Machine Learning Engineer

Nora Alaoui, M.S.

Machine Learning Engineer - Generative AI - MLOps

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

GitHub LinkedIn

An experienced Machine Learning Engineer and Data Science Engineer with over 5 years of experience in architecting, developing, and deploying state-of-the-art AI and deep learning solutions across domains such as healthcare, autonomous systems, and logistics. Working with advanced tools and techniques, including TensorFlow, PyTorch, Python, SQL, AWS, and Databricks, as well as frameworks for data pipeline automation like Snowflake and Airflow.

Senior Machine Learning & Generative AI Engineer

Prosyn June 2024 -Present

● Spearheaded the design and deployment of advanced diffusion models (e.g., DDPM, Latent Diffusion) using PyTorch and AWS SageMaker, reducing synthetic medical imaging data creation time by 40%, directly supporting faster drug discovery.

● Architected and fine-tuned StyleGAN-3 and Stable Diffusion models, leveraging transformer-based architectures and perceptual loss metrics (e.g., FID, LPIPS) to achieve a 30% improvement in image quality over baseline models.

● Led a team of engineers, data scientists to deploy AI imaging solutions. Implemented noise scheduling and gradient clipping techniques in Denoising Probabilistic Diffusion Models, improving training stability by 20% and reducing GPU training cost.

Skills: Generative AI · Diffusion Models (DDPM, Latent Diffusion) · GANs (StyleGAN-3, Stable Diffusion) · PyTorch · TensorFlow · AWS SageMaker · Python · Biopython · Computer Vision · Medical Imaging (DICOM, NIfTI) · Transformer Architectures · Perceptual Loss Metrics (FID, LPIPS)

· HIPAA Compliance · Data Science · Deep Learning · Machine Learning · Synthetic Data Generation · Model Fine-Tuning · GPU Optimization · Cross-functional Leadership Machine learning/Artificial Intelligence Researcher & Developer Virginia Tech Arlington Innovation Center (AIC), Arlington, VA December 2021 - June 2022

● Developed state-of-the-art Computer Vision systems utilizing Deep Learning and Convolutional Neural Networks (CNNs) for lung cancer detection and mass classification. Leveraged TensorFlow for neural architecture design, data augmentation, and hyperparameter optimization, combined with volumetric data preprocessing in 3D Slicer. Achieved 80% accuracy by integrating transfer learning techniques and fine-tuning advanced model architectures, driving innovation in AI-powered medical diagnostics.

● Preprocessed high-dimensional, complex medical datasets utilizing advanced data wrangling techniques and feature engineering to detect abnormalities and ensure data integrity in cancer research models. Implemented data quality monitoring pipelines leveraging AI-driven anomaly detection methods and managed the retrieval of large-scale datasets from the Cancer Imaging Archive, optimizing them for deep learning workflows. This process improved model training efficiency by 30% and enhanced detection accuracy for early-stage cancer.

● Applied advanced statistical methods to explore trends in lung cancer detection, improving early diagnosis rates.

● Conducted research to improve the healthcare industry and assess the future state of cancer treatment when dealing with the in-operability of tumors

Nora Alaoui, M.S.

Machine Learning Engineer - Generative AI - MLOps

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

GitHub LinkedIn

Skills: Machine Learning · Python (Programming Language) · Computer Vision · Tensorflow · OpenCV · Docker · Kubernetes · YOLO (Ultralytics) ·AWS S3 · TensorFlow · Data Science · Deep Learning

Machine learning Scientist

mdlogix, Towson, MD April 2021 - May 2022

● Leveraged advanced statistical modeling and predictive analytics to analyze mental health risk factors, employing machine learning techniques such as feature selection and regression analysis to identify critical patterns. Developed an AI-powered predictive framework that successfully forecasted over 80 suicide events, enabling data-driven intervention strategies and improving crisis response outcomes.

● Utilized state-of-the-art AI technology, Machine Intelligence Learning Optimizer (MILO), to build predictive models for forecasting adolescent outcomes during mental health emergencies. Applied machine learning algorithms and dynamic risk assessment techniques to predict over 130 unique mental health crises, enabling timely and targeted interventions.

● Led the transition of data from AWS to Oracle DBMS, improving data processing speeds and facilitating large-scale data analysis for real-time decision-making.Configured and optimized. Collaborated with SaaS teams to align security protocols with HIPAA compliance, reducing system vulnerabilities.

● Optimized Oracle DBMS for enhanced performance, robust security, and seamless scalability, effectively managing large-scale datasets for AI-driven analytics. Architected and implemented automated data pipelines leveraging ETL processes to manage and query complex datasets, ensuring high data accuracy and integrity for research systems. This improvement increased usability and streamlined workflows, enabling more efficient data-driven decision-making Skills: Machine Learning · Python (Programming Language) · DBMS · TensorFlow · Data Science · SQL · Advanced Statistical Modeling · R/R Shiny · Cuda · Elastic Search Data Specialist Intern

eHMISSA, Mclean VA August 2018 - December 2020

● Designed and optimized relational databases using SQL to store and manage patient data, ensuring data integrity, security, and compliance with HIPAA regulations.

● Utilized data visualization tools (e.g., Tableau, Power BI) to create interactive dashboards for healthcare providers, enabling real-time monitoring of patient progress and treatment efficacy.

● Applied statistical analysis and predictive modeling to identify trends in wound healing rates, contributing to a 20% improvement in treatment protocols.

● Conducted data quality audits and implemented corrective measures to improve data accuracy and reliability, resulting in a 15% increase in system usability.

● Documented system architecture, workflows, and data processes to maintain transparency and support future scalability.

Skills: Python (Programming Language) · Power BI · Pandas · TensorFlow · Java/Spring Boot · MySQL · AWS SageMaker · Statistics · SQL · Oracle DBMS · Data Modeling · Apache NiFi · Talend · Informatica · Tableau · R · SAS · HIPAA Compliance · EHR Integration · Data Security · Cross-functional Collaboration · Stakeholder Management · Requirement Gathering · User Training · Technical Documentation

STARTUP & ACHIEVEMENTS

Nora Alaoui, M.S.

Machine Learning Engineer - Generative AI - MLOps

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

GitHub LinkedIn

● 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 edge 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 to accurately identify 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. SKILLS

● Programming Languages: Proficient in Python, C++, JavaScript, HTML, Go, Swift, R, SQL, and Bash; experienced in Unix/Linux environments for efficient system-level programming and scripting.

● Frameworks/Libraries: Expertise in TensorFlow, PyTorch, Pandas, Flask, and PySpark; adept at leveraging AWS for cloud computing, Oracle DBMS for robust database management, and Postgres/NoSQL for scalable data storage solutions. EDUCATION

NORTHWESTERN UNIVERSITY

Evanston, IL

MS Data Science, concentration AI

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

Fairfax, VA

B.S. Psychology, concentration Neuroscience

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

Contact this candidate