Noze
Hafizur Rahman has worked at multiple companies in various technical roles.
In 2019, they joined Sherweb as a Technical Analyst. In this role, they analyzed and resolved technical issues related to Office 365 products and actively documented solutions. Hafizur also collaborated with a team of 15 people and participated in discussions.
In 2020, Hafizur joined Evidence Partners as a Data Scientist. Hafizur developed a web-scraping tool using Python to extract biomedical research data from PubMed. Hafizur also built multi-label and binary text classification models using various algorithms and achieved a performance of 92%.
Also in 2020, they joined Promark Electronics Inc. as a Machine Learning Developer. Here, they designed and maintained end-to-end autonomous machine learning and computer vision deployment system architecture and infrastructure on an on-premise Ubuntu Server. Hafizur also developed computer vision applications using various algorithms and frameworks, such as YOLO, RCNN, U-NET, and CNN. Additionally, they performed ETL processes to collect data from multiple internal sources and stored them in an MS-SQL database.
Most recently, in 2022, Hafizur joined Noze as a Machine Learning Engineer (MLOps). Further details about this role are not provided.
Hafizur Rahman's education history includes a Master's degree in Applied Mathematics from Concordia University, which they pursued from 2017 to 2019. Prior to that, they completed a Master's degree in Mathematics from Northern Arizona University from 2015 to 2016, and a Bachelor's degree in Mathematics from Pabna University of Science and Technology between 2009 and 2014.
Additionally, Hafizur has obtained several certifications in various fields. Hafizur completed the Deep Learning Specialization, Fundamentals of Scalable Data Science, IBM Data Science Professional Certificate, and Machine Learning Specialization from Coursera in 2019. In the same year, they also completed the R Programming A-Z™: R For Data Science course from Udemy.
In 2019, Hafizur obtained certifications in Big Data Fundamentals via PySpark and Feature Engineering with PySpark from DataCamp. The following year, in 2020, they pursued a Nano Degree in Data Streaming from Udacity.
Furthermore, Hafizur has also acquired additional knowledge and skills through the Intermediate Python course from Treehouse, although the specific details regarding the month and year of completion are unavailable.
Noze
Noze (formerly known as Stratuscent) is the global leader in digital smell detection. Using technology built on a foundation of NASA-patented innovations deployed on the International Space Station, Noze created the world’s first and only digital nose that can detect, identify and interpret odors in everyday environments with incredible accuracy.This creates groundbreaking opportunities across multiple sectors and industries, where digital olfaction has high-value applications ranging from medical diagnostics and food manufacture & storage to public safety, air intelligence, and smart consumer electronics.Our platform is an end-to-end solution that combines a neural network-based AI engine with a cutting-edge digital olfaction sensor based on exclusive NASA patents. The sensor decodes the environment as a whole, isolating signal from background scent noise and creating a unique ‘scentprint’ for each individual chemical (VOC), chemical mixture, or smell. The AI engine decodes data from the sensor to identify exactly what is in the air, in what quantity, and how it’s evolving. This continuous real-time analysis enables scent recognition in under a second with incredible accuracy. And each time the AI engine learns a new smell, every sensor in the world learns it too. Our APIs can be seamlessly integrated with new or existing user interfaces (UIs), enabling real-time access to data, analytics, and alerts. Meanwhile, a secure and encrypted communication protocol protects all the data flows within our platform and its ecosystem.