Based in Erlangen, Germany 🇩🇪
I am completing my M.Sc. in Data Science at Friedrich-Alexander- Universität Erlangen-Nürnberg, with a focus on Machine Learning and Artificial Intelligence.
My work covers predictive modelling, deep learning, explainable AI, data engineering and LLM-powered applications. I enjoy building complete data products—from data ingestion and preprocessing to model evaluation, interactive applications and interpretable results.
For my master's thesis, I developed and evaluated machine-learning models for used-vehicle price prediction on more than 240,000 records, comparing performance under conventional IID evaluation and realistic temporal distribution shift.
I am currently seeking full-time opportunities in Germany in:
- Data Science
- Machine Learning
- Applied AI
- Data Analytics
- Junior ML Engineering
An applied AI platform designed to analyse job requirements, extract relevant skills and identify gaps between candidate profiles and market expectations.
Key components
- Candidate and job-profile analysis
- Structured skill extraction and taxonomy
- Skill-gap identification
- FastAPI backend
- Streamlit user interface
- Modular Python application structure
Technologies: Python, FastAPI, Streamlit, pandas
Master's thesis comparing linear, tree-based and neural-network models for used-vehicle price prediction under IID and temporal evaluation settings.
Highlights
- Built an end-to-end machine-learning pipeline for 240K+ vehicle records
- Implemented Linear Regression, Random Forest, XGBoost, CatBoost and ANN models
- Designed separate IID and temporal evaluation strategies
- Achieved an IID test R² of 0.917 with XGBoost
- Analysed model robustness when predicting prices for future vehicle listings
- Compared predictive performance, generalisation and model stability
Technologies: Python, pandas, NumPy, scikit-learn, XGBoost, CatBoost, PyTorch
Repository will be published after the final thesis result and academic requirements are completed.
An LLM-powered automotive analytics assistant that allows users to explore large vehicle datasets using natural-language questions.
Highlights
- Supports arbitrary CSV uploads
- Performs automatic schema detection and preprocessing
- Handles missing values and numeric type conversion
- Integrates OpenAI and Groq language models
- Enables natural-language querying over datasets containing 100K+ records
- Provides interactive summaries, exploration and query results
- Includes safeguards for parsing errors and application state
Technologies: Python, pandas, Streamlit, LangChain, OpenAI API, Groq API
A deep-learning project focused on potato leaf disease classification and efficient model deployment across different hardware platforms.
Highlights
- Achieved up to 97.29% classification accuracy
- Evaluated models on T4 GPU, RTX 2080 and Coral TPU hardware
- Compared accuracy, latency, power usage and energy efficiency
- Applied TensorRT and TensorFlow Lite optimisation
- Reduced measured inference time from 7.96 seconds to 0.23 seconds
- Investigated TensorFlow retracing and graph-execution bottlenecks
Technologies: TensorFlow, Keras, TensorRT, TensorFlow Lite, CNN, Python
Crime and Weather Correlation Analysis — Los Angeles
Built an ETL and analytics pipeline combining Los Angeles crime and weather data from 2020–2023.
- Integrated crime and weather datasets through a structured ETL pipeline
- Performed temporal and geospatial analysis
- Investigated seasonal patterns and high-density crime locations
- Examined statistical associations between weather variables and crime rates
- Created maps and visualisations for communicating results
Technologies: Python, pandas, PostgreSQL, Matplotlib, Folium
CNN versus Vision Transformer Image Classification
Compared VGG-16 and ViT-B/16 architectures using a multi-class image dataset containing more than 22,000 images.
- CNN test accuracy: 90.60%
- Vision Transformer test accuracy: 95.40%
- Analysed convergence, architecture, model capacity and computational trade-offs
- Compared convolution-based and attention-based feature representations
Technologies: Python, PyTorch, VGG-16, Vision Transformer
Explainable AI for Predictive Quality in Manufacturing
Developed predictive models for manufacturing quality control and used explainability methods to make model outputs more understandable.
- Built XGBoost and LSTM models for predictive quality analysis
- Applied SHAP to identify influential input variables
- Created Power BI dashboards for model outputs and feature importance
- Translated model results into decision-support information
Technologies: Python, XGBoost, LSTM, SHAP, Power BI
FAU Innovation Ecosystem Platform
Designed a user-centred platform concept to improve access to the innovation ecosystem in Fürth.
- Conducted user and stakeholder research
- Applied Design Thinking and the Double Diamond framework
- Developed interactive map and recommendation concepts
- Defined user journeys, value propositions and customer segments
- Contributed to prototyping and business-model development
Methods: Design Thinking, User Research, Prototyping, Business Model Canvas
| Degree | Institution | Status |
|---|---|---|
| M.Sc. Data Science | FAU Erlangen-Nürnberg, Germany | Thesis completed; final result pending |
| B.Sc. Computer Science, Mathematics and Statistics | CHRIST University, India | First Class, CGPA 9.05/10 |
- e-fellows.net Scholarship Holder — selected for a leading German career and academic network
- Rashtrapathi Award, Bharat Scouts and Guides — national recognition for leadership and community service
- Student Welfare Office Member, CHRIST University — supported student engagement and peer-guidance initiatives
I am open to full-time opportunities and collaborations involving machine learning, data science, deep learning, explainable AI and applied AI systems.