“One Algorithm at a time”
Driven by curiosity, fueled by data
Data Scientist
Creative problem solver
statistical analysis
Business Development
Avid Researcher
Design thinking
Biomedical Signal processing
Problem to product
Highlights
“Collaboration is the key to success”
Collaboration Project with ETH Zurich and Schweizer Paraplegiker-Zentrum (SPZ) in Nottwil, Switzerland
(02/2023-Present)
As an AI enthusiast in practical applications, especially in the medical field, I had a great opportunity to work at SPZ to develop a noninvasive and continuous method for blood pressure estimation.
My work revolved around creating an explainable, transfer learning-based, CNN-based automated feature extraction framework using multimodal biosignals to detect blood pressure changes, especially in spinal cord injured patients.
Design Science Research Saloon in collaboration with the University in St. Gallen, Switzerland
(06/2021, 04/2022)
Why create a solution that solves a single problem when you can create a solution that is reusable, transferable, and generalizable? Design thinking much!
During this cross-disciplinary, thought-provoking research saloon, I had a great opportunity to think outside my technical hat. Structured and collaborative formulation of new solutions along with knowledge sharing in the form of artefacts: This is what I learned and use consistently in my work.
Innovation Management in collaboration with the European Institute of Innovation & Technology (EIT) Digital project at Eötvös Loránd University, Budapest, Hungary
(09/2021 - 03/2022)
Let’s use the lesson learned and knowledge gained in different problem settings and assess its viability. Moreover, how many other creative ways can a solution be solved?
During this collaboration, I was the main researcher in the following tasks: business problem formulation and assessment, AI-based innovative solution creation and analysis, possible product concept formulation, market analysis.
Internal Collaborative Exchange Program at Robert Bosch GmbH, Reutlingen, Germany
(09/2023-04/2024)
Another great opportunity to work in research work for discovering defect patterns in semiconductor wafers. I was the lead architect and main data science researcher.
I was responsible for developing a Convolutional Autoencoder (CAE)-based explainable, unsupervised clustering framework to detect single/multiple defect patterns from semiconductor wafer maps. This project resulted in an end-to-end, automated, and flexible clustering framework.