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Nick Arango



Nick received his B.S. in electrical engineering from MIT in 2016. He began working with the computational prototype group in summer of 2015 collaborating with the Charleston-based MGH Martinos Center RF lab on an open source, multi-channel, current driver system used for static magnetic field control on MRI machines. Beginning in 2017, he has explored improving functional, structural, and spectroscopic brain imaging using static magnetic field control with local 'shim' coils. He is interested in finding convex optimization problems that approximate applications of static field control. In 2019 he focused on the automatic design of shim-coil wire-patterns of the brain using large quantities of reference-subject images. Presently he is interested in applying static field control methods developed for brain imaging to body and fetal imaging and improving image encoding using continuously varying magnetic field control. 




Sam Chevalier



Sam received his B.S. and M.S. degrees in Electrical Engineering from the University of Vermont in 2015 and 2016, respectively. His Master's thesis focused on characterizing the statistical warning signs of voltage collapse in power system networks. He is currently pursuing his Ph.D. in Mechanical Engineering, where his research focuses on a broad range of topics related to energy system modeling and analysis. His currents projects include developing methods for locating the sources of forced oscillations in transmission networks, leveraging inverse problem theory for power system applications, and investigating the coupled dynamics of natural gas pipeline networks and power systems.


Ilias I. Giannakopoulos

iliasg@mit.edu | personal website


Ilias received his diploma degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece, in 2016. Currently he is pursuing his Ph.D. degree at the Skolkovo Institute of Science and Technology, at the center for Computational and Data-Intensive Science and Engineering, Moscow, Russian Federation. From Oct. 2018 he is a visiting Ph.D. student at the Massachusetts Institute of Technology, at the Research Laboratory of Electronics, Cambridge, United States of America. His research is focused on computational electromagnetics, with an emphasis on volume and surface integral equation methods, numerical linear algebra, magnetic resonance imaging, electrical properties reconstruction and machine learning.




Juan David Gil



Juan is an undergraduate student researching neural network sensitivity metrics and their application to fairness.  He is interested in interpretability and robustness of AI metrics.


Ching-Yun Ko



cyko@mit.edu | personal website

Ching-Yun Ko received her B.S. degree in Applied Mathematics in 2017 from Wuhan University and M.Phil. degree in Electrical and Electronic Engineering in 2019 from the University of Hong Kong. Specially, she had research experience in tensor decomposition applications in system control and machine learning. Currently, her research interests include quantifying the robustness for different neural network architectures and broadening the use of tensor decomposition techniques in memory/ computationally-intensive applications.


Irene Kuang



Irene graduated with a B.S. in Biomedical Engineering from The University of Texas at Austin in 2017. She currently is a graduate student in the MIT EECS PhD program working with Professor Jacob White. Irene’s research investigates unconventional methods for magnetic resonance (MR) scanner construction. Her research aims to build hand-held, low-cost MR scanners from a permanent magnet topology designed through equivalent-charge-based optimization. Furthermore, she is interested in exploring opportunities in the image reconstruction space using non-Cartesian, non-Fourier image and RF encoding. Previously, she worked on several research projects involving design of flexible electronics using 2D materials for electrophysiological signal acquisition under Professor Nanshu Lu at UT Austin.


Lily Weng

twweng@mit.edu | personal website

Lily received her B.S. and M.S. degree in Electrical Engineering in 2011 and 2013, both from National Taiwan University, Taiwan. She had research experience in electromagnetic compatibility (EMC) on the topic of synthesizing broadband GHz common-mode filters in high-speed digital circuits. In the first three years at MIT (Sep 2013- Sep 2016), her research focus was on Uncertainty quantifications with applications in silicon photonic circuits. Starting from Sep 2016, she has shifted her research focus to non-convex optimization and machine learning. Specifically, she had experience on robust regression with mixed-integer optimization and non-negative matrix factorization. Currently, she is highly interested in the field of adversarial machine learning and studying the robustness of neural networks.


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