Justin Gottschlich at the University of Pennsylvania's Campus
|Citizenship||United States of America|
|Education||Colorado State University|
|Occupation||Adjunct Assistant Professor|
Justin Gottschlich is an American business executive, scientist, and inventor. His research spans artificial intelligence, embedded systems, computer architecture, and distributed computing.
Gottschlich is the Principal Scientist and Director/Founder of Machine Programming Research at Intel Labs.  He has led projects and collaboration initiatives at various corporations (e.g., BMW, Intel), startups (Machine Zone), universities (e.g., University of California, Berkeley, MIT, University of Pennsylvania, Stanford), and research working groups (e.g., MAPL, SG5). Gottschlich also serves as an adjunct assistant professor in the Department of Computer and Information Science at the University of Pennsylvania. 
Gottschlich is a primary contributor to the Machine Programming, both in academia and industry. His work towards a unified research platform has directly led to advances in this area, including automated regression testing and code similarity measurements.
After graduating with his Bachelor of Science from Colorado State University in 1998, Gottschlich began his career at Quark (company). In 1999, he founded Nodeka LLC., a multi-player online game company. Gottschlich joined Raytheon in 2004 as a Principal Software Engineer, and simultaneously began his Master of Science and Doctor of Philosophy at the University of Colorado Boulder, where he specialized in the optimization of parallel computing systems. Upon receiving his Doctorate, Gottschlich accepted a position at the Intel Corporation as a Research Scientist in 2010.
- "Justin Gottschlich". Intel. Retrieved 12 November 2020.
- "Justin Gottschlich". Penn Engineering. University of Pennsylvania. Retrieved 4 December 2020.
- Heaven, Will. "A new neural network could help computers code themselves". MIT Technology Review. Retrieved 12 November 2020.
- "The Three Pillars of Machine Programming Provide Core Concepts for Research Advances". Intel. Retrieved 12 November 2020.
- Alam, Mejbah; Gottschlich, Justin; Tatbul, Nesime; Turek, Javier; Mattson, Timothy; Muzahid, Abdullah (2019). "A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions". NeurIPS. 33. arXiv:1709.07536.
- Ye, Fangke; Zhou, Shengtian; Venkat, Anand; Marucs, Ryan (June 2020). "MISIM: A Novel Code Similarity System". arXiv:2006.05265. Cite journal requires
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