (Public, Private, Hybrid), and infrastructure components. Explore key trends like Hybrid Multicloud, Serverless, and Cloud Native. Understand offerings from AWS, Azure, Google Cloud, and more.

• Introduction to Agile Development and Scrum
  Gain insight into Agile principles and the Scrum framework. Learn to create effective user stories, manage product backlogs, and utilize metrics for performance improvement. The course covers Agile methodologies like Kanban and Extreme Programming (XP) and emphasizes collaborative, self-organizing team dynamics. Ideal for project managers, product managers, and IT practitioners seeking to enhance their Agile practices.

• Getting Started with Git and GitHub
  Learn version control fundamentals with Git and GitHub, covering repositories, branches, and collaborative coding. Create and share an open-source project to enhance your portfolio.

• Hands-on Introduction to Linux Commands and Shell Scripting
  Gain practical understanding of Linux and Bash shell commands, including file management, scripting basics, and cron job scheduling. Ideal for beginners seeking to automate tasks using Linux commands and scripts.

• Developing AI Applications with Python and Flask
  This course guides you through creating AI-enabled applications using Python and the Flask framework, including unit testing and packaging. Ideal for those with foundational Python skills, it features hands-on projects using IBM Watson AI Libraries to develop and deploy web applications.

• Introduction to Containers w/ Docker, Kubernetes & OpenShift
  This intermediate course covers the fundamentals of containerization using Docker, Kubernetes, OpenShift, and Istio, teaching you to build cloud-native applications and manage them across various environments. It equips learners with the skills to describe Kubernetes architecture, set up container management systems, and create resources using YAML deployment files.

• Application Development using Microservices and Serverless
  This intermediate course guides learners through microservices and serverless technologies, emphasizing the creation and deployment of REST APIs and microservices using Docker containers and IBM Cloud Code Engine. Gain hands-on experience with labs and projects, culminating in a final project where you deploy a serverless microservices-based application in the cloud.

• Introduction to Test and Behavior Driven Development
  Explore the essentials of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) through practical labs and projects. This intermediate course is ideal for those looking to enhance their software testing skills and apply them in real-world scenarios.

• Continuous Integration and Continuous Delivery (CI/CD)
  Develop a comprehensive understanding of CI/CD within a DevOps pipeline, including automated software development, social coding, Git Feature Branch Workflow, and tools like Jenkins and GitHub Actions. Ideal for intermediate learners aiming to enhance their skills in Infrastructure as Code, automation, and deployment to Kubernetes environments.

• Application Security for Developers and DevOps Professionals
  Develop a deep understanding of security practices for application development, including OWASP principles, vulnerability scanning, and security testing. Enhance your skills with hands-on labs and projects to identify, mitigate, and prevent security threats in software development environments. Ideal for intermediate learners with Python programming experience.

• Monitoring and Observability for Development and DevOps
  Learn the fundamentals of monitoring and observability with hands-on experience in tools like Prometheus, Grafana, and Instana. This intermediate-level course covers key concepts such as Golden Signals, logging, telemetry, and tracing for container applications. Ideal for professionals looking to enhance application performance monitoring skills.

• DevOps Capstone Project
  Showcase your DevOps and Software Engineering skills through a hands-on Capstone project. Develop, test, deploy, and enhance a secure microservices-based application on Cloud, utilizing Agile planning, CI/CD tools, and Kubernetes over several sprints. Ideal for those completing the IBM DevOps Engineering Professional Certificate.

Generative AI Engineering with LLMs Specialization

Generative AI Engineering with LLMs Specialization

• Generative AI and LLMs: Architecture and Data Preparation
• Gen AI Foundational Models for NLP & Language Understanding
• Generative AI Language Modeling with Transformers
• Generative AI Engineering and Fine-Tuning Transformers
• Generative AI Advance Fine-Tuning for LLMs
• Fundamentals of AI Agents Using RAG and LangChain
• Project: Generative AI Applications with RAG and LangChain

IBM Data Engineering Professional Certificate

IBM Data Engineering Professional Certificate

• Introduction to Data Engineering
• Python for Data Science, AI & Development
• Python Project for Data Engineering
• Introduction to Relational Databases (RDBMS)
• Databases and SQL for Data Science with Python
• Hands-on Introduction to Linux Commands and Shell Scripting
• Relational Database Administration (DBA)
• ETL and Data Pipelines with Shell, Airflow, and Kafka
• Data Warehouse Fundamentals
• BI Dashboards with IBM Cognos Analytics and Google Looker
• Introduction to NoSQL Databases
• Introduction to Big Data with Spark and Hadoop
• Machine Learning with Apache Spark
• Data Engineering Capstone Project
• Generative AI: Elevate your Data Engineering Career
• Data Engineering Career Guide and Interview Preparation

Portfolio

Deep Learning

Simple CNN models - Happyface and Digit hand Signs

• This repository highlights projects showcasing deep learning applications using TensorFlow and Keras.
• Focuses on convolutional neural networks (ConvNets) for image classification tasks.
• Demonstrates practical use of both the Sequential and Functional APIs for model development.

ResNet - Digit hand Signs

• The Kaggle notebook demonstrate the developement, training, and testing of ResNet Model using TensorFlow and Keras. The validation accuracy of 100% is achieved by best model using checkpoints.

Transfer Learning using MobileNet - alpaca, non-alpaca dataset

• The project demonstrate the developement, training, and testing of MobileNet Model using TensorFlow and Keras through Transfer Learning for alpaca, non-alpaca dataset.

• The kaggle notebook demonstrate 100% accurate MobileNet Model using TensorFlow and Keras through Transfer Learning for alpaca, non-alpaca dataset and advanced data augmentation techniques.

Object Detection using yolov2

• This notebook demonstrates object detection using YOLOv2, leveraging technologies such as TensorFlow for deep learning and the PIL library for image processing to identify and classify objects with bounding boxes for 80 classes supported by the COCO dataset. Users can process any input image, visualize detection results, and benefit from the model’s efficiency and versatility in real-time applications.

Image segmentation using Unet

• This project focuses on developing and optimizing a U-Net model for image segmentation tasks. It involves training on RGB images paired with corresponding masks to accurately segment images.

Face recognition using facenet

• This project implements a state-of-the-art facial recognition system using the FaceNet model, which generates 512-dimensional embeddings of face images to perform verification and recognition tasks.

Machine Learning

SpaceX Falcon 9 ML Project

This notebook details an ML project focusing on SpaceX Falcon 9 launches that encompasses:

• Data Collection via API and web scraping
• Data Wrangling and Exploratory Data Analysis (EDA)
• Visualization and Interactive Dashboards using Plotly Dash and Folium
• Predictive Analysis through classification techniques

EDA

Tesla and GameStop Stock/Revenue Data and Dashboard

This notebook details an exploratory data analysis on historical stock data focusing on Tesla and GameStop Stock/Revenue Data that encompasses:

• Fetch Data: Utilizing the yfinance library to extract stock data for your company of interest
• Analyze: Explore key metrics, visualize trends, and draw insights from the data.
• Report Findings: Summarize the analysis and trends in alignment with real-world market behavior and financial performance.

Socioeconomic Indicators in Chicago (2008-2012)

This notebook presents a comprehensive exploratory data analysis (EDA) of socioeconomic indicators in Chicago from 2008 to 2012. Through a combination of visualizations and statistical summaries, this notebook aims to uncover trends and insights related to Chicago’s socioeconomic landscape during the specified period. The analysis includes:

• Pairplots: Visualizing relationships between multiple variables.
• Heatmaps: Illustrating data correlation and distributions.
• Correlation Matrix: Examining relationships between different socioeconomic features.
• Descriptive Statistics: Summarizing key metrics of the dataset.
• Detailed Analysis: In-depth exploration of the correlation matrix for various features.

Google Looker

Sales and Service Analysis Report for SwiftAuto Traders - Looker Dashboard Project

This Looker report captures the detailed analysis and visualizations for both the Sales and Service dashboards, allowing for a comprehensive view of the performance metrics at SwiftAuto Traders.

This report presents an analysis of car sales and profits for each dealer at SwiftAuto Traders. The analysis aims to provide insights into key performance indicators (KPIs) that can assist in making informed business decisions. The report is divided into two main sections: Sales and Service.

Sales Dashboard

KPI Metrics

• Total Profit: $X.XX million
• Total Quantity Sold: Y units
• Average Quantity Sold: Z units

Visualizations

1. Quantity Sold by Model
2. Profit by Dealer ID

Service Dashboard

Visualizations

1. Number of Recalls per Model
2. Customer Sentiment Analysis
3. Monthly Car Sales vs. Profit
4. Recalls by Model and Affected System

Products and Sales Analysis Report for Customer Loyality Program - Looker Dashboard Project

KPI Metrics

• Total Revenue: $X.XX million
• Total Quantity Sold: Y units

Visualizations

1. Line Chart - Quantity Sold of d/f Product lines by Year
2. Bar Chart - Total Quantity Sold of to Male or female customers - Gender Slicer
3. Line and bar chart for average unit sale price and total revenue with product line slicer.
4. Quantity Sold of d/f product lines on world map
5. heat map of quantity sold on world map.
6. treemap of quantity sold and revenue by country, state, and city.
7. Word cloud of revenue by state or province.
8. Bubble chart of revenue by loyality status and product line.

Education

• 2017 - Present
  Master & Ph.D. (Combined) in Computer Science & Engineering
  CGPA: 4.11/4.3
  Department of Computer Science & Engineering, Kyung Hee University, Yongin, South Korea

• 2011 - 2015
  BS in Electrical (Telecommunication) Engineering
  CGPA: 3.83/4.00
  National University of Sciences & Technology (NUST), Islamabad, Pakistan

Experience

PHP Developer

Artologics, Islamabad, Pakistan
2015 – 2016

• Developed robust back-end applications using Core PHP and CodeIgniter framework.
• Implemented jQuery and JavaScript to facilitate smooth communication between the user interface and server-side components via AJAX requests, enhancing the interactivity of web applications.
• Employed SQL queries to interface with MySQL databases, ensuring data integrity and reliability while developing robust solutions for efficient data management.

Technologies: