Research Associate in Germany

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

Part-time position in a federally funded research project

• Supported research in continuum mechanics with emphasis on finite element modeling, material behavior, wear mechanics, and coupled PDE formulations.
• Developed and validated numerical simulations, performed data processing and analysis, and contributed to technical publications.
• Assisted with automation scripts, preparation of figures and documentation, and day-to-day project coordination to ensure smooth research progress.
• Implementation of algorithms is performed in Fortran and Python.

• Implemented stereo camera calibration and applied incremental Structure from Motion (SfM) algorithms to build a detailed 3D model of a forklift for an AR-enabled Smart Forklift project
• Developed OpenCV-based preprocessing that improved data accuracy by 20% and enhanced the analysis of industrial videos

• Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
• Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
• Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
• Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.

• Performed qualitative and quantitative analysis of extracted findings, categorizing themes, evaluating methodologies, and assessing study quality and reliability.
• Produced research reports and evidence summaries communicating key trends, gaps, and opportunities to academic advisors or cross-functional teams.
• Presented findings through well-structured visualizations, tables, and narrative summaries to support decision-making and guide future research directions.

• Developed machine learning and symbolic modeling workflows to predict RF material permittivity based on impedance spectroscopy data.
• Focused on preprocessing techniques to enhance model accuracy and improve RF sensor performance in high-frequency environments.

• Led a research project on investigating factors modulating HPV16 infection in keratinocytes by using microscopy and omics approaches.
• Curated and analysed RNAseq and mass-spec data and performed pathway analysis to identify target proteins.
• Responsible for the operation, proper maintenance, functionality and user training of microscopes and FACS equipment.
• Mentored master students on their thesis on how cell-ECM interactions play a role in HPV susceptibility in keratinocytes.

• Developed a React-based research platform with interactive 3D/AR product visualization workflows.
• Implemented interaction tracking and usage analytics to measure user behavior and feature engagement within a research platform.
• Deployed the research platform and integrated a R Shiny analytical dashboard, enabling researchers to interactively explore meta-analysis results within a unified platform.

• Developed NLP and LLM POCs for use in manufacturing companies
• Applied advanced machine learning algorithms to analyze production data and develop custom data pipelines for quality assurance
• Designed and led the IAO basic seminar on AI in industry, including hands-on training modules

• Designed and implemented a real-time perception pipeline using YOLOv7 on Time-of-Flight (ToF) sensor data, enabling live streaming, inference, and on-frame visualization for passenger detection.
• Fine-tuned and evaluated multiple state-of-the-art monocular depth estimation models for Automatic Passenger Counting (APC), and developed a custom hybrid depth model that improved depth accuracy in challenging scene regions.
• Demonstrated that model-generated depth maps outperform raw sensor depth for APC tasks across several datasets, contributing to measurable reductions in counting error.

• Implemented data/feature pipelines and automated validation/reporting for multi-million-row insurance datasets.
• Built production-grade image processing to detect fabric defects; delivered maintainable, well-tested Python code and concise reports.

• Published a peer-reviewed article on comparative analysis of biophysical models in diffusion MRI, impacting ongoing research projects.
• Got SciPy selected for the cover image of the corresponding journal issue.

• Built end-to-end pipelines for sensor-driven time-series vibration signals: data cleaning, feature engineering, model selection for unsupervised learning, and validation for size classification tasks.
• Applied statistical feature extraction to characterize dynamic responses.
• Developed Python algorithms for signal processing (FFT, PSD) and classification; compared multiple model architectures with uncertainty-aware evaluation.
• Supervised students in labs and projects (analysis methods, interpretation, reporting); delivered a practical laboratory course and contributed to documentation and presentations.
• Prepared academic outputs: Master’s thesis and conference contribution (SimScience 2025).