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Benevolent Research scholar Chirantan Gosh channelizes his entrepreneurial mindset toward social welfare

Posted on August 8, 2022 By

Chirantan Ghosh, a computer science researcher and scholar

August 8: A computer science researcher interested in computer vision, data science, machine learning, and robotics, Chirantan Ghosh has dedicated most of his life to coming up with solutions to societal and scientific problems. The journey of Chirantan Ghosh is about passion, perseverance and humanity.

Ghosh found his interest in research work while pursuing a master’s at the New Jersey Institute of Technology in Information Systems. A man of grit and determination, he soon enrolled himself in another master’s program in Computer Science at the University of Delaware. Ghosh channelized its passion for research to solve complex environmental, scientific and societal obstacles.

“Every youth is an asset to its society. Not everyone is privileged in this world, and achievements are to be carved out of situations. People can channel their achievements to benefit or utilize them for society’s welfare. It all depends on which path one chooses to walk. But the satisfaction and self-respect derived by serving for the greater good of the society are unmatched”, said Chirantan Ghosh.

The journey started while pursuing his master’s at the University of Delaware when Chirantan Ghosh was working on a project named ‘The National Oceanic and Atmospheric Administration (NOAA)’ by the US Government. It involves predicting the event of surface runoff and its magnitude to alert the farmers.

Surface Runoff occurs mainly due to heavy precipitation leading to nutrients being washed away into the water bodies. It affects both the economy and the environment. It negatively affects crop production and profit due to the loss of nutrients from the agricultural fields. It also pollutes the water leading to harmful algal blooms and hypoxia in the Great Lake region water bodies. Thus, the alerting system is essential to prevent water pollution by mitigating the farmers’ effort, cost, and time. The existing physics-based model used by NOAA does not show good performance.

Therefore, his research aimed to improve the performance by utilizing a machine learning algorithm and risk level prediction of surface runoff. Ghosh predicts that this research can move toward deep learning and has a scope of improvement to enhance model performance.

With his thirst for knowledge and determined mindset, Chirantan worked on another project for the National Institutes of Health (NIH), the US government. In this project, he used the deep learning-based multi-segmentation method to segment tiny RNA cells. This helps to automate the process, reducing the complexity of manually segmenting the cell wall and minimizing error and time.

These research experiences have motivated him to continue his studies for a doctorate in computer vision and deep learning at Lehigh University beginning in the fall of 2022. Chirantan will once more do research at Lehigh that will benefit society. One of his research projects will involve identifying fake news or videos from the plethora of online news available as videos or image content. This will help prevent or control the spreading of misinformation across the web and stop any damage that occurs.

His research skill and entrepreneurial mind have made him think about creating a startup named Camai.us, which is in the early stage. Camai.us plans to solve any object detection or identification problem like google lens but with better accuracy and on any device with a camera.

Chirantan’s concern for society and women’s security has pushed him to think about starting another organization named Hap.ai which will use computer vision and deep learning-based surveillance methods to detect and identify any threat and alert the appropriate organization to the subject in concern. It will help to overcome most of the challenges and security concerns related to women and promote a safe and healthy society.

To know more, visit: https://chirantanghosh.com/

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