Junaid Hassan
Independent AI Researcher & Founder, Codoplex
I build production AI systems — large language models, retrieval-augmented generation, and agentic architectures — and research how to make them more reliable. Published in Neural Processing Letters. Currently pursuing doctoral research opportunities in AI/ML.
About
I hold a BSc in Telecommunications Engineering (UET Taxila) and an MSc in Computer Science with a specialization in AI and Deep Learning (University of Gujrat). My MSc thesis proposed a gated recurrent neural network architecture for text classification, later published in Neural Processing Letters (Hassan & Shoaib, 2020) after peer review.
Over a decade of software engineering, I’ve moved from full-stack web development into building production systems around large language models — retrieval-augmented generation, multi-agent orchestration, fine-tuning, and multi-backend inference infrastructure — as founder of Codoplex. I’m now applying that practitioner’s perspective to doctoral-level research in AI/ML.
Research Focus
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Agentic AI Systems
Multi-agent orchestration, tool-use, and inter-agent communication for complex, multi-step tasks.
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Retrieval-Augmented Generation
Grounding LLM outputs in retrieved context to reduce hallucination and improve reliability.
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Efficient Adaptation
Parameter-efficient fine-tuning (LoRA/PEFT) and multi-backend inference routing for cost and reliability.
Publications
Peer Reviewed
Hassan, J., & Shoaib, U. (2020). Multi-class Review Rating Classification using Deep Recurrent Neural Network. Neural Processing Letters, 51, 1031–1048.
Junaid Hassan, Muhammad Shahzad Sarfraz (2019). Impact of Suicide Bombings in Pakistan using Spatial and Temporal Analysis. Proceedings of SPIE, Vol. 11174 (Seventh International Conference on Remote Sensing and Geoinformation of the Environment, RSCy2019), 111740S.
Preprints
Hassan, J. (2026). Latent Attribution Regularization: Cross-Domain Attribution-Consistency Training for Stable
NLP Explanations. Research Square (preprint). Currently under submission to a peer reviewed journal.
Books & Technical Writings
From Zero to Agents: self published a technical book (Amazon, 2026). A from first principles AI/ML curriculum covering foundational mathematics through transformers, fine tuning, and agentic systems, with every implementation independently derived and empirically verified.
Let’s connect
Message me on WhatsApp or send an email.