Aditya Mishra, Ph.D., is a Lithography and Device Integration Yield Engineer at Intel Corporation in Oregon, USA. He received his Ph.D. from the University of Texas at Dallas. Dr. Mishra has authored numerous peer-reviewed publications in the field of optoelectronics and semiconductor technology, with his work appearing in prestigious, highly-cited academic journals. In recognition of his significant contributions to perovskite-based optoelectronic device research, he was awarded the SPIE Fellowship in 2022. His technical expertise includes CMOS Semiconductor Process Integration, electron beam metrology, electron beam lithography, and failure analysis methodologies.
Yogesh Kumar Rathore, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in Raipur, India. He completed his Ph.D. in Information Technology from the National Institute of Technology, Raipur. Before that, he completed his M. Tech. from Chhattisgarh Swami Vivekanand Technical University and his B. Eng. from Pt. Ravishankar Shukla University. He has published two edited books, contributed chapters to internationally edited books, and authored a textbook on data mining. Dr. Rathore has also been involved in patenting with three Indian patents published and two USA-granted patents, showcasing his expertise in the field of computer science engineering.
Suman Kumar Swanrkar, Ph.D., is an Assistant Professor in the Department of Computer Science and Engineering at Shri Shankaracharya Institute of Professional Management and Technology in Raipur, India. He received his Ph.D. (CSE) from Kalinga University, Nayaraipur and his M.Tech. (CSE) from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has contributed to book chapters published by Elsevier and Springer and is a member of IEEE, IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOP, IAOIP, EAI, and CSTA. Dr. Swanrkar’s research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.