Phil joined JBS in 2009 as a software architect. Over time, Phil has founded JBS’s Open Source Practice and its Architecture Group; he is now JBS’s Chief Architect. An industry veteran with over 20 years of experience, Phil has designed and developed systems in a variety of areas – defense, healthcare, retail, scientific, finance, and insurance industries to name just a few. He holds both a BS in Computer Science and a BS in Mathematics from the University of Pennsylvania and lives in Philadelphia, PA.
Experienced researcher with a demonstrated history of working in the higher education industry. Skilled in Educational Technology, Psycholinguistics, Research Design, Leadership, and Classroom Instruction. Strong research training with an M.S. and Ph.D. focused on Human Factors Psychology from Wright State University, and a Post Doctoral Research Fellowship under Prof. Art Graesser at the University of Memphis.
Joe Pringle is a Principal Technical Business Development Manager at AWS supporting public sector customers in their adoption and use of AI and machine learning. Joe’s passion is helping customers unlock new insights and value from data, and put those insights into the hands of people to make better decisions. Prior to AWS Joe worked in various leadership roles in startups and consulting. He brings 20+ years of experience working with influential public sector organizations at all levels of government, the non-profit sector, and academia.
EdTech Maryland Meetup Virtual Event
As the influence of machine learning in the educational ecosystem evolves and expands, consideration must be given to a broad range of novel ethical and practical issues.
Check out the virtual EdTech Maryland Meetup for an insightful panel discussion as our experts in academia and technology attempt to summarize the central conflicts and challenges in establishing responsible machine learning practices to mitigate bias in education.
There are a lot of positive things machine learning has brought to the classroom, helping the education industry advance to better benefit students and teachers. However, many common machine learning algorithms can be heavily affected by biased data. This can lead to many inaccuracies and unwanted behavior in our machine learning models. Tune in as our panelists give an overview of how seemingly objective applications can lead well-meaning practitioners astray. They will discuss the efforts underway to mitigate the risk associated with these scenarios, a range of tools available, and how these issues fit within the larger educational technology field.
- Phil Horwitz, Chief Architect at JBS Custom Software Solutions
- Dr. Andrew Hampton, Assistant Professor of Psychology at Christian Brothers University, and Chair of the IEEE Standards Association Working Group for Adaptive Instructional Systems
- Joe Pringle, Principal Technical Business Development, AI and machine learning at Amazon Web Services