ML Engineer - Generative AI & Data Science

Nora Alaoui, M.S.

Machine Learning Engineer - Generative AI - Data Science

****@*******.*** 202-***-****

GitHub LinkedIn Personal Website

EDUCATION

NORTHWESTERN UNIVERSITY M.S.

Evanston, IL

MS Data Science, concentration AI

GPA: 3.7

Coursework: Practical Machine Learning, Computer Vision, Image processing, Artificial Intelligence, Machine Learning, Complex Data Storage, Data Structures, Generative AI, Databases and Warehousing, Natural Language processing, Deep Learning, AI/ML Ethics, SQL GEORGE MASON UNIVERSITY B.S.

Fairfax, VA

B.S. Psychology, concentration Neuroscience

Major GPA: 3.9/ Overall: 3.2

Coursework: Technology and Psychology (How AI will affect our Daily Lives), I/O Psychology, AI/ML Ethics, Human-Computer Interaction (HCI), Technology in Mental Health, Neuropathy, Virtual Reality (VR) and Psychology

SKILLS

Programming Languages:Python

Skills: Generative AI (Diffusion, GANs) · LLMs/RAG (Hugging Face) · Python · PyTorch · TensorFlow · Computer Vision (OpenCV/YOLO) · MLOps/LLMOps (Docker, CI/CD, FastAPI) · Cloud ML (AWS SageMaker; Vertex AI/Azure ML) · Model Evaluation (FID, LPIPS) · Monitoring/Drift · SQL · HIPAA-aware workflows · Expertise in Gen AI

PROJECTS & STARTUP & EXPERIENCE

Machine Learning & Generative AI Engineer

Prosyn June 2024 -Present

● Spearheaded the design and deployment of advanced diffusion models (DDPM, Latent Diffusion) using PyTorch and AWS SageMaker, reducing synthetic medical imaging generation time by 40%

● Architected and fine-tuned StyleGAN3 and Stable Diffusion frameworks with transformer-based architectures and perceptual loss metrics (FID, LPIPS), achieving a 30% improvement in image fidelity over baseline models.

● Collaborated in a team setting, a cross-functional team of ML engineers and data scientists to deploy production-grade AI imaging solutions, implementing noise scheduling and gradient clipping in Denoising Diffusion Probabilistic Models (DDPMs), improving training stability by 20% and lowering GPU utilization.

● Enhanced ProSyn’s NLU pipeline by integrating multimodal generative AI and transformer-based LLMs for medical text-to-protein generation, boosting semantic accuracy, interpretability, and biological insight in clinical contexts.

Machine learning Researcher & Developer

Virginia Tech Arlington Innovation Center (AIC), Arlington, VA December 2021 - June 2022

● Designed state of the art computer vision pipelines for lung cancer detection, using TensorFlow based CNNs and transfer learning.

Nora Alaoui, M.S.

Machine Learning Engineer - Generative AI - Data Science ****@*******.*** 202-***-****

GitHub LinkedIn Personal Website

● Engineered volumetric preprocessing workflows in 3D Slicer, including data augmentation, which reduced model training time.Conducted advanced statistical analyses to uncover trends in lung cancer imaging.

● Researched initiatives that evaluated the future impact of AI on cancer treatment workflows, focusing on the operability of tumors and potential future workflow optimizations. Machine learning Scientist Intern

mdlogix, Towson, MD April 2021 - May 2022

● Leveraged statistical modeling & ML (feature selection, regression) to build a predictive framework that forecast over 80 suicide events, enabling data driven interventions.

● Deployed MILO to model adolescent mental health outcomes, predicting 130+ crises and supporting timely, targeted interventions.

● Migrated analytics from AWS to Oracle DBMS, boosting processing speed by ~25 % and aligning security with HIPAA to cut vulnerabilities by ~30 %.

● Optimized Oracle DBMS for performance, security, and scalability; architected automated ETL pipelines that slashed manual data prep time by 40 % and ensured high data integrity. SELECTED PROJECTS

● Project Lead on Object Detection for Autonomous Driving with Edge Computing (2024) Northwestern University

Led the development of a real-time object detection system deployed on edge devices, ensuring model accuracy and monitoring data health in production. Led the tailoring of model architecture to fully utilize the processing capabilities of the target devices, utilizing GPU acceleration and specialized neural processing units.

● Project Lead on Capstone - Facial Recognition in Augmented Reality (2024) Northwestern University

The project achieved significant accuracy and real-time performance, setting a precedent for future applications in various domains. Implemented a 26-layer ResNet model for facial recognition, achieving 90% accuracy through rigorous statistical analysis of training data.

● Object Detection App (2024) Northwestern University Developed an iOS application leveraging the Cloud Vision API and RAG (Retrieval-Augmented Generation) pipelines to accurately identify and contextualize various objects, integrating seamless functionality optimized for the iOS platform.

● Bridging the Translation Chasm with Automated Machine Learning for mdlogix (2021) Washington, D.C.

Poster presentation of a research article presented at the Conference on AI at Georgetown University. For mdlogix.

● Combat Related PTSD and treatment through the Oculus Quest, at Abertay University (2020) Abertay, Scotland

Presentation on Neuropathy and novel technologies used for treatment.

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