Taymaz Ghaneh

Looking for a job

Graduation Internship in Data Science

Data Scientist at Boskalis who developed an LSTM/CRF pipeline to classify vessel activity from irregular AIS data.

NetherlandsGraduation Internship in Data Science at Boskalis

Experience

Graduation Internship in Data Science
Boskalis
Developed a reproducible ML pipeline (LSTM/CRF) for vessel-activity classification using irregular AIS data, including data-quality quantification, model evaluation, reliability assessment, and error analysis. Achieved statistically significant improvements across two operational metrics and improved generalizability, communicating findings to stakeholders.
PythonDatabricksSpark SQLTensorFlowMLFlowLSTMCRFMachine LearningData QualityModel Evaluation

University of Tabriz
Ph.D. in Gravitational Physics from the University of Tabriz.
Leiden University
MSc in Statistics & Data Science from Leiden University, with experience in dense time-series modeling and additional training in longitudinal modeling, causal inference, and Bayesian modeling.

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Looking for a job

Graduation Internship in Data Science

Data Scientist at Boskalis who developed an LSTM/CRF pipeline to classify vessel activity from irregular AIS data.

NetherlandsGraduation Internship in Data Science at Boskalis

Graduation Internship in Data Science
Boskalis
Developed a reproducible ML pipeline (LSTM/CRF) for vessel-activity classification using irregular AIS data, including data-quality quantification, model evaluation, reliability assessment, and error analysis. Achieved statistically significant improvements across two operational metrics and improved generalizability, communicating findings to stakeholders.
PythonDatabricksSpark SQLTensorFlowMLFlowLSTMCRFMachine LearningData QualityModel Evaluation

University of Tabriz
Ph.D. in Gravitational Physics from the University of Tabriz.
Leiden University
MSc in Statistics & Data Science from Leiden University, with experience in dense time-series modeling and additional training in longitudinal modeling, causal inference, and Bayesian modeling.