Application and Product-Volume Specific Customization of BEOL Metal Pitch

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S. Pagliarini, M. Isgenc, M. Martins and L. Pileggi, “Application and Product-Volume Specific Customization of BEOL Metal Pitch,” IEEE Transactions on VLSI, Vol. 26, Issue:9, pp. 1627-1636, September 2018.

ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph

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B. Hooi, D. Eswaran, A. Pandey, M. Jereminov, L. Pileggi, and C. Faloutsos, “ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph,” (Best Student Paper Award, Runner Up), Proceedings of the 2018 ACM Conference on Information and Knowledge Management, 2018.

An Oscillatory Neural Network with Programmable Resistive Synapses

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T. Jackson, S. Pagliarini and L. Pileggi, “An Oscillatory Neural Network with Programmable Resistive Synapses,” in 28 nm CMOS, IEEE International Conference on Rebooting Computing, November 2018.

An Equivalent Circuit Formulation for Power System State Estimation including PMUs

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A. Jovicic, M. Jeremino, L. Pileggi and G. Hug, “An Equivalent Circuit Formulation for Power System State Estimation including PMUs,” (Best Paper Award, Second Prize), 50th North American Power Symposium, October 2018.

GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid

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B. Hooi, D. Eswaran, H.A. Song, A. Pandey, M. Jereminov, L. Pileggi, and C. Faloutsos. “GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid,” European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018.

A 125 Ms/S 10.4 ENOB 10.1 fJ/conv-Step Multi-Comparator SAR ADC with Comparator Noise Scaling in 65nm CMOS

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S. Liu, T. Rabuske, L. Pileggi, J. Fernandez, J. Paramesh, “A 125 Ms/S 10.4 ENOB 10.1 fJ/conv-Step Multi-Comparator SAR ADC with Comparator Noise Scaling in 65nm CMOS,” IEEE European Solid-State Circuits conference, September 2018.

Best Student Data Mining Paper Runner Up in ECML/PKDD

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This recognition was received at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, for the following paper:

GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid

Bryan Hooi (ML/Stat), Dhivya Eswaran (CSD), Hyun Ah Song (MLD), Amritanshu Pandey (ECE), Marko Jereminov (ECE), Larry Pileggi (ECE), Christos Faloutsos (CSD/MLD)

http://www.ecmlpkdd2018.org/wp-content/uploads/2018/09/10.pdf

The paper solves two related problems: (a) it gives fast algorithms to detect anomalies in power-grid networks, for a given set of voltage and current sensors placed on some nodes and edges; and (b) it shows where to put such sensors, to maximize detection probability.