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Jared Schoepf is the Director of Operations for Engineering Projects in Community Service (EPICS) at Arizona State University. Jared received his PhD in Chemical Engineering at ASU, developing a tiered approach to rapidly detect nanomaterials in the environment and consumer products. Jared has been a lecturer of EPICS for 6 years, mentoring over 300 teams. Currently he teaches introduction to engineering, EPICS, and chemical engineering courses. He has founded 2 starts ups and has 3 patents for water purification, removal of trash from storm water, and antibacterial liquid hand soap formula. He has a passion for teaching and mentoring students, aiming to help each student achieve their goals

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Stephanie Gillespie is a lecturer at the University of New Haven in the Engineering and Applied Science Education department. She previously specialized in service learning while teaching at the Arizona State University in the Engineering Projects in Community Service (EPICS) program. Her current teaching and research interests are in developing study skills and identity in first-year engineering students and improving retention rates. She acts as the faculty liaison for the University of New Haven Makerspace and facilitates student and faculty training. She received her Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology, and her BSEE from the University of Miami.

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Amy Trowbridge is a Senior Lecturer in the Ira A. Fulton Schools of Engineering at Arizona State University and is the Director of the National Academy of Engineering (NAE) Grand Challenge Scholars Program (GCSP) at ASU. Through the GCSP, Amy aims to prepare students to become globally and socially aware engineers who will lead future efforts to solve the world’s biggest challenges. Amy also helps new schools to develop GCSPs as part of the NAE GCSP Proposal review committee. She is also actively involved in the Kern Entrepreneurial Engineering Network (KEEN), focused on students’ development of entrepreneurial mindset through GCSP and curriculum. Amy recently received the 2019 KEEN Rising Star award for her efforts in encouraging students to develop an entrepreneurial mindset. Amy has contributed to the development of a new hands-on multidisciplinary introduction to engineering course and a unique introduction to engineering MOOC. She is interested in curricular and co-curricular experiences that broaden students’ perspectives and enhance student learning, and values students' use of Digital Portfolios to reflect on and showcase their accomplishments. Amy earned her Master’s degree in Biomedical Engineering from Arizona State University (ASU), and is currently pursuing her PhD in Engineering Education Systems and Design.

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Dr. Alison Cook-Davis is Assistant Director for Program Evaluation at the Arizona State University’s Office of Evaluation and Educational Effectiveness (UOEEE). She has a BA in Psychology, MS in Social Psychology, MLS Legal Studies, and a Ph.D. in Experimental Social Psychology. Prior to joining UOEEE, she supported the research and program evaluation efforts of Maricopa County Adult Probation Department, coordinated and executed the research and program evaluation for a large Department of Justice Second Chance Act grant. These efforts included monitoring, assessing, and evaluating the impacts of program outcomes. Since joining the UOEEE in 2015, Dr. Cook-Davis has led research and evaluation activities for over 50 separate grant-funded programs or initiatives funded by the National Science Foundation, U.S. Department of Education, U.S. Department of State, U.S. Department of Agriculture, National Institutes of Health, and The Kern Family Foundation. These projects have focused on the evaluation of student success, outreach impacts, innovative learning techniques, and STEM-related interventions and curricula.

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Kristen Peña is the Senior Program Coordinator for the Arizona State University (ASU) Kern Grant. The project’s goal is to instill an entrepreneurial mindset (EM) in engineering education. She works closely with the Ira A. Fulton Schools of Engineering and Undergraduate Student Engagement office. Kristen manages several entrepreneurial programs and events across the project’s initiatives at ASU including: KEEN Entrepreneurial Catalysts, KEEN student grants, the Grand Challenges Scholars Program (GCSP) Entrepreneurial Experience, NAE Grand Challenges for Engineering Speaker Series, and the Robots That Matter series.

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Building on existing research, I use advanced statistical analyses and research methods to answer questions regarding parenting, family relations, disabilities, and other topics. I also use these skills to evaluate the effectiveness of programs. I use R and other statistical software for my analyses and reports. I am continually expanding my skill set in statistics and data science to best answer research questions.

Leaders in science, policy, and business committed to evidenced-based decision-making embrace the refrain, "Data or it didn't happen." I have adopted this refrain as a guiding principle in my life and work. I use data to make sound decisions and draw conclusions that do not extrapolate beyond the data. When hard data are not available, I wait to make a decision, when possible, or critically evaluate what is known before making a decision.

My extensive training and experience have focused on obtaining and analyzing data. As a firm believer that days of statistical analyses cannot make-up for a missed hour of project design, I value the design process of generating good data. Indeed, some have suggested a revised refrain "Good data or it didn't happen." Good data are data that answer the right question. Part of the design process involves updating measures and methods to answer the question and, in many cases, revising the question to address what we really want to know.

I have invested and continue to invest significant time and effort into developing quantitative and qualitative analytic skills. I do this because it allows me to select the methods that best answer a research question. It also allows me to balance methodological rigor and practical constraints, but still obtain the most correct answer possible. My approach to data analysis is (paraphrasing Einstein) that data analysis should be as simple as possible, but not simpler.

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