Sangita Conjeevaram Viswanathan
Köln
Summary
A detail-oriented researcher with a background in data analysis and electrochemistry. Experienced in designing experiments and interpreting complex datasets in electrochemical organic reactions, leading to a strong focus on data-driven problem-solving and analytical optimization. Currently pursuing a second master's in Data Analytics and Decision Science at RWTH Aachen, with practical exposure to machine learning, predictive modeling, and optimization. Excited to apply analytical and technical skills to solve complex problems and support informed decision-making.
Overview
4
4
years of professional experience
Work History
Predictive Modeling Project
RWTH Aachen
04.2024 - Current
- Designing a Discrete Choice Experiment (DCE) to investigate how passengers choose subway doors based on attributes such as walking distance, crowd density, familiarity, and psychological factors.
- Generating choice sets in Python by applying attributes and constraints to ensure realistic, statistically sound combinations.
- Preparing a structured survey to collect respondent data, with the aim of estimating a Multinomial Logit (MNL) model to analyze behavioral patterns.
Machine Learning Project
RWTH Aachen
10.2024 - 01.2025
- Developed and optimized regression models to predict concrete compressive strength, using the UCI dataset (1,030 samples, 9 features).
- Implemented feature engineering and scaling techniques to enhance model performance and data interpretability.
- Trained and evaluated Linear, Polynomial, Ridge, and Lasso Regression models; conducted grid search with nested cross-validation for robust hyperparameter tuning.
Research Assistant
Fraunhofer Umsicht
08.2021 - 06.2023
- Designed and executed complex electrochemical experiments for sustainable chemical synthesis, leveraging Design of Experiments (DoE) to identify key parameter influences and optimize system performance - forming the basis for predictive analysis of electrochemical behavior.
- Collected, processed, and analyzed large-scale experimental datasets using Python, OriginPro, and Excel to evaluate reproducibility, reaction efficiency, and yield improvements.
- Used SAP for tracking chemicals and inventory management, integrating experimental data with ERP systems to streamline material flow and lab operations.
Education
Master of Science - Data Analytics & Decision Science
RWTH Aachen
09-2026
Master of Science - Chemistry
Ruhr University Bochum
06.2023
Bachelor of Science - Chemistry
Stella Maris College
04.2019
Skills
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
- SQL
- Power BI
- Excel
- R programming
- Machine learning
- Electrochemistry
- Predictive modeling
- Problem-solving
- Project management
- Collaborative teamwork
Publications
- Both Sides Matter: Anode Configurations Alter the Activity of Electrolyzers for Organic Hydrogenations, Pellumbi, K., Wolf, J., Viswanathan, S. C., Wickert, L., Kräenbring, M.-A., Kleinhaus, J. T., Puring, K. junge, Özcan, F., Segets, D., Apfel, U.P., Siegmund, D., RSC Sustain., 2023, 1, 3, 631639, https://doi.org/10.1039/D3SU00043E
- Developing Electrochemical Hydrogenation towards Industrial Application, Kleinhaus, J. T., Wolf, J., Pellumbi, K., Wickert, L., Viswanathan, S. C., Junge Puring, K., Siegmund, D., Apfel, U.P., Chem. Soc. Rev., 2023, https://doi.org/10.1039/D3CS00419H
Timeline
Machine Learning Project
RWTH Aachen
10.2024 - 01.2025
Predictive Modeling Project
RWTH Aachen
04.2024 - Current
Research Assistant
Fraunhofer Umsicht
08.2021 - 06.2023
Master of Science - Data Analytics & Decision Science
RWTH Aachen
Master of Science - Chemistry
Ruhr University Bochum
Bachelor of Science - Chemistry
Stella Maris College
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