Shiwani is from India, which is a beautiful country full of passionate people. With an aim to develop strong fundamentals in a reputed academic research environment, she chose to do M.Tech (optoelectronics & optical Communication Engineering) at the Indian Institute of Technology Delhi (IIT-Delhi) which she completed in June 2019.
Shiwani finished her master dissertation on the “Microstructured Optical Fiber for midinfrared (MIR) Source” under the supervision of Prof. R.K. Varshney at the Indian Institute of Technology, Delhi - India as an ‘MHRD funded Research scholar’. The objective of her work was to develop an efficient and cost-effective source for MIR spectral range, based on a non-linear optical phenomenon like four-wave mixing. At this institute, she had the opportunity to work independently after having been trained on different analysis techniques for various photonic structures, such as the Beam Propagation Method (BPM), Plane Wave Expansion Method (PWE) and Finite Element Method (FEM). Additionally, she worked with the numerical calculations using nonlinear coupled wave equations in the process of four-wave mixing using MATLAB which provided more understanding of numerical analysis for the non-linear optical process.
Throughout her academic career, Shiwani has been exploring different areas of research to acclimatize to some of the vast available fields of research and as such expand her knowledge and research aptitude; this is how she secured a position in Leibniz-Institut für Photonische Technologien eV Jena under the guidance of Iwan Schie.
Besides her research interest, she is also keen on outreach and communicating science to the wider public.
Shiwani’s MONPLAS focus is to develop a high-throughput Raman microspectrometer with laser line excitation for analysis of microplastic particles on a substrate or in continuous flow. She is working on development of the system that can be used for the spectroscopic characterization of microplastics. This work also includes optical and software design an additionally the application and implementation of image and spectroscopy based machine-learning approaches including automated object localization and sampling.