Independent AI researcher and founder of Khwand AI, building open-source models, datasets, and research at the intersection of machine learning, cybersecurity, and trustworthy AI.
Interests: trustworthy AI, reproducible research, practical ML systems, and building tools that make complex technology more reliable and useful.
Highlighted contributions across research, datasets and engineering.
Submitted work on governance, institutional coordination and normative frameworks for responsible AI transition.
Read moreA benchmark dataset for evaluating safety-critical refusal calibration in instruction-tuned language models.
View datasetAI-native self-healing CI/CD platform using multi-agent orchestration for autonomous pipeline failure resolution.
Learn moreKhwand is an assurance platform for AI agents, helping teams test, harden, and monitor agentic systems before they ship.
Turn brittle agent workflows into production-ready systems with automated checks, regression testing, and safer deployments.
Quantify how ready an agent is to ship with a clear score that surfaces weak spots, failure modes, and reliability gaps.
Move beyond ad-hoc debugging with monitoring, traceability, and resilience checks that keep agents dependable over time.
Designed for founders, platform engineers, and AI teams who need practical guardrails without slowing delivery.
Datasets published on Hugging Face for experimentation, benchmarking and model development.
Cybersecurity-focused benchmark dataset for identifying threats, vulnerabilities and attack vectors in text.
Benchmark dataset for evaluating safety, calibration and robustness of language models in real-world settings.
Core areas of investigation and contribution.
A selection of credentials spanning AI engineering, data science, NLP, cybersecurity, and cloud foundations.
My academic path combines formal study in artificial intelligence and data science with early leadership and service experience.
BSc, Artificial Intelligence and Data Science
2025 – 2028
First-year modules included Computing Challenge, Systems Analysis and Database Design, C++ Programming, Machine Learning Foundations, Ethics, Data and Professionalism, and Mathematics and Statistics for AI and Data Science.
Bachelor of Applied Science – BASc, Artificial Intelligence
Sep 2024 – Aug 2025
A Levels in Computer Science, Business, Graphic Design, and EPQ
Sep 2022 – Aug 2024
Grades: A, A, A, D.
Served as a Cadet and later Cadet Lance Corporal through the Combined Cadet Force at Maiden Erlegh School in Reading. This experience strengthened my leadership, discipline, reliability, problem-solving, and time-management skills.
Maiden Erlegh School in Reading — GCSEs (Sep 2019 – Aug 2022), grades: 6, 6, 5, 5, 4, 4, 4, 4 M1.
Roots Millennium Schools & Colleges – TME — River Tree Campus, Nowshera (Dec 2017 – Jul 2019).
The City School — City School Nowshera (Sep 2016 – Dec 2017).
Alfred Sutton Primary School, Reading and New Town Primary School, Reading for earlier schooling.
Peer-reviewed research across machine learning, cybersecurity and trustworthy AI evaluation.
Selected engineering work spanning AI platforms, developer tools and SaaS systems.
AI-powered, self-healing CI/CD platform for AI-native engineering teams. Automatically detects, diagnoses, and fixes pipeline failures without human intervention using a LangGraph multi-agent orchestration system.
SaaS management system for mobile laptop repair shops — integrating device history security checks, business analytics, digital record-keeping, and remote management tools for small business owners.
Repositories and contributions to the open-source ecosystem.
Multi-agent orchestration framework for AI-native CI/CD pipelines
Production-grade RAG pipeline with hybrid retrieval and re-ranking
Benchmarking suite for cybersecurity ML classifiers with statistical testing
Framework for evaluating LLM trustworthiness across safety and fairness
Contributed BYearEnd docstring with examples — pandas-dev/pandas
Began exploring machine learning, cybersecurity and trustworthy AI — building the foundation for future work.
Published first peer-reviewed paper on significance-aware benchmarking for phishing detection classifiers.
Shipped CyberSecBench and TrustworthyLLM frameworks. Merged first contribution to pandas (pandas-dev/pandas).
Released cissp-llmbench and safecalib-bench on Hugging Face for public experimentation and benchmark evaluation.
Launched Khwand AI — an emerging startup focused on practical ML systems, trustworthy AI and developer tooling.
Tools and technologies used in research, engineering and production systems.