Data Scientist and Data Engineer with expertise in machine learning, cloud-based data solutions, and scalable ETL pipelines. Skilled in predictive modeling, statistical analysis, and automation workflows. Experienced in Azure Databricks, Power BI, DAX, and SQL-based analytics. Additionally, proficient in 3D design, 3D printing, and rapid prototyping for developing technical solutions.
English: Fluent (TOEFL: 105/120)
Finnish: Basic
- Developed scalable data pipelines and ETL workflows using Python, Apache Airflow, and SQL, improving processing efficiency by 30%.
- Implemented AWS cloud-based solutions, optimizing data storage, processing, and model deployment.
- Automated data validation workflows, reducing manual intervention by 60%.
- Built predictive models for analyzing time-series data, improving forecasting accuracy.
- Developed machine learning algorithms for anomaly detection and time-series forecasting using PyTorch and TensorFlow.
- Conducted feature extraction and preprocessing for large-scale datasets, reducing error rates by 10%.
- Published research in IEEE JBHI and Computing in Cardiology, contributing to AI-driven medical advancements.
- Designed 3D models and CAD prototypes for research and product development.
01/2024 - 05/2024
University of Turku
- ECG and PPG circuit simulation with LT Spice software and hands-on lab implementation.
- The course discusses about basics of different sensor types and their measurement pricinples, measurement of different medically relevant signals and embedded medical systems.
08/2023 - Present
University of Turku
Thesis: Unobtrusive Monitoring for Cardiovascular Disease using ML.
Supervisors: Assistant Prof. Matti Kaisti, Assistant Prof. Antti Airola
Core Focus: Scalable data pipelines, multimodal clinical data analysis, and AI-driven healthcare solutions.
09/2018 - 02/2022
Iran University of Science and Technology
Thesis: Despeckling of Medical Ultrasound Images using Deep Learning
Supervisor: Associate Professor. Hamid Behnam
GPA: 4/4
Core Focus: Medical image processing, deep learning, and computer vision techniques with applications in diagnostics.
Relevant Coursework: Deep Learning, Medical Imaging Processing
09/2013 - 08/2018
Bu-Ali Sina University
Thesis: Application of FibroScan® device in liver diseases
Supervisor: Professor. Soheil Ganjefar
GPA: 3.5/4
Core Focus: Signal processing, embedded systems, and microcontroller programming, Robotics.
Relevant Coursework: Control Systems, Electronic Circuits, Digital Systems, Embedded Systems, Microprocessors.
- S. Seifizarei et al, “Continuous Radar-based Heart Rate Monitoring using Autocorrelation-based Algorithm in Intensive Care Unit.”, IEEE Journal of Biomedical and Health Informatics
- S. Seifizarei et al, “Evaluating Piezoelectric Ballistocardiography for Post-Surgical Heart Rate Monitoring.”, Computing in Cardiology 2024
Address
Univ. of Turku, Turku, Finland
sepehr.seifizarei@utu.fi