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Tom Effland started their work experience as a Research Assistant at The Research Foundation for SUNY, where they collaborated with a team to develop matching algorithms for fingerprints and conducted research on statistical machine learning methods. Tom then worked as a Research Assistant at the University of Illinois at Urbana-Champaign, where they parallelized atmospheric simulation software and studied the use of Hadoop Streaming for parallelization of High Performance Computing codes. Tom also worked as a NSF Data-Intensive Computing Fellow at the University at Buffalo, where they focused on finding and retrieving semantically similar textual information from websites using semi-supervised learning.

Tom continued their career as a Summer Research Intern at Text IQ, where they gained experience in research. Tom then pursued a PHD at Columbia University in the City of New York, where they successfully defended their thesis and was a NSF Graduate Research Fellow and NSF IGERT "Data to Solutions" Fellow advised by Prof. Michael Collins. Tom's most recent position is as a Co-Founder and CTO at Noetica AI, a role they currently hold.

Tom Effland has a strong educational background in computer science. Tom earned a Doctor of Philosophy (Ph.D.) degree in Computer Science from Columbia Engineering, completing their studies between 2015 and 2022. Prior to that, they obtained a Master of Philosophy (MPhil) degree in Computer Science from the same institution, finishing in 2019. Tom also holds a Master of Science (MS) degree in Computer Science from Columbia Engineering, which they completed from 2015 to 2017.

Before attending Columbia Engineering, Tom Effland pursued a Bachelor of Science (BS) degree in Applied Mathematics at the State University of New York at Buffalo, graduating in 2015. Tom began their higher education journey at Frederick Community College in 2010 but did not complete a specific degree or field of study there.

In addition to their academic accomplishments, Tom Effland obtained a certification in "Introduction to Data Science" from Coursera in September 2014.