I am a Computer Science student at NUST who thrives on learning, problem-solving, and building
production-grade AI systems. My work spans computer vision, medical imaging research, classical ML
pipelines, and agentic RAG platforms — always with a focus on measurable outcomes and clean engineering.
I value collaboration and clear communication, but I am equally comfortable taking initiative and
working independently. At my core, I am driven by a desire to make an impact through rigorous
research and scalable software, stay open to new perspectives, and keep evolving both personally
and professionally.
• Contributed to early-stage development of a vehicle speed estimation and license plate recognition system.
• Annotated industrial safety data using Labelme.
• Built backend for a Windows virtual camera app, streaming real-time UDP video via FFmpeg as a low-latency system-level source for Zoom, Teams, and Meet.
• Learned the complete research workflow, including literature review and identifying research gaps.
• Executed research-driven data workflows including academic paper retrieval, exploratory data analysis, and structured documentation using spreadsheets for literature synthesis.
Building an upcoming multi-tenant SaaS platform that helps researchers extract citation-grounded insights from scientific literature — turning dense PDFs into structured, trustworthy evidence for faster literature review.
Built a GPU-accelerated classical ML pipeline segmenting nuclei on 7,904 PanNuke images using 93 handcrafted features — with memmap streaming, Active Boundary Mining, GPU RFE (12× speedup), and an RF/XGBoost/LightGBM ensemble achieving 0.8802 Macro-F1.
Built a RAG system to answer academic policy queries from institutional documents — with ingestion, chunking, embedding, and retrieval pipeline for accurate context, integrated with LLMs to reduce hallucinations and ground responses.
Built a full clinical workflow prototype covering patient/case management, X-ray upload, AI landmark detection, interactive landmark refinement, and Ricketts cephalometric analysis with report generation — including drag-and-drop landmark editor with undo/redo.
@mananbyte
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