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ISE Grad-Alumni Panel Discussion

Event Type
Seminar/Symposium
Sponsor
ISE Department
Date
Nov 11, 2021   5:00 - 6:00 pm  
Views
30
Originating Calendar
ISE Event Calendar

Panelist Biographies:

Dr. Conrad Tucker

Dr. Conrad Tucker is an Arthur Hamerschlag Career Development Professor of Mechanical Engineering and Machine Learning (Courtesy) at Carnegie Mellon University. His research focuses on the design and optimization of systems through the acquisition, integration and mining of large scale, disparate data.

Dr. Tucker has served as PI/Co-PI on federally/non-federally funded grants from the National Science Foundation (NSF), the Air Force Office of Scientific Research (AFOSR), the Defense Advanced Research Projects Agency (DARPA), the Army Research Laboratory (ARL), the Office of Naval Research (ONR) via the NSF Center for eDesign, and the Bill and Melinda Gates Foundation (BMGF). In February 2016, he was invited by National Academy of Engineering (NAE) President Dr. Dan Mote, to serve as a member of the Advisory Committee for the NAE Frontiers of Engineering Education (FOEE) Symposium. He received his Ph.D., M.S. (Industrial Engineering), and MBA degrees from the University of Illinois at Urbana-Champaign, and his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology.

Roberto Andrade

My name is Roberto Andrade, I was born and raised in Quito, Ecuador. I studied Electrical Engineering for my undergrad and came to UIUC and ISE (General Engineering back then) for grad school. I deeply enjoyed my time in C-U, it profoundly shaped my career and my life. One of my favorite activities was to be a TA for Control Systems during all 5 semesters that I was there. More recently, I completed a M.S. in Business Analytics from the University of Virginia in 2019 (But I still will pick Illinois in any game :)).

I am now part of Facebook’s People Analytics and Workforce Strategy organization. I lead a team working with data to provide evidence based solutions to create the best environment to attract and make people successful at their jobs. In my previous job, I led People Analytics for Retail Bank at Capital One. It was a similar yet different role as my current one, both rewarding and challenging. Before fully diving into People Analytics, I was part of Marriott, where I was a Sr. Director in Business Consulting. I worked in helping launch Marriott's homestay rental business "Homes and Villas" and also a joint venture with Alibaba in China. My first encounter with People Analytics was at LinkedIn where I led the launch of a pilot consulting offering for LinkedIn customers. Prior to LinkedIn I was part of Deloitte consulting, working in global technology transformation projects, particularly in Latin America. Before my consulting days, I was a software developer at Verizon. I joined their software development program, right after graduating from UIUC.

I have two daughters and I enjoy spending time with my family, reading, taking photos and watching soccer. 

Dr. Megan McGovern

Dr. Megan McGovern is a Senior Researcher in the Manufacturing Systems Research Lab at General Motors Global Research and Development. She joined General Motors in 2016, during which time she has used her nondestructive evaluation skillset to provide manufacturing solutions to internal customers. She leads research projects to develop inline and post manufacturing inspection systems which are crucial for first-time quality, especially in low-volume applications where launches are quick and highly customized. In 2019, she was the lead inventor in developing a technique to enable inline inspection of welded joints during pre-production battery pack assembly. Dr. McGovern received her Bachelor’s in General Engineering, Master’s and Doctorate degrees in Systems and Entrepreneurial Engineering from the University of Illinois Urbana-Champaign. Her research interests include inline and post manufacturing inspection solutions, nondestructive evaluation of materials, components and structures, digital signal and image processing, process monitoring, and prognosis of materials and components. She is proficient in several nondestructive techniques, including ultrasound, thermography, X-Ray radiography, and laser ultrasound.

Dr. McGovern is the chair of the Detroit Section of the American Society for Nondestructive Testing (ASNT). She is active in various professional societies and is a licensed Professional Engineer (Michigan). She has 5 patents and 24 peer-reviewed journal articles, one of which received the Outstanding Paper Published in Materials Evaluation in 2015. Dr. McGovern can be reached at megan.mcgovern@gm.com.

Dr. Andrea Hupman

Andrea Hupman earned a Ph.D. in Systems and Entrepreneurial Engineering from the University of Illinois at Urbana-Champaign in 2015, where she focused her research on decision analysis and target-based decision making. Upon graduation, she accepted a position of assistant professor at the University of Missouri-St. Louis in the Supply Chain & Analytics Department.  She has been active in research, teaching, and service since that time.  Her research interests include decision making under uncertainty, predictive modeling and uncertainty quantification, and applications of data analysis to business. She is particularly interested in understanding the value of information and leveraging insights from data for improved decision making within organizations. Her work has appeared in leading journals, including the European Journal of Operational Research, IEEE Systems Journal, and Decision Analysis. In her teaching, Dr. Hupman strives to foster critical thinking skills in her classes to help students reason through the logic underpinning probability, statistics, and data analysis, helping to make quantitative concepts accessible to all students. In recognition of her teaching excellence, Dr. Hupman received the 2017 Gerald and Deanne Gitner Excellence in Teaching Award.  Dr. Hupman is active in the Decision Analysis Society of INFORMS, serving as a society council member from 2018-2021. She also is an editorial board member for Decision Analysis.

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