Kusumika Krori Dutta is an enthusiastic lifelong learner. She believes that knowledge increases with sharing so a true team player. Mathematics and innovation are her main life goals, and Machine Learning helps her to go forward to achieve the same. She completed B.E  in Electrical Engineering and stood 1st class 1st, M.Sc (Engg) by research on Superconducting Fault Current Limiter, and PhD in “Electrophysiological Based Detection and Classification Of Epileptic Seizure Using Machine Learning Techniques”. Her research on the human brain made her a better human. She is now able to read through the minds, and it helps her excel in the teaching-learning process a lot. She is a passionate innovator with lots of patience. She believes AI/ML is not only for the software field but equally applicable to hardware implementations. In ever-growing demand for AI/ML can be matched by the shaking hands of hardware and software engineers. Being in teaching for 24 years, invented pattern based multiplication technique, 70+ publications, had 14 patents (8 granted, 2 published), 50+ lectures delivered in different Conferences, FDPs, Workshops, invited Tech talks, etc., developed more than 10-course content, taught 30+ different subjects both coding and mathematics-based, handled more than 14 different labs, guiding 50+ projects (many of it based on Electronics & AI/ML applications in various domains), working as a reviewer for many Q1 and Q2 journals. She has excellent leadership and management skills and is excited to collaborate with different researchers across the world. 

He holds a bachelor's degree in Chemical Engineering from the Lorena School of Engineering EELUSP (1994), a master's degree in Mechanical Engineering from the University of Taubaté UNITAU (2005), and a doctorate in Mechanical Engineering from the São Paulo State University Júlio de Mesquita Filho UNESP (2011). He is currently an Associate Professor at the State University of Rio de Janeiro, teaching undergraduate engineering courses, the Master's in Environmental Engineering (PEAMB), and the Doctorate in Environmental Engineering (DEAMB). He is the leader of the Environmental and Climate Data Analysis Research Group: statistical treatment and use of artificial intelligence, registered with CNPq. He has experience in the field of Mathematics and Statistics, with an emphasis on Applications of Statistics and Mathematics in Science, Mathematical Modeling, Optimization and Design of Experiments, Multivariate Statistics, Process Monitoring, and Artificial Intelligence.