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Eman Chaudhary

NUST · 2026

eman.abc05@gmail.com

Phone

03354468887

https://www.linkedin.com/in/eman-chaudhary/

GitHub

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Academic

Program

BSE

CGPA

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Year

2026

Education

SEECS

Address

Sector G-14 , Islamabad , Pakistan

DOB

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Career

Current role

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Target role

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Skills

Generative AI, Diffusion-based generative models, Deep learning, Edge hardware, Root Cause Analysis (RCA), Code Llama, RAG frameworks, Transformer models, Anomaly detection, OpenTelemetry, Logstash, CNNs, 5G channel estimation, INT8 quantization, NVIDIA Jetson, MATLAB 5G Toolbox, Model compression, Edge computing, System-level evaluation, Machine learning pipelines, Attention-based CNN, F1-macro scoring, ArrayRecord, Grain, Python, Selenium, MySQL, Django, BERT, DistilBERT, NLP

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.

Eman Chaudhary
Cell: 03354468887 |  Email: eman.abc05@gmail.com
LinkedIn: https://www.linkedin.com/in/eman-chaudhary/
Address: Sector G-14 , Islamabad , Pakistan
PROFESSIONAL PROFILE
A Software Engineering undergraduate with a strong research orientation. I work at the intersection of generative AI
and resource-constrained deployment. My experience spans diffusion-based generative models and ultra-lightweight
deep learning architectures optimized for real-time inference on edge hardware. I am particularly interested in
trustworthy and efficient AI systems.
EDUCATION
Bachelor of Engineering in Software Engineering
School of Electrical Engineering and Computer Science , Islamabad (2026)
INTERNSHIP EXPERIENCE
MITACS GLOBALINK RESEARCH INTERN – Brock University, Canada
01-Jun-2026 - 31-Aug-2026
Engineered an automated Root Cause Analysis (RCA) pipeline using Code Llama and RAG frameworks to parse high-velocity
unstructured logs, overcoming context window limitations for long-sequence traces. Fine-tuned domain-speciﬁc transformer models
for anomaly detection and integrated them with OpenTelemetry and Logstash to enhance distributed system observability. Developed
a prototype AI assistant capable of interpreting complex system failures and generating human-readable debugging explanations.
ML INTERN - EMBEDAIOT LAB, SINES, NUST
01-Jun-2025 - 01-Sep-2025
Optimized ultra-lightweight CNNs for 5G channel estimation, reducing model size to ~163 trainable parameters with INT8
quantization for sub-millisecond inference on NVIDIA Jetson. Developed lightweight CNN architectures (101–745 parameters) for
pilot-based SISO OFDM channel estimation, trained on 1024 synthesized CSI grids from MATLAB 5G Toolbox Achieved NMSE as
low as −11.9 dB on MATLAB 5G Toolbox data while reducing inference to < 1 ms on Jetson Nano and Xavier using model
compression techniques Focused on integrating ML models for channel estimation into software systems under operational
constraints. It enhanced my skills in edge computing, model deployment, and system-level evaluation, directly connecting with
EDISS courses on Data Intensive Engineering, Edge Computing for ML, and Software Quality Engineering.
AI/ML Contributor - NASA Open Science Data Repository (OSDR), United States (Remote)
01-Aug-2025 - 30-Nov-2025
Contributed to the development of machine learning pipelines for classifying radiation-induced DNA damage in microscopy images
from the OSD-366 dataset. An attention-based CNN architecture was implemented, and evaluation metrics were optimized by
transitioning from accuracy to F1-macro scoring, addressing severe class imbalance. A new data-loading pipeline, built with
ArrayRecord and Grain, minimized bottlenecks from remote ﬁle access, improving eﬃciency in shared environments like Google
Colab. The project emphasized the importance of system-level engineering in deploying machine learning models within operational
workflows, highlighting how evaluation and data pipelines shape system performance and conclusions.
SOFTWARE DEVELOPMENT INTERN – APNA-WIFI (NSTP, NUST)
01-Jun-2024 - 31-Aug-2024
Built scalable Python web scraping pipelines (Selenium) and automated data ingestion into MySQL databases, enabling continuous
course metadata collection Developed a Django-based course recommender with BERT and DistilBERT for semantic similarity
matching, demonstrating the feasibility of applied NLP for ed-tech solutions
Research Intern - SMART LABS

AI enrichment

Eman Chaudhary is a Software Engineering undergraduate specializing in generative AI, edge computing, and model optimization for resource-constrained environments. Their experience includes developing lightweight deep learning architectures for 5G channel estimation and building automated root cause analysis pipelines using LLMs and RAG frameworks.

Skills (AI)

["Generative AI", "Edge Computing", "Deep Learning", "Model Quantization", "Python", "Django", "RAG", "LLMs", "Computer Vision", "Data Pipelines", "OpenTelemetry", "PyTorch", "TensorFlow"]

Status: ai_done

Provenance

Source file: SEECS - Software Engineering-2026(1).pdf
From job #260 page 7
Created: 1778138736