AIRA Matrix Private Limited
Dinisha Kadam began their work experience as a Trainee Design and Development Engineer at XcelLance Medical Technologies Pvt Ltd in June 2015. Dinisha later transitioned to a Research Design and Development Engineer role in July 2015, where they worked until July 2016.
In June 2016, Dinisha joined the Defense Institute of Advanced Technology (DIAT), DU, DRDO as a Post Graduate Student. Dinisha remained in this role until November 2021.
In August 2018, Dinisha joined AIRA MATRIX as a Data Scientist, where they are currently employed.
Dinisha Kadam earned a Master of Technology (MTech) degree in Modelling and Simulation from the Defense Institute of Advanced Technology (DIAT), DU, DRDO from 2016 to 2018. Prior to that, from 2011 to 2015, Dinisha attended the University of Mumbai. During the same period, they also obtained a Bachelor of Engineering (B.E) degree in Biomedical Engineering from Vidyalankar Institute Of Technology. Before attending the University of Mumbai, Dinisha completed their Higher Secondary Certificate (HSC) at Atomic Energy Junior College. Dinisha'ssecondary education was completed at Atomic Energy Central School, where they received their Secondary School Certificate (SSC). Dinisha has also acquired various certifications from Aspiring Minds, including AMCAT Certified Proficiency in English, AMCAT Certified in English Comprehension, AMCAT Certified Business Analyst, AMCAT Certified Data Processing Specialist, and AMCAT Certified Engineering Trainee - Electronics and Semiconductor Engineering, all obtained in 2014.
AIRA Matrix Private Limited
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AIRA MATRIX is a software company that provides image analysis and management solutions for preclinical toxicology and pathology applications. The company’s decision-support solutions help improve workflow efficiency and increase diagnostic accuracy in research labs, drug discovery studies, and clinics. They develop and design comprehensive DigitalPathology solutions.AIRA MATRIX deep-learning-based platform helps pathologists analyze large volumes of image data and quickly helps focus on relevant study findings. Their solutions go well beyond the simple marking applications currently offered in the industry, instead, their solutions address the more complex workflow processes and image analysis issues faced in pathology reporting.