Linköping University-- Development of tools for interactive 3D visual data analysis using Python and C++ enabling users to interact and make sense of their data .-- Exploration of complex and dynamic datasets through advanced data-driven analysis and visualization techniques (i.e. clustering, pattern recognition, feature extraction) to gain deeper insights and facilitate more accurate and well-informed decision-making. -- Involved in every step of the process, from data tooling and modeling to prototyping, visualization, and the creation of user-friendly dashboards. -- Together with colleagues at Zenseact, we adapted Neural Radiance Field (NeRF) to reconstruct real-world driving scenarios by utilizing camera and lidar data. My contribution was to utilize persistence homology approaches to preserve the topology of the reconstructed scenes.-- Critically analyzing and presenting the results through developed visual representation along with insightful interpretation and visual storytelling. -- Collaborating with diverse partners across various domains, each offering unique datasets and specifications. -- Teaching courses in Data structure, Advance C++, scientific visualization.-- See the publication section for the research outcomes.

Implemented an end-to-end neural network method for 3D medical image registration for multi-modal prostate dataset and compared with classical iteration-based approaches through statistical testing techniques.

Grid Manipulation in Visualization Toolkit (VTK) for performance evaluation

My responsibilities are to implement the image processing algorithms in C++.Client-Server Programming in QT5 in C++ with OPENCV.

As a Software Developer internee my task was to modify the existing c++ code as per requirement.