Quality Assurance .Net C#

Talha Hussain Syed

*****@*******.*******.*** 737-***-****

Summary:

Aspiring Engineer with a solid foundation in software development, quality assurance, and CI/CD integration. Proficient in building .NET C# applications and Azure App-Services, with expertise in behavior-driven development, database validation, and streamlining testing pipelines using tools like Jenkins and Azure DevOps. A collaborative problem-solver with research and leadership experience, dedicated to delivering efficient, high-quality software solutions.

Education:

M.S. in Computer Science and Engineering - GPA: 3.86 University of Toledo, Toledo, OH, USA. Aug 2022 – Present B.Tech. in Computer Science and Engineering - GPA: 3.6 JNTU, India. Aug 2016 – July 2021

Technical Skills:

Programming Languages: C#, Python, JavaScript, HTML, CSS, VHDL, SQL Frameworks & Libraries: .Net Framework 4.5, .Net Core, React, MS-Test, X-Unit Tools: Visual Studio, Visual Studio Code, Postman, SSMS, Tableau, Snowflake, Fiddler

Database: MS SQL Server, Mongo DB, Cosmos DB

CICD: Jenkins, Azure DevOps

Performance: Azure-AppInsights

Professional Experience:

Software Engineer

9I Web Solutions Pvt Ltd Hyderabad, India February 2021 – December 2022

• Developed and optimized software applications and Microservices across .NET frameworks, resolving performance issues and ensuring system stability.

• Engineered and deployed multiple web applications for healthcare customers, providing health management, fitness, and exercise programs, serving over 2 million users and managing subscriptions.

• Created scalable APIs using .NET 6.0 and microservice architecture for fitness clients, enhancing service delivery with scheduled jobs via Hangfire and feature toggles.

• Secured application data by integrating Azure Key Vault and Azure App Configuration to manage and protect configuration information.

• Validated application functionality by conducting unit and integration testing to ensure seamless module interactions and software reliability.

• Improved system integration through comprehensive API testing with Postman, ensuring reliable endpoints and optimized performance.

Talha Hussain Syed

*****@*******.*******.*** 737-***-****

• Enhanced collaboration by implementing behavior-driven testing (BDD) with Gherkin syntax, bridging the gap between technical and non-technical teams.

• Streamlined development cycles by integrating automated tests into CI/CD pipelines, providing immediate feedback on software quality.

• Collaborated effectively with Developers, Product Managers, and QA Engineers to design test strategies, identify critical defects, and increase overall testing coverage. Research Assistant – Data Scientist

University of Toledo, Toledo, OH Aug 2022 – Present

• Developed predictive models in Python to analyze and simulate vulnerabilities in FPGA-based systems, enhancing system reliability and security.

• Designed and generated synthetic datasets using Generative Adversarial Networks (GANs), coupled with data preprocessing and feature engineering using Pandas and NumPy for optimal modeling performance.

• Implemented machine learning algorithms such as Random Forest, Support Vector Machines (SVMs), and Neural Networks to predict Challenge Response Pairs (CRPs), leveraging GANs for advanced dataset generation and analysis.

• Utilized Pandas and NumPy to preprocess and analyze large hardware datasets, ensuring data consistency and quality for machine learning applications.

• Built data validation workflows with MATLAB and Scikit-learn, ensuring the accuracy, robustness, and reliability of machine learning models in detecting hardware vulnerabilities. Publications:

• Hardware Trojan Detection employing Machine Learning, Physical Unclonable Functions and Side Channel Analysis, Submitted to EIT 2024 – University of Wisconsin-Eau Claire, Wisconsin, USA. Developed machine learning-based approaches for detecting Trojans in FPGA circuits through side channel analysis. Used predictive classification algorithms on CRP data sets.

• Protecting Hardware IP by Employing Non-Fungible Tokens (NFTs), Presented at NAECON 2023, Ohio, USA. Explored the use of NFTs and blockchain technology to secure hardware intellectual property.

• Attacks on Physical Unclonable Functions employing Generative AI and Machine Learning, in progress Investigated the use of Generative AI to simulate attacks on hardware security primitives, specifically focusing on unclonable challenge response pairs.

Google Scholar: https://scholar.google.com/citations?user=brtnf90AAAAJ&hl=en

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