News & EventsDepartment Events
Events
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Jun11
EVENT DETAILS
All McCormick graduates and their guests are welcome.
You are invited to the McCormick Dean’s Graduation Reception for engineering graduates, their families, and guests. Food and beverages will be served along with champagne to toast our graduates. Dean Julio M. Ottino and faculty will be present to celebrate with graduates.
TIME Sunday, June 11, 2023 at 10:00 AM - 11:30 AM
LOCATION East Lawn Tent, Norris University Center map it
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Jun11
EVENT DETAILSmore info
McCormick School of Engineering PhD Hooding and Master’s Degree Recognition Ceremony
TIME Sunday, June 11, 2023 at 1:30 PM - 3:30 PM
LOCATION Welsh-Ryan Arena/McGaw Memorial Hall map it
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Jun11
TIME Sunday, June 11, 2023 at 5:00 PM - 7:00 PM
LOCATION Welsh-Ryan Arena/McGaw Memorial Hall map it
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Jun12
EVENT DETAILS
Commencement
TIME Monday, June 12, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Jun14
EVENT DETAILS
The ECE Department will be hosting a seminar: "Machine Learning for Real-Time Constrained Optimization: The Case of Optimal Power Flows" with Prof. Minghua Chen from City University Hong Kong. This will event will take place on Wednesday, June 14th at 10:00am.
Abstract: Optimization problems subject to hard constraints are common in time-sensitive applications such as autonomous driving and signal processing. However, existing iterative solvers often face difficulties in solving these problems in real-time. In this talk, we focus on one such problem - the critical optimal power flow (OPF) problem in power system operation. We develop DeepOPF as a deep neural network (DNN) approach to solve OPF problems directly, a few orders of magnitude faster than state-of-the-art iterative solvers. The idea is to employ DNN's approximation capability to learn the input-solution mapping of the OPF problem (or any constrained problem). Thus, one can pass the input to the DNN and receive a quality solution instantly. A fundamental issue, however, is to ensure DNN solution feasibility with respect to the hard constraints, which is non-trivial due to inherent DNN prediction errors. To this end, we present two approaches, predict-and-reconstruct and homeomorphic projection, to ensure DNN solution strictly satisfies the equality and inequality constraints. In particular, homeomorphic projection is a low-complexity scheme to guarantee DNN solution feasibility for optimization over a general set homeomorphic to a unit ball, covering all compact convex sets and certain classes of nonconvex sets. The idea is to (i) learn a minimum distortion homeomorphic mapping between the constraint set and a unit ball using an invertible NN (INN), and then (ii) perform a simple bisection operation concerning the unit ball so that the INN-mapped final solution is feasible with respect to the constraint set with minor distortion-induced optimality loss. We prove the feasibility guarantee and bound the optimality loss under mild conditions. Simulation results, including those for non-convex AC-OPF problems in power grid operation, show that homeomorphic projection outperforms existing methods in solution feasibility and run-time complexity, while achieving similar optimality loss. We will also discuss open issues in machine learning for solving constrained puzzles.
Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He is a Professor of School of Data Science, City University of Hong Kong. He received the Eli Jury award from UC Berkeley in 2007 (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, and IEEE INFOCOM Best Poster Award in 2021. His recent research interests include online optimization and algorithms, machine learning in power system operation, intelligent transportation, distributed optimization, delay-critical networking, and capitalizing the benefit of data-driven prediction in algorithm/system design. He is an ACM Distinguished Scientist and an IEEE Fellow.
TIME Wednesday, June 14, 2023 at 10:00 AM - 11:00 AM
LOCATION L440, Technological Institute map it
CONTACT Catherine Healey catherine.healey@northwestern.edu EMAIL
CALENDAR Department of Electrical and Computer Engineering (ECE)
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Jun15
EVENT DETAILS
The ECE Department will be hosting a seminar: "Opportunities and Challenges in Spintronics: A first principles perspective" with Prof. Nicholas Kioussis from California State University Northridge. This will be on Thursday, June 15th at 2pm in L440.
Abstract:
I will review our computational developments during the past decade to explore several open questions and provide guiding rules for the design of ultra-low energy spintronic devices. (1) Exploit the large spin orbit coupling and emergence of magnetism in ultrathin heavy-metal-based ferromagnetic (FM) or antiferromagnetic (AFM) heterostructures to achieve large perpendicular magnetic anisotropy and high voltage-controlled magnetic anisotropy efficiency, the two major challenges for ultralow-power and high density nonvolatile MeRAM devices; (2) Employ the effect of alloying or phonons to enhance the charge-to-spin current conversion efficiency in nonmagnetic heavy metals; (3) Dynamically control both the direction and amount of current-induced spin accumulation at heavy metal/FM interface using an electric field in an oxide capped spin orbit torque device; and (4) Search and identify novel two-dimensional van der Waals Dirac half-metal magnets characterized by a band structure with a large gap in one spin channel and a Dirac cone in the other with carrier mobilities comparable to those in graphene.
This research was supported by NSF-PREM Grant No. DMR-1205734 and NSF Grant No. ERC TANMS-116050.
Nicholas Kioussis obtained his PhD from University of Illinois at Chicago in 1984 after which he became a postdoctoral researcher at West Virginia University. He joined California State University Northridge (CSUN) as a faculty member of Physics in 1987. He is the founder and director of the W. M. Keck Computational Materials Theory Center at California State University Northridge. His research centers in the areas of electronic structure calculations of strongly correlated systems, multiscale modeling of defects, spin transport in magnetic tunnel junctions, electric field control of magnetism in multiferroic heterostructures, and defect calculations in Type II Super Lattices.
TIME Thursday, June 15, 2023 at 2:00 PM - 3:00 PM
LOCATION L440, Technological Institute map it
CONTACT Catherine Healey catherine.healey@northwestern.edu EMAIL
CALENDAR Department of Electrical and Computer Engineering (ECE)
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Jul4
EVENT DETAILS
Fourth of July (No classes)
TIME Tuesday, July 4, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec9
EVENT DETAILS
The ceremony will take place at 4 p.m. on Saturday, December 9 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
*No tickets requiredTIME Saturday, December 9, 2023 at 4:00 PM - 5:30 PM
LOCATION Pick-Staiger Concert Hall map it
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science