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edfas.org ELECTRONIC DEVICE FAILURE ANALYSIS | VOLUME 21 NO. 2 20 ACKNOWLEDGMENTS This work was funded in part by the NSF under CNS 16-24811 and the industrymembers of the CAEML I/UCRC. REFERENCES 1. B. Schroeder and G. Gibson: “Disk Failures in the Real World: What Does anMTTF of 1,000,000HoursMean to You?” FAST’07: 5thUSENIX Conference on File and Storage Technologies, February 2007. 2. J. Elerath: “Hard-Disk Drives: The Good, the Bad, and the Ugly,” Commun. ACM, June 2009, 52, p. 38-45. 3. B. Zhu, G. Wang, X. Liu, D. Hu, S. Lin, and J. Ma: “Proactive Drive Failure Prediction for Large Scale Storage Systems,” 2013 IEEE 29th Symposium on Mass Storage Systems and Technologies (MSST), May 2013, p. 1-5. 4. C. Xu, G. Wang, X. Liu, D. Guo, and T.Y. Liu: “Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks,” IEEE Transactions on Computers, Nov 2016, 65, p. 3502-3508. 5. Backblaze: “Hard Drive Data and Stats,” Backblaze Hard Drive Stats, 2018. [Online]. https://www.backblaze.com/b2/hard-drive- test-data.html. 6. I. Guyon and A. Elisseeff: “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, March 2003, 3, p. 1157-1182. 7. D. Koller andN. Friedman: “ProbabilisticGraphical Models: Principles and Techniques.” Cambridge, Mass.: MIT Press, 2009. ABOUT THE AUTHORS AlanYang receivedhis B.S. degree inelectrical engineering fromtheUniversity of Illinois at Urbana- Champaign in 2018. He is currently a graduate student in the electrical and computer engineering department at the University of Illinois at Urbana-Champaign. AmirEmad Ghassami received a B.Sc. degree in electrical engineering from the Isfahan University of Technology in 2013 and a M.Sc. degree from the University of Illinois at Urbana-Champaign in 2015. He is currently a graduate student with the department of electrical and computer engineering, University of Illinois at Urbana-Champaign. His research interests include causal structure learning, information theory, and statistical inference. Elyse Rosenbaum is the Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering at the University of Illinois at Urbana- Champaign. She received her Ph.D. in electrical engineering from University of California, Berkeley. She is the director of the NSF-supported Center for Advanced Electronics through Machine Learning (CAEML), a joint project of the University of Illinois, Georgia Tech, andNorth Carolina State University. Rosenbaum, a Fellow of IEEE, has authored nearly 200 technical papers and has been an editor for IEEE Transactions on Device andMaterials Reliability and IEEE Transactions on Electron Devices. She was the recipient of a Technical Excellence Award from the SRC, an NSF Career Award, an IBM Faculty Award, and the ESD Association’s Industry Pioneer Recognition Award. Negar Kiyavash is a joint associate professor in the H. Milton Stewart School of Industrial & Systems Engineering (ISyE) and the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Technology (Gatech). Prior to joining Gatech, she was a Willett Faculty Scholar and a joint associate professor of industrial and enterprise engineering (IE) and electrical and computer engineering (ECE) at the University of Illinois. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF Career and AFOSR YIP awards and the Illinois College of Engineering Dean’s Award for Excellence in Research.