About

Ayusman Chiranjibi is a highly skilled Engineer currently working at udaan. With a Bachelor of Science degree in Chemistry from the Indian Institute of Technology, Kharagpur, Ayusman has gained 1.86 years of relevant experience in the field of Research Science. Prior to his current role, Ayusman has worked as a Research Intern at Carnegie Mellon University, where he trained and tested Convolutional Neural Networks (CNNs) for segmentation and classification of subtomograms simulated from known structures of macromolecules with noise and missing wedge-effects. He also proposed models with 3D residual blocks based on DenseNet and Grouped convolution architecture, which improved the accuracy and precision of classifying subtomograms even in low Signal-to-Noise Ratios (SNRs). Ayusman's proposed models were tested on EMPIAR datasets with transfer learning and retrieved a much higher F1-score. Ayusman has also worked as a Summer Research Intern at Università degli Studi di Padova, where he analyzed Time-Dependent Density Functional Theory (TDDFTs) of Polycyclic Aromatic Hydrocarbons (PAHs) to model them as molecular nanoplasmons. He demonstrated the enhancement of absorbance and emission properties of the system due to nanoplasmonic resonance of PAHs with naturally occurring chromophores. He also studied the role of distance, orientation, solvent, and charge on the enhancement of absorption in the near-resonance effect of PAH plasmons. Ayusman's Tech Stack includes Research Scientist, and he has a deep understanding of the latest technologies and trends in his field. With his strong academic background and relevant experience, Ayusman is a valuable asset to any organization.