Sam F. Email & Phone Number

Vancouver, Bc, Ca

San Diego, California, United States

- Design Machine Learning Pipelines: Constructed innovative ML pipelines using object-oriented programming in Python, implemented CI/CD processes using DVC and GitHub Actions, and scaled training/inference pipelines using cloud computing/storage solutions.- Perform MLOps Practices: Implemented ML best practices in model training and public clouds (AWS, Azure, GCP) using distributed methods. Set up automated error analysis, version control (DVC), data processing, and CD/CI Pipelines (Git Action). Containerized solutions using Docker and deployed to production using AWS EC2, ECS, ALB, SQS, and edge locations, improving scalability, reliability, and efficiency.- Machine Learning Modeling: Developed advanced ML models using Transformers. Led the training, validation, and fine-tuning of models using innovative libraries such as PyTorch, HuggingFace, SMP, and Torchvision. - NLP and Large Language Model (LLM) Modeling: Successfully implemented LLMs (BERT, GPT, BLOOM) to train problem-specific generative models. - Problem-Specific Model Customization: Adapted neural network architectures (Autoencoders, Attention Mechanisms, Transformers, CNNs, UNET) for NLP, computer vision, and time series analysis.- Expertise in using Apache Spark (RDD, DataFrame/SQL API). Built data ingestion/transform pipelines using AWS Glue ETL and EMR, scripted using Spark and Python. Designed distributed clusters for big data ingestion of up to 10 terabytes per batch and over 500M records.- Delivered web systems using React.js (JavaScript), Next.js (TypeScript), Django (Python), Node.js, Steamlit, and AWS Beanstalk. Designed APIs based on REST endpoints and GraphQL. Containerized applications and Machine learning services using Docker, AWS ECR, ECS, and EC2.

Montreal, Quebec, Canada

Research Assistant:- Handling High-Dimensional Data: Extracted patterns from high-dimensional data, achieving a 25% improvement in pattern recognition accuracy through dimensionality reduction and specialized network architectures. Successfully collected and analyzed data, resulting in a 30% reduction in processing time through optimized distributed methods for parallelism using Apache Spark and supercomputers on +2000 cores per job.- Implement algorithms and data analysis for aerospace and combustion technologies using C++, Python, PyTorch, Numpy, Panda, and Matplotlib. - Simulation setup to collect big data of flame characteristics using supercomputers by parallel coding in C++. Managed to define new combustion stabilization phenomena leading to the reduction of emissions in jet engines.- Main author of a paper published in Combustion and Flames journal, titled, 'Ignition and flame stabilization of n-dodecane turbulent premixed flames under Spray A thermochemical conditions' (https://www.sciencedirect.com/science/article/pii/S0010218022001523).- Attend conferences and seminars to present team projects and network with well-known scientists and researchers.Teaching assistantship:- Mentor students through lectures, daily communication, and managing project progress.- Courses: "Programming and Data Analysis with Python", "Introduction to Combustion".

Ottawa, Canada Area

- Research Assistant: Heavy hydrocarbon combustion flame analysis using Matlab and C++.- Teaching Assistant: "Programming and Data Analysis with Python" and "Introduction to Data in Combustion".- Paper publications on Energy and Combustion in collaboration with NSERC and NREL- Seminar speaker, University of Ottawa, Winter 2021- Speaker at APS physics international conference DFD 2020 Chicago, Winter 2020: https://meetings.aps.org/Meeting/DFD20/Session/P02.3

Applied Computer Science Certificate, 4

- Web Programming with Python and JavaScript - SQL and Postgres - Machine learning using Pandas, Numpy, PyTorch, SciKit-learn, Matplotlib

Master Of Science - Ms, Mechanical Engineering, 3.9

- Research Assistant - Teaching Assistant - Awarded full study excellence scholarship - Earned GPA: 3.9/4 - Seminar speaker - Numerical.

Bachelor Of Applied Science - Basc, Major In Mechanical Engineering With Honors, Minor In Computer Science, 3.8

- Teaching assistantship: “Programming with Python”, “MATLAB Workshop”.