About the organization
Jaro Education is India's most trusted online higher education company. The Edtech firm, a pioneer in the executive education industry, was founded in 2009 by Dr Sanjay Salunkhe and has been profit-making since its inception. The company has been awarded the Edtech leadership award and the National best employer accolade by World HRD Congress in March 2022.
The company aims to nurture entrepreneurs & working professionals from entry-level to C-Suite level in every field and industry by offering world-class executive education programs that cater to their requirements. Having been recognized for changing the landscape of online education in India, Jaro Education acts as a bridge between industry and academia. It offers diverse management and technology programs in collaboration with top-ranked institutes and universities across the globe. The company also facilitates the development of management and technology-based online programs for many leading Indian and global universities and institutes.
With its strong domain expertise and insight into executive education, Jaro Education has transformed the careers of over 3 lakh professionals in the last 13+ years through its 20+ learning centers across India.
For more information, please visit: https://www.jaroeducation.com
Faculty Members
- Ganapathy Krishnamurthi
Prof. Ganapathy Krishnamurthi is faculty in the Department of Engineering Design and associate faculty at the Robert Bosch Center for Data Science and Artificial Intelligence at IIT Madras. He earned his PhD from Purdue University, and MSc in Physics from IIT Madras. He worked as a post-doctoral research fellow at Case Western Reserve University, USA and at Mayo Clinic, USA.
His research and work experience focuses on applying Machine Learning and Artificial Intelligence techniques to problems in medical image analysis, computer vision, interpretability/explainability of Deep Learning models across various applications  and using deep learning to solve inverse problems in medical imaging and computer vision. His current research involves developing deep learning solutions for time series data in business, engineering and imaging applications. He has published numerous research papers pertaining to Deep Learning and Machine Learning applied to many areas in science, engineering and technology.
His research and work experience focuses on applying Machine Learning and Artificial Intelligence techniques to problems in medical image analysis, computer vision, interpretability/explainability of Deep Learning models across various applications  and using deep learning to solve inverse problems in medical imaging and computer vision. His current research involves developing deep learning solutions for time series data in business, engineering and imaging applications. He has published numerous research papers pertaining to Deep Learning and Machine Learning applied to many areas in science, engineering and technology.
- Prof. Balaji Srinivasan
Prof. Balaji Srinivasan is faculty in the Department of Mechanical Engineering and an associate
faculty at the Robert Bosch Center for Data Science and Artificial Intelligence at IIT Madras,
pursuing research in the areas of fundamental Machine Learning and Deep Learning with focus on applications to science and engineering disciplines. He earned his B.Tech from IIT Madras, MS from Purdue University, and PhD from Stanford University, where he was the William K. Bowes, Stanford Graduate Fellow. Prior to his current role at IIT-Madras, he was a faculty member at IIT- Delhi and a post-doctoral fellow at University of Michigan, Ann Arbor.
His current research involves developing computational algorithms and models for a range of practical engineering problems that use a combination of probabilistic models, PDE based approaches as well as data-driven approaches. He has published research papers across multiple domains including Machine Learning, Partial Differential Equations, Computational algorithms, and High performance computing.
His current research involves developing computational algorithms and models for a range of practical engineering problems that use a combination of probabilistic models, PDE based approaches as well as data-driven approaches. He has published research papers across multiple domains including Machine Learning, Partial Differential Equations, Computational algorithms, and High performance computing.
