Early Academic Pursuits

He, an accomplished researcher and assistant professor, embarked on his academic journey with a profound interest in Applied Mathematics. Graduating with distinction from the University of Calcutta, Bivas pursued his academic endeavors with unwavering dedication and a thirst for knowledge. His academic achievements, marked by exemplary performance throughout his educational tenure, laid the foundation for his illustrious career in the realm of machine learning and physics-informed models.

Professional Endeavors

With a Ph.D. in Applied Mathematics, he ventured into academia, assuming roles as an assistant professor at esteemed institutions such as the Indian Institute of Technology, Rourkela, and the Institute of Engineering & Management, Kolkata. His tenure as an educator and researcher has been characterized by a commitment to excellence, where he leverages his expertise in machine learning and deep learning to mentor students and lead cutting-edge research projects.

Contributions and Research Focus in Nanofluid

His research endeavors revolve around the intersection of machine learning and nanofluid properties prediction. With a focus on theory, algorithms, and optimization applications, he explores innovative approaches to predict nanofluid properties using deep learning models. His pioneering work on developing a smart model for predicting the viscosity of nanofluids containing spherical nanoparticles exemplifies his commitment to pushing the boundaries of knowledge in this field. Additionally, his projects encompass a diverse range of applications, from stock price prediction to fake news classification, showcasing the versatility of machine learning techniques.

Accolades and Recognition

Throughout his academic and professional journey, he has been the recipient of numerous accolades and honors. His contributions to the field of machine learning and nanofluid research have earned him recognition from peers and institutions alike. Notably, his innovative approach to predicting nanofluid properties using deep learning models has garnered praise for its potential to revolutionize engineering applications.

Impact and Influence

His research has made a significant impact on the fields of nanotechnology and machine learning. By harnessing the power of advanced computational techniques, he has contributed to advancing our understanding of nanofluid behavior and its implications for thermal conductivity and heat transfer enhancement. His work not only expands the frontiers of scientific knowledge but also holds promise for practical applications in engineering and materials science.

Legacy and Future Contributions

Notable Publications

• Combined effect of induced magnetic field and thermal radiation on ternary hybrid nanofluid flow through an inclined catheterized artery with multiple stenosis
• Combined impact of Brownian motion and thermophoresis on nanoparticle distribution in peristaltic nanofluid flow in an asymmetric channel
• Physics-based smart model for prediction of viscosity of nanofluids containing nanoparticles using deep learning
• A unique physics-aided deep learning model for predicting viscosity of nanofluids
• An expert model based on physics-aware neural network for the prediction of thermal conductivity of nanofluids
• Biomedical simulations of hybrid nano fluid flow through a balloon catheterized stenotic artery with the effects of an inclined magnetic field and variable thermal conductivity
• Deep learning based solution of nonlinear partial differential equations arising in the process of arterial blood flow
• Nanoparticle aggregation and electro-osmotic propulsion in peristaltic transport of third-grade nanofluids through porous tube
• A Comparative Study on Procedural and Predictive Systems on Drowsiness Detection