Omnia Consulting

• Led advanced data analytics projects, applying statistical modeling, machine learning, and data mining techniques.
• Architected and deployed scalable data pipelines for comprehensive data processing.
• Analyzed complex datasets to identify trends and insights, bolstering predictive modeling and statistical analysis.
• Designed, trained, and deployed ML models to address pressing business challenges.
• Executed A/B tests optimizing model performance metrics such as accuracy and precision.
• Developed data visualization dashboards, enabling stakeholders to make informed, data-driven decisions.
• Stayed updated with current data science trends and integrated best practices in analytics

Senior Data Scientist

Drowzee

• Led the product’s journey from conceptualization to market launch.
• Developed predictive models for medical datasets, including Time Series Forecasting and advanced Image processing using Python
and TensorFlow.
• Processed and prepared large datasets for effective data mining.
• Tackled challenges posed by imbalanced datasets to extract meaningful insights.
• Monitored ML models in production environments, ensuring optimal performance and reliability.
• Collaborated with cross-functional teams and managed relationships with external hardware contractors.
• Pioneered a real-time Neurofeedback protocol, enhancing sleep efficiency by 20% for 85% of users in 6 weeks.
• Advocated the company’s tech advantages in investor pitches, aiding in securing significant investments.
• Played a pivotal role in recruiting, from candidate screening to onboarding.

Data Scientist & Computational Neuroscientist 

Inria

• Worked under the mentorship of Dr. Fabien Lotte, contributing to the POTIOC team’s research initiatives.
• Designed a computational model to predict MI-BCIs user performance from brief 3-minute baseline recordings, achieving a 5% MAE.
This was achieved by integrating neurophysiological predictors with advanced machine-learning techniques.
• Devised and refined algorithms that predicted and enhanced the quality of subject training based on EEG data processing.
• Conducted rigorous experiments to corroborate the significance of identified neurophysiological predictors.

Data Scientist

My Size (NASDAQ: MYSZ)

• Pioneered the integration of Machine and Deep Learning algorithms, significantly enhancing the algorithm’s accuracy.
• Devised and deployed a deep learning network, reducing user errors and mitigating sensor artifacts.
• Spearheaded the transition of algorithm development from MATLAB to Python.
• Designed, implemented, and validated innovative signal processing algorithms; ensured real-world performance through extensive
testing.
• Conducted comprehensive research in data analytics, sensors, and signal processing, contributing to product improvement.
• Refined and optimized algorithms based on real-world data, simulations, and implementation considerations.
• Collaborated closely with app development teams to ensure the effective real-life performance of implemented algorithms.

Signal Processing Algorithm Developer

Holon Institute of Technology

• Contributed to research initiatives in fNIRS technology under the guidance of Dr. Michal Balberg.
• Processed and analyzed physiological signals using MATLAB, enhancing data accuracy and insights.
• Performed advanced statistical analyses on experimental results leveraging tools like SPM and GLM, as well as various statistical
tests.

Research Assistant

At Omnia Consulting, my role as a Senior Data Scientist is underpinned by a robust education in Computational Neuroscience from ESPCI Paris - PSL. We architect scalable data pipelines and deploy machine learning models that empower stakeholders to make data-driven decisions, enhancing business outcomes. Our team's dedication to integrating the latest trends in data science ensures that our practices remain at the cutting edge.

My tenure at Drowzee, where we developed Neurofeedback protocols to improve sleep efficiency, exemplifies my commitment to applying data science for tangible health benefits. The competencies I honed there, such as predictive modeling and A/B testing, are vital in my current projects, where we analyze complex datasets and refine ML model performance to address challenging business demands.

Eidan Tzdaka

Norwegian Meteorological Institute | Meteorologisk institutt

As an AI engineer and researcher, I focus on developing data-driven AI and machine learning models for medium- and long-range weather forecasting.

I work closely with the European Centre for Medium-Range Weather Forecasts (ECMWF), a global leader in numerical weather prediction. Our team recently developed BRIS, an AI-based forecasting system, built on the stretched-grid concept and is a Graph Transformer based architecture. BRIS is implemented using the Anemoi framework, an open-source platform designed and maintained by ECMWF and its member states—with significant contributions from myself and my institute.

I am also involved in the Destination Earth (DestinE) initiative, specifically within the Demand Extremes (DEODE) project. There, I have developed a multi-domain architecture that trains stretched-grid data-driven models across multiple high-resolution domains simultaneously. This system uses a semi-dynamic graph switch and dynamic encoder–processor–decoder components, aiming to create a hectometric-scale, AI-based digital twin for advanced weather forecasting.

BRIS arXiv preprint: https://arxiv.org/abs/2409.02891

Machine Learning Researcher

Sopra Steria

Working as Data scientist, data and AI engineer.
Developing analysis, etl pipelines, plattforms and AI architectures.

Experience with:
Spark, azure, palantir, powerbi, AI development and data engineering/ETL processes

University of Oslo

Assistant in M.Sc. and Ph.D course IN5400/IN9400, deep learning for image analysis.

Teacher Assistant

ENT3R

Teaching and helping high school students with higher and lower level mathematics, physics and chemistry.

Mentor

MAT-IN1105: Mathematics, programing and modeling. This course teaches the students how to use numerical methods and programing to solve problems in mathematics and physical sciences. This course is a first year bachelor course.

FYS3150/4150: Computational physics I. First year master and last year bachelors course in numerical modeling and computational physics. 

The goal of this course is to learn the student to use different type of numerical methods such as Monte Carlo simulations to solve physicals problems. The student also learns how to optimize and paralelize (using MPI or openMP) codes in C, C++ or ForTran for best possible result and speed.

Institute of Theoretical Astrophysics, University of Oslo

Project description:

The radiative transfer code RH is used
to calculate the predicted spectra from 3D magneto-hydrodynamic simulations of the solar atmosphere. RH was used to calculate many of the spectral lines observed by the IRIS mission, giving fundamental insights about how they are formed.

 While most of that analysis focused
on emergent spectra, the study of inclined rays is still uncharted territory. This a rather important point as a good amount of the
observations are at the solar limb or close to it.

Supervisor: Associate Prof. Thiago Pereira

Research assistant

I’m an AI/ML Engineer with a background in astrophysics (B.Sc) and computational science with a focus on biomedical computing and imaging (M.Sc, University of Oslo). My expertise spans machine learning, deep learning, image processing, and numerical modeling.

Previously, have I worked as AI/ML consultant, but currently I work as an AI/ML engineer and researcher at the Norwegian Meteorological Institute. I develop AI-driven solutions for analyzing complex weather and climate data. More specifically engineering a graph-transformer based stretched-grid architecture (global and regional model) trained on reanalysis and benchmark against current NWP models. I also collaborate with ECMWF, pioneers in medium weather forecasting and also contribute to open source package called AnemoI which is an ai framework for creating data-driven climate and weather forecasting models.

I also work extensively with high-performance computing (HPC/super-computer) and parallel GPU programming, using systems like (CSC) LUMI and (EUROHPC)Leonardo to scale scientific models and ML workflows.

My main programming language is Python, complemented by experience in C, C++, and Bash. I have also a lot of experience with RCCL,NCCL, CUDA  and ROCM, and cloud platforms such as AWS and Azure. I’m passionate about applying AI to real-world scientific and industrial challenges across disciplines, optimize models (speed,cost, memory,…) and deploy them in realistic hardware environments.

GitHub: https://github.com/einrone
Stretched-grid paper: https://arxiv.org/abs/2409.02891


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