Hasaan Hamid
NUST
· 2026
Email
hasaanhamid77@gmail.com
Phone
923447670008
GitHub
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Academic
Program
BS Computer Science
CGPA
3.54
Year
2026
Education
SEECS
Address
Sector H-12 , Islamabad , Pakistan
DOB
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Career
Current role
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Target role
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Skills
NLP, Computer Vision, Mass Spectrometry, Time-series Forecasting, Selenium, BeautifulSoup, Transformer, CNN, RAG Pipelines, PEFT/LoRA, Quantization, Knowledge Distillation, Federated Learning, FastAPI, Docker, Kubernetes, Cloud-native Microservices, TensorFlow Lite, PyTorch, Avalanche, Xception, EfficientNet, ConvNeXt, ViT, M2M100, Grad-CAM, SHAP, Bayesian Layers, Posterior Predictive Analysis
Verbatim text
The exact text the LLM saw on the page (or the booklet text from the old import).
This is what powers semantic search.
Hasaan Hamid Cell: 923447670008 | Email: hasaanhamid77@gmail.com LinkedIn: https://www.linkedin.com/in/ hasaan-hamid-06229633a Address: Sector H-12 , Islamabad , Pakistan PROFESSIONAL PROFILE Computer Science undergraduate at NUST (CGPA 3.5) with experience building end-to-end, production-grade AI systems across NLP, computer vision, mass spectrometry–based spectral analysis, and time-series forecasting. Strong ownership of the full ML lifecycle, from automated data acquisition (Selenium, BeautifulSoup) and large-scale preprocessing to transformer/CNN model development and optimization. Hands-on experience with RAG pipelines, PEFT/LoRA, quantization, knowledge distillation, and federated learning. Proven ability to deploy and scale ML services using FastAPI, Docker, Kubernetes, and cloud-native microservices with low-latency constraints. EDUCATION BS Computer Science SEECS , Islamabad , 3.54 (2026) INTERNSHIP EXPERIENCE truID 30-Jun-2025 - 22-Aug-2025 Engineered production-grade computer vision and NLP systems for identity verification, owning the pipeline from data acquisition and automated scraping (Selenium, BeautifulSoup) to model training, optimization, and deployment. Developed and benchmarked state- of-the-art CNN and Transformer architectures (Xception, EfficientNet, ConvNeXt, ViT) for card liveness detection and replay-attack prevention under real-world constraints. Built and fine-tuned custom M2M100-based transliteration models, applying PEFT/LoRA, knowledge distillation, and quantization to achieve low-latency inference. Optimized models for mobile and edge deployment using TensorFlow Lite, ensuring robustness, scalability, and sub-100ms performance in production environments. Machine Vision and Intelligent Systems Lab 01-Jun-2025 - 20-Aug-2025 Engineered continual learning pipelines using Avalanche to support incremental, non-stationary training regimes on high-dimensional biomedical and spectral datasets. Designed unified, cross-species architectures to replace legacy single-species models, emphasizing domain generalization, distribution shift robustness, and zero-/few-shot transfer. Implemented custom hybrid CNN– Transformer backbones and bespoke training loops in PyTorch, optimizing representation learning across heterogeneous modalities. Integrated model interpretability and calibrated uncertainty estimation, leveraging Grad-CAM, SHAP, Bayesian layers, and posterior predictive analysis to ensure reliable and explainable inference in safety-critical settings. FINAL YEAR PROJECT Pathoshield PATHOSHIELD is an AI-driven antimicrobial stewardship and surveillance system that predicts antimicrobial resistance (AMR) from MALDI-TOF mass spectrometry data. It replaces traditional rule-based diagnostics with deep learning models—both species-specific and cross-species—that generalize across hospitals, bacterial species, and evolving resistance patterns. The system uses hybrid CNN and CNN–Transformer architectures in PyTorch, employs continual learning to handle real-world non-stationarity, and integrates explainability (Grad-CAM, SHAP) and Bayesian uncertainty estimation for interpretable, trustworthy predictions. PATHOSHIELD enables real-time AMR surveillance, uncertainty-aware decision support, and electronic prescription integration, making it a scalable, deployable tool for modern microbiology labs and public health systems. TECHNICAL EXPERTISE
AI enrichment
Hasaan Hamid is a Computer Science undergraduate with a 3.54 CGPA, specializing in end-to-end AI system development across NLP, computer vision, and spectral analysis. He possesses hands-on experience in the full ML lifecycle, including data acquisition, model optimization via PEFT and quantization, and deployment using cloud-native technologies.
Skills (AI)
["Python", "PyTorch", "TensorFlow Lite", "Computer Vision", "NLP", "Transformers", "CNN", "RAG Pipelines", "PEFT/LoRA", "Quantization", "Knowledge Distillation", "Federated Learning", "FastAPI", "Docker", "Kubernetes", "Selenium", "BeautifulSoup", "Continual Learning", "Model Interpretability", "Grad-CAM"]
Status: ai_done