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EECS faculty are MCubing to find answers - fast

Thanks to the MCubed program, EECS faculty are teaming up with colleagues across the University of Michigan - from Epidemiology to Political Science, Ophthalmology to Psychiatry, Neurosurgery to Astronomy - to pursue new initiatives deemed to have major societal impact.

The MCubed program was established to minimize "the time between idea conception and successful research results by providing immediate startup funds for novel, high-risk and transformative research projects."

Here are the MCubed projects that involve our faculty in Electrical Engineering and Computer Science:

Adaptive Health Communications over Mobile Devices Satinder Singh Baveja, PI (CSE), Karen Farris (Social and Administrative Sciences), John Piette (Internal Medicine) The team plans leverage mobile technology and machine learning to develop tailored/personalized health communication that should be more effective at improving health outcomes. [more info]	Data-Mining for Optimal Metal-Organic Frameworks Donald Siegel, PI (Mechanical Engineering), Michael Cafarella (CSE), Antek Wong-Foy (Natural Sciences) The team proposes to use computation and experimentation to identify new metal-organic frameworks (MOFs) for applications ranging from CO2 capture to the storage of chemical fuels. [more info]	COOL-Plasma: Efficient cold-plasma generation for applications in spacecraft propulsion and wound treatment Juan Manuel Rivas Davila, PI (ECE), Benjamin Longmier (Aerospace Eng.), Alexander Rickard (Epidemiology) The team plans to develop small and efficient cold plasma source devices for use in spacecraft propulsion systems and topical wound healing in a medical setting. [more info]
Mechanical Properties and Computational Methods for Composites Formed of Pre-stressed Knitted Textiles Sean Ahlquist, PI (Architecture & Urban Planning), Anthony Waas (Aerospace Engineering), Georg Essl (CSE) Proposed is the utilization of pre-stressed knitted textiles for reinforcements in structural composites, as tailored materials for tissue engineering scaffolds, examining manufacturing methods, testing their mechanical properties, and developing computational methods for simulating their performance. [more info]	Low power electronics for brain machine interface applications Cindy Chestek, PI (Biomedical Engineering), Parag Patil (Neurosurgery), Michael Flynn (ECE) Brain machine interfaces for the treatment of paralysis will require implantable devices that can record from hundreds of neural channels simultaneously. The team will attempt to dramatically lower the power consumption of the implant without suffering any measurable drop in signal quality. [more info]	A Novel Glaucoma Drainage Device Joshua Stein, PI (Ophthalmology and Visual Sciences), Denise John (Ophthalmology and Visual Sciences), Yogesh Gianchandani (ECE) Glaucoma is a sight-threatening condition which affects over 2 million Americans. This collaborative effort hopes to design a novel implant that can effectively lower the eye pressure and prevent worsening of glaucoma. [more info]
Reconfigurable graphene beams for microscopy and micromanipulation Anthony Grbic, PI (ECE), Hui Deng (Physics), John Hart (Mechanical Eng.) Graphene supports strongly confined surface plasmons at infrared frequencies that can be tuned using a gate voltage. In this work, these surface plasmons will be used to generate reconfigurable, TM-polarized Bessel beams for microscopy applications and micromanipulation. [more info]	High-resolution imaging of fat tissue metabolism Tae-Hwa Chun, PI (Internal Medicine: Endocrinology, Diabetes & Metabolism), L. Jay Guo (ECE), Xueding Wang (Radiology) This research aims to better understand glucose metabolism by combining cell biology with nanoscale bioengineering and photoacoustic microscopic technique. [more info]	Big Data in astronomy: U-M astroinformatics research group Chris Miller, PI (Astronomy), Jeff McMahon (Physics), Alfred Hero (ECE) The team will attempt to tackle questions about our Universe that can best be answered by combining observations with advances in imaging analysis, non-parametric statistics, inference through machine learning, and high dimensional hypothesis testing and regression statistics. [more info]
Digital Humanities Approaches to Popular Periodicals: Quantifying Reading Trends with Time Series Analysis Peter McIssac, PI (German Studies), Ines Ibanez (natural Resources & Environment), Sugih Jamin (CSE) This project uses distant reading and digital humanities techniques to study mainstream German periodicals between 1850 and 1918. Because the bulk of material cannot all be close read, computer-based techniques will index, organize, and present information. [more info]	Virtual reality as a surrogate sensory environment for evaluation of human luminous environment Mojtaba Navvab, PI (Architecture), Kwoon Wong (Opthalmology & Visual Sciences), Pei-Cheng Ku (ECE) The team will design and fabricate different types of LED-based light sources exhibiting different lighting characteristics and study the effects of these LED light sources within specially designed luminous environments. [more info]	Machine Learning for Computational Healthcare Honglak Lee, PI (CSE), Elliot Soloway (CSE), James Wrobel (Internal Medicine) The team will use machine learning to identify important high-level abstractions (e.g., computational biomarkers) from data and improve the performance of computer-aided diagnosis and risk prediction. [more info]
Using satellite data to study how variation in energy access can lead to economic, political instability Brian Min, PI (Political Science), Pauline Luong (Political Science), Rajesh Rao Nadakuditi (ECE) The team will study how variations in energy access at the local level can lead to economic and political instability in a study region spanning North Africa, the Middle East, South Asia, and Central Asia. [more info]	Pattern classification for discovery of biomarkers of psychiatric disease Chandra Sripada, PI (Psychiatry), Clay Scott (ECE), James Swain (Psychiatry, Child and Adolescent Section) To better diagnose diseases such as ADHD, autism, and schizophrenia, the team will use advanced pattern classification methods derived from statistics and computer science to discover hidden patterns in diseased brains, patterns too subtle and distributed to be detected by human observers. [more info]	ALDH inhibitors as cancer differentiation therapy Ronald Buckanovich, PI (Medicine), Scott Larsen (Medicinal Chemistry), Euisik Yoon (ECE) The team has identified novel ALDH inhibitors that deplete ovarian cancer stem cells, and will attempt to determine the mechanism by which this occurs, and the impact of these inhibitors on stem cell survival. [more info]