Intelligent Dental Implant

Description

Dental implants are widely used and their deployment is expected to further increase in a market up to $6.81 Billion by 2024. Titanium (Ti) implants account for the largest share of the global dental implant market (75.8%). The risk associated with these implants is still high, and failures can be accurately detected only a long time after insertion. Thus, scientific reports show a success rate of 87 – 97% for healthy patients 2 years after insertion and less than 70% 10 years after insertion. A few years after insertion, “peri-implantitis appears to develop and then progresses quickly at an accelerating pace”. One possible mechanism of developing this kind of failure is the high difference between the stiffness of the implant (180GPa for Ti) and the stiffness of the mandibular/maxillary bone (20-40GPa) causing a non-uniform stress distribution on the natural bone. In addition, with age, more than 50% of the patients will face bone loss caused by newly developed diseases such as osteoporosis, periodontitis, diabetes that affect the interface between the implant and the bone. In addition, such implants offer even a lower success rate (60-70%) for edentulous patients (with poor tissue healing). Moreover, current implant solutions often lead to insufficient remaining bone structure for patients with periodontitis. To overcome these challenges, we will create an innovative solution for a Ti 3D printed dental implant, which provides a faster osseintegration compared to current solutions. The proposed implant ensures a gradient in mechanical properties, such as stiffness, which matches the stiffness of the mandibular/maxillary bone. Moreover, our teeam will create a portable and non-invasive monitoring system to provide an earlier detection of possible failure in all kinds of implants.

Project Team

  • Owner: Mihaela Banu (Engineering)
  • Gustavo Mendonca (Dentistry)
  • Laurie McCauley (Dentistry, Medicine)
  • Sun-Yung Bak (Dentistry)
  • Bogdan Ioan Popa (Engineering)
  • Robert Buechler (Engineering)

Interested in collaborating? Join this team


Fastest Path to Zero: Clean Energy Deployment

Description

The ability to mitigate the negative effects of climate change due to carbon emissions requires a multifaceted interdisciplinary approach given the complex interplay between technology, economics, public policy, and the social elements of dynamically changing populations. Yet, many traditional approaches to developing mitigation actions tend to only focus on one of these four pillars. This collaboration will start, nurture, and grow interdisciplinary teams and ideas intended to lead national discussions on climate mitigation.

Project Team

  • Owner: Todd Allen (Engineering)
  • Jane Prophet (Art & Design)
  • Omolade Adunbi (LSA: Humanities)
  • Seth Guikema (Engineering)
  • Sol Hart (LSA: Social Sciences, SEAS, ISR)
  • Ellen Hughes-Cromwick (UMOR)
  • Mark Kushner (Engineering)
  • Johanna Mathieu (Engineering)
  • Ashley Payne (Engineering)
  • Tony Reames (SEAS)
  • Patricia Schuster (Engineering)
  • Volker Sick (Engineering)
  • Steven J. Skerlos (Engineering)
  • Stuart Soroka (LSA: Social Sciences, ISR)
  • Anna G. Stefanopoulou (Engineering, UMOR)
  • Kaitlin Raimi (Public Policy, ISR, LSA: Social Sciences)
  • SangHyun Lee (Engineering)
  • Suzanne Baker (Engineering)
  • Al-Thaddeus Avestruz (Engineering)
  • Xun Huan (Engineering)
  • Brendan Kochunas (Engineering)

Interested in collaborating? Join this team


Trustworthy and Robust Machine Learning

Description

There are several recent results in security and ML communities showing that deep neural networks for complex tasks such as computer vision or speech recognition are susceptible to incorrectly classifying adversarial inputs that are strategically generated by slightly perturbing inputs that are classified correctly. For example, in one class of attacks, placing stickers on a stop traffic sign could fool a state-of-the-art classifier into classifying the sign as a 80 speed limit sign. Similarly, adding a small amount of noise matrix to a Panda could make it appear to be a Gibbon to a ML classifier with a high confidence in erroneous classification. In a related line of research, it has been shown that a single malicious training input can cause learning of a model that makes a desired error on other inputs.. This has obvious implications for society as AI techniques are increasingly used in safety-critical applications or where preventing manipulation is important. This theme would like to bring together researchers in ML, security, robust optimization, and application areas such as autonomous vehicles, robotics, and healthcare with interest in developing a deeper understanding of the challenges in developing trustworthy ML models.

Project Team

  • Owner: Atul Prakash (Engineering)
  • Benjamin Kuipers (Engineering)
  • Vineet Kamat (Engineering)
  • Xun (Ryan) Huan (Engineering)

Interested in collaborating? Join this team


Aerosols and Health

Description

Acute respiratory infections (ARIs) ranging from seasonal influenza to Legionnaire’s disease to “valley fever” are caused, wholly or in part, by inhaling airborne pathogens: viruses, bacteria, or spores. Such infections occur when three criteria are met: the pathogen is sufficiently virulent (infectivity), sufficiently retains its infectivity against atmospheric degradation as an aerosol (resistance), and reaches target cells in the host in quantities exceeding its infectious dose (transmission). The focus of this collaboration of researchers with complementary expertise is the study of airborne pathogen resistance to inactivation and its role in airborne transmission of disease.

Project Team

  • Owner: Herek Clack (Engineering)
  • Krista Wigginton (Engineering)
  • Allison Steiner (Engineering, LSA: Natural Sciences)
  • Paolo Elvati (Engineering)

Interested in collaborating? Join this team


Electrify Michigan

Description

A gradual transition to electrified mobility is underway. The benefits of such a transition to CO2 emissions, energy security, and air quality are well known. However, it is uncertain if this transition will occur at a rate and scale sufficient to avoid irreversible environmental damage and associated economic losses. Furthermore, such a transition must be accomplished without substituting different, yet still damaging environmental impacts. Consequently, the new technologies that replace hydrocarbon-based mobility should be intrinsically safe, convenient, affordable, reusable, and built from earth-abundant materials. We hypothesize that this electrification transition can be dramatically accelerated through a comprehensive, cross-disciplinary effort that spans all forms of electric mobility and their integration with the grid. This will be accomplished by synergizing research, education, and technology translation. The goal is to develop the control strategies, policies, and energy storage systems that will substantially shorten the timeline for sustainable electrification.

Project Team

  • Owner: Jason Benjamin Siegel (Engineering)
  • Anna G. Stefanopoulou (Engineering, UMOR)
  • A. Galip Ulsoy (Engineering)
  • Donald Jason Siegel (Engineering)
  • Katsuyo S. Thornton (Engineering)
  • Ellen Hughes-Cromwick (Engineering)
  • Gregory A. Keoleian (SEAS, Engineering)
  • Bart Bartlett (LSA: Natural Sciences, UMOR)
  • Neda Masoud (Engineering)
  • Huei Peng (Engineering, UMOR)
  • Johanna Mathieu (Engineering)
  • Jing Sun (Engineering)
  • Heath Hofmann (Engineering)
  • Neil Dasgupta (Engineering)
  • Christian M. Lastoskie (Engineering)
  • Al-Thaddeus Avestruz (Engineering)
  • Jeff Sakamoto (Engineering)
  • Wei Lu (Engineering)
  • Yafeng Yin (Engineering)
  • Danai Koutra (Engineering)
  • Steven Skerlos (Engineering)
  • Robert Hampshire (Engineering, Public Policy)
  • Ming Xu (SEAS, Engineering)
  • Seth Guikema (Engineering)
  • Ella Atkins (Engineering)
  • M. Reza Amini (Engineering)

Interested in collaborating? Join this team


Big Data Methods & Applications

Description

This theme aims to bring together researchers working on big data methodologies and applications to different modalities: text, images, videos, audio, networks, tabular data, sensor data, and more. The meetings of this team will be an outlet to share ideas, learn about new concepts and methodologies to deal with big data challenges, and gain insights from other subfields of big data. Interactions between the participating members are expected to lead to new research collaborations, and joint applications for external funding and other initiatives.

Project Team

  • Owner: Danai Koutra (Engineering)
  • Laura Balzano (Engineering)
  • David Jurgens (Information, Engineering)
  • Hosagrahar V. Jagadish (Engineering)
  • Vijay Subramanian (Engineering)
  • Alfred O. Hero (Engineering, LSA: Natural Sciences)
  • Jeffrey A. Fessler (Engineering, Medicine)
  • Kevyn Collins-Thompson (Information, Engineering)
  • Daniel M. Romero (Information, Engineering)
  • Raj Rao Nadakuditi (Engineering)
  • Eytan Adar (Engineering, Information)
  • Paul J. Resnick (Information)
  • David Ford Fouhey (Engineering)
  • VG Vinod Vydiswaran (Medicine, Information)
  • Jenna Wiens (Engineering)
  • Clayton Scott (Engineering)
  • Robin Revette Fowler (Engineering)
  • Laura Kay Alford (Engineering)
  • Ceren Budak (Information, Engineering)
  • Michael John Cafarella (Engineering, ISR)
  • Qiaozhu Mei (Information, Engineering)
  • Rada Mihalcea (Engineering)
  • Emily Provost (Engineering)
  • Brendan Kochunas (Engineering)
  • Atul Prakash (Engineering)
  • Joshua Welch (Medicine)
  • Jason Corso (Engineering)

Interested in collaborating? Join this team


Design Science

Description

Design Science is an interdisciplinary program for communicating and collaborating about science, practice, and training in design across disciplines. Design is widely dispersed across fields with differing terminologies, traditions, literatures, and research practices. Design Science provides connections across diverse disciplines at U-M, including Engineering; Art & Design, Architecture & Urban Planning; Behavioral, Social and Cognitive Sciences; Life and Health Sciences; Business (Organizational, Marketing, Manufacturing, Management Science, and Entrepreneurship); and Computer and Information Sciences. Design Science further aims to motivate U-M scholars, students, and practitioners from these diverse fields to recognize the value of collaborative exploration in design for the creation of new knowledge, artifacts, services, and systems embedded within our physical, virtual, psychological, economic, social, cultural, and societal environments.

Project Team

  • Owner: Colleen Seifert (Engineering, LSA: Social Sciences, ISR)
  • Matthew Reed (Engineering)

Interested in collaborating? Join this team


Moving Vehicle-to-Vehicle Power Transfer

Description

The age of electrification is upon us with communicating and autonomous vehicles (CAVs) dawning. Imagine a world where energy is available everywhere and anytime through an expansion of the idea for a virtual mobility network beyond that which is already visible and available in Uber and Lyft. Envision energy transactions that are fluid and transparent in what is literally and figuratively an energy superhighway.

We introduce a bold new concept of moving vehicle-to-vehicle wireless power transfer (MV2V-WPT) between Electric Vehicles (EVs) to facilitate frequent, real-time, and on-demand charging. The proposed transformative technology is built on the concept of the sharing economy (much like the ridesharing platform Uber) and paves the way toward a fully electrified transportation system with enormous environmental and societal benefits.

Project Team

  • Owner: Neda Masoud (Engineering)
  • Al-Thaddeus Avestruz (Engineering)
  • Chinedum Okwudire (Engineering)
  • Victor Li (Engineering)
  • Alanson Sample (Engineering)

Interested in collaborating? Join this team


Structural Batteries

Description

This project is focused on developing weight- and energy-saving devices that combine load bearing and charge storage functionalities defined here as structural batteries. These two functionalities are ubiquitous in modern technologies and, therefore, development of structural batteries with high capacity and uncompromised safety will be transformative for transportation infrastructure, robotics, space exploration, prosthetics, distributed energy generation, health technologies, wearable optoelectronics, and sustainable buildings. The center of excellence at the University of Michigan (U-M), focused on the practical realization and integration of key technologies broadly represented in the U-M College of Engineering, including transportation, robotics, and biomedicine, selected as focus areas for this project will have an extensive catalytic effect on proliferation of structural batteries.

However, the contrarian structural and physicochemical requirements for materials with high mechanical, charge transport, and storage properties represents the fundamental roadblocks for the successful design and device integration of structural power. Our team will address this fundamental bottleneck of structural batteries by using bioinspired engineering of nanocomposites based on branched aramid nanofibers. Solid-state batteries with Zn and Mg chemistries that represent attractive alternatives to Li batteries due to their high energy density, earth-abundance, reliability, and non-flammability will be developed. Furthermore, these batteries will be made plastically deformable to produce complex corrugated shapes that will be essential for the integration of batteries into load-bearing structures. Computational methods will be used for engineering the materials and establishing the limits of their performance. The corrugated solidstate batteries will be integrated with several testbed devices represented by energy-efficient unmanned aerial systems and prosthetic knee-ankle systems. Demonstration of corrugated structural batteries, along with establishing their advantages and disadvantages, will open multiple venues for their applications from electrical vehicles to satellites.

Project Team

  • Owner: Nicholas Kotov (Engineering)
  • Bart Bartlett (LSA: Natural Sciences, UMOR)
  • John Kieffer (Engineering)
  • Carlos Cesnik (Engineering)
  • Elliott J. Rouse (Engineering)
  • Jing Sun (Engineering)

Interested in collaborating? Join this team


Weyltronics – A Revolutionary Technology for Next Generation Information Processing

Description

The recent discovery that stoichiometric crystals XY, where X = {Ta,Nb} and Y = {As,P}, demonstrate properties of Weyl semimetals (WSM) opens unique opportunities for developing Weyltronics as a revolutionary technology for the next generation information processing. WSM are a special class of 3D Dirac semimetals where electrons behave like massless Dirac fermions as in graphene. Due to zero effective mass of electrons in Dirac materials, the electrons can be characterized by a definite helicity (sign of the mutual orientation of the electron spin and its momentum), so that the helicity density, commonly called the axial charge, satisfies a conservation law similar to conservation of the electric charge. Additionally, in WSM, the Dirac cones corresponding to electrons with opposite helicities are separated leading to an effective axial field coupled to the axial charge making helicities dynamically distinguishable. The proposed research will lead to introduction of the relativistic quantum field theory into the context of classical and quantum information processing. Our goal is to develop the theoretical foundation for information processing in axial circuits and to investigate basic elements of such circuits.

Project Team

  • Owner: Pinaki Mazumder (Engineering)
  • Mikhail Erementchouk (Engineering)
  • Rachel Goldman (Engineering, LSA: Natural Sciences)

Interested in collaborating? Join this team