Yogesh Mukhi
About Yogesh Mukhi
Yogesh Mukhi is an Associate Vice President with extensive experience in GIS and machine learning. He has held various technical leadership roles at notable companies and has a strong academic background in Geomatics Engineering and Computer Science.
Current Role at Xceedance
Yogesh Mukhi serves as the Associate Vice President at Xceedance, a position he has held since 2021. He is based in Noida, Uttar Pradesh, India. In this role, he focuses on advanced geospatial analytics and the integration of technology solutions to enhance operational efficiency.
Previous Experience at Xceedance
Before his current role, Mukhi worked at Xceedance as a Principal Consultant from 2018 to 2021. During this time, he contributed to various projects that involved geospatial data analysis and technology implementation. He initially joined Xceedance as Tech Lead GIS from 2016 to 2018, where he led significant initiatives in GIS technology.
Career at RMS and Webonise Lab
Yogesh Mukhi began his career as a GIS Engineer at LASA from 2010 to 2012. He then transitioned to RMS, where he worked as Lead GIS Engineer from 2012 to 2015 in Delhi NCR. Following his tenure at RMS, he served as Technical Lead GIS at Webonise Lab from 2015 to 2016 in Pune Area, India. His roles involved managing GIS projects and leading technical teams.
Educational Background
Mukhi earned a Master's Degree in Geomatics Engineering from the Indian Institute of Technology, Roorkee, where he studied from 2008 to 2010. Prior to that, he completed his Engineer's Degree in Computer Science and Engineering at the College of Engineering Roorkee from 2004 to 2008. His educational background provides a strong foundation for his expertise in GIS and technology.
Specializations and Projects
Yogesh Mukhi specializes in building footprint extraction and attribute identification using computer vision models. He has led the development of a TRW Catalog Database, focusing on API development and Big Data implementation. Additionally, he has worked on hazard model development for sinkholes and wildfires using machine learning techniques.