Akhilesh Jain
About Akhilesh Jain
Akhilesh Jain is a Data Science Manager at SparkCognition, where he leads a team applying machine learning and artificial intelligence across various industries. He holds a PhD in Chemical Engineering and has extensive experience in academia and industry, including roles at The University of Texas at Austin and OSIsoft.
Work at SparkCognition
Akhilesh Jain has been serving as a Data Science Manager at SparkCognition since 2022. In this role, he leads a team of data scientists focused on applying machine learning and artificial intelligence across various industries, including energy, aerospace, and defense. His work involves overseeing projects that leverage advanced data analytics to drive innovation and efficiency within these sectors.
Education and Expertise
Akhilesh Jain holds a Doctor of Philosophy (PhD) in Chemical Engineering from The University of Texas at Austin, where he studied from 2010 to 2016. He also earned a Master of Technology (M.Tech.) in Chemical Engineering from the Indian Institute of Technology, Kharagpur, from 2009 to 2010. Additionally, he completed a Bachelor's degree in Chemical Engineering from the same institution from 2005 to 2009. Jain has further enhanced his skills through a Machine Learning Engineer Nanodegree from Udacity in 2018 and certifications in data science and R programming from Coursera.
Background
Before his current role, Akhilesh Jain worked at OSIsoft as a Senior Product Support Engineer in Cloud Services and Network Operations from 2016 to 2020. He has a significant academic background, having served as a Graduate Research Assistant and Doctoral candidate at The University of Texas at Austin from 2010 to 2016. He also held multiple Teaching Assistant positions at the same university during 2011, 2012, and 2013. Jain's international experience includes a role as a Visiting Research Fellow at the National University of Singapore in 2009 and at UC Berkeley in 2008.
Research Contributions
Akhilesh Jain has conducted research in areas such as nanoimprint lithography and fluid dynamics during his PhD studies. He has published multiple papers in prestigious journals, contributing to the academic community and advancing knowledge in his field. His research work reflects a commitment to addressing complex challenges in engineering and data science.