MHP – A Porsche Company

- Developing multimodal Retrieval Augmented Generation applications on company data with Large Language Models
- Building an LLM-based Quality Evaluator and ML-based Duplicate Detection Method for Software Bug Tickets

Data Scientist

Bosch Power Tools

- Implementation and evaluation of probabilistic deep learning models for demand forecasting on AWS SageMaker

Master Student Data Science

- Development of a tool for automated data analysis and visualization in SAP Analytics Cloud
- Implementation of time series forecasting and outlier detection models

Working Student Data Science

Mercedes-Benz AG

- Speeding up the assembly process by using machine learning to predict throughput times and errors on Azure Databricks
- Automation of graph visualization for the optimization of assembly analysis

Fraunhofer IPA

- Implementation and evaluation of a generative deep learning model for image data augmentation for cutting edge patterns
- Implementation of scientific papers in Python

Bachelor Student Deep Learning

MAHLE

- Analysis of engine data and application of machine learning for prediction in piston temperature measurement

Intern Data Analysis & IT Project Management

I make learning Data Science effortless for you | Data Science @ Bosch

Hi 👋🏻
I'm Banias, Data Scientist at MHP.

Applying new technologies and methods in Data Science is my passion.

Through my experience and continual learning, I've gained valuable insights and tips that I love sharing with Data Scientists, Machine Learning Engineers and builders like you.

If you're looking for practical advice, helpful resources, and articles on the latest trends or best practices in the field, you're in the right place.

Let's connect and stay updated on the exciting world of Data Science together. Feel free to reach out, read my articles, and let's grow our knowledge and network. :)


Note: My views are my own and don‘t represent my employer.

Banias Baabe

Exponent

Senior Engineer

Senior Associate

May is a mechanical engineer in Exponent’s Thermal Science Practice with a background in combustion, heat transfer, thermodynamics, and fluid dynamics.  She specializes in multidimensional modeling and computational analysis of complex systems including industrial equipment, combustion system and fluid & heat transfer equipment with focus on failure analysis and fire and explosion investigations.

Associate, Thermal Science

Manager

Purdue University

Research Assistant

Cummins Inc.

Combustion/Performance/Emissions Engineer Intern

Cummins, Inc

Heavy Duty Product Planning Intern

Cummins Power Generation

G-Drive Controls Intern

Cummins Engine Company

Design Engineering Intern

May is a mechanical engineer in Exponent's Thermal Science Practice with a background in combustion, heat transfer, thermodynamics, and fluid dynamics. She specializes in multidimensional modeling and computational analysis of complex systems including consumer electronics, biomedical devices, industrial equipment, combustion systems and fluid & heat transfer equipment with focus on failure analysis and fire and explosion investigations. 

May has experience on consequence analysis associated with flammable releases and vapor cloud explosions in Oil & Gas facilities. She routinely performs facility evaluations for permitting and planning purposes. She specializes in computation modeling, using computational tools including FLACS, StarCCM+, CFX, and ANSYS Fluent. 

May has extensive experience in turbulent diffusion flames, soot formation for direct injection engine applications and performance and emission evaluations of diesel engines in test cells. She is proficient at coding in Fortran, C, Python, and MATLAB as well as parallelizing code with MPI (Message Passing Interface) and OpenMP. May regularly utilizes CAD software (Solidworks, Catia, ProE, Spaceclaim). 

She has extensive experience in modeling and testing of wearables and consumer electronics for performance and risk assessment of burn injury hazards by radiation, convection, and conduction. She is also experienced in the analysis and investigation of burn injuries by flame, scald, extreme environment, and electronics as well as frostbite.

May has performed engineering analysis using computational fluid dynamics (CFD) for blood flow, blood trauma, and fluid path analysis in medical devices such as heart pumps, specialized catheters, IV infusion devices, and blood oxygenators.

May applies her engineering expertise to intellectual property disputes. She has worked on several aspects of intellectual property litigation such as validity/invalidity, infringement/non-infringement, Markman hearings, and trade secrets. 

May performed her PhD research at Purdue University where she conducted multidimensional modeling of turbulent diffusion flames for diesel engine applications. She assessed the effect of exhaust gas recirculation, combustion chamber temperature, and injection pressure on fuel-air mixing and soot formation under direct injection engine conditions. May’s work focused on evaluating and developing soot models that were experimentally validated across several regimes and fuels types. Additionally, she has experience in evaluating performance and emissions of diesel engines in test cells.

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Banias Baabe

May Yen

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