Mark Hannel

Mark Hannel has a diverse work experience spanning multiple industries. Mark is currently working as a Machine Learning Scientist at Impact Observatory since June 2021. Prior to this, Mark worked at Neuberger Berman as a Data Science Engineer from April 2019 to May 2021. In this role, they evaluated prospective data sets and pitched potential use cases for asset managers. Mark also worked as a Data Science Fellow at Insight Data Science from January 2019 to April 2019.

Mark's academic background includes being a PhD Candidate in Physics at New York University from August 2011 to September 2018. During this time, they developed software for real-time image analysis and hardware manipulation. Mark also replaced heuristic-based algorithms with a convolutional neural network and trained support vector machines for parameter estimation. Mark also held a part-time adjunct instructor role, teaching undergraduate courses in Physics from August 2012 to May 2013.

Earlier in their career, Mark worked as an Academic Tutor at Endeavor Tutoring from May 2013 to March 2018, providing tutoring and test preparation services in Physics, Mathematics, and Engineering. Mark also gained research experience as an Undergraduate Researcher at Purdue University from October 2009 to May 2011, University of Florida from June 2009 to August 2009, and as an Undergraduate Research Assistant at Purdue University from August 2008 to May 2009. Mark also worked as an Academic Tutor at Purdue University from November 2008 to May 2011.

Mark Hannel has a strong educational background in the field of physics. Mark earned a Bachelor of Science (B.S.) degree in Physics from Purdue University, completing their studies from 2007 to 2011. Mark then pursued further education, enrolling in New York University from 2011 to 2018. During this period, they successfully obtained a Doctor of Philosophy (PhD) degree in Physics. Their educational journey showcases a continuous focus and commitment to advancing their knowledge and expertise in the field.