Since 2011, researchers at the University of Michigan, ​Technion – Israel Institute of Technology and the Weizmann Institute of Science in Israel have collaborated on nearly 50 competitively funded research projects as part of the Michigan-Israel Partnership for Research and Education.

A $20 million gift from The D. Dan and Betty Kahn Foundation, announced in October 2018, is supporting two large interuniversity research projects that aim to enhance quality of life through advancements in precision health and robotics.

“We see the partnership as a natural extension of Dan Kahn’s vision and an opportunity for three of the world’s great universities to pursue transformative research advancements in health, science and education.”

— Larry Wolfe, President, The D. Dan and Betty Kahn Foundation

Revolutionizing Precision Health Among Older Adults

Aging

Researchers at U-M, Technion and Weizmann are working together to address two major challenges related to aging health.

  1. Common, recurring infections, including urinary tract infections, respiratory viral infections and pneumonia that decrease the quality and quantity of life, and – because of increasing drug resistance – pose challenges for treatment.
  2. Highly important, yet difficult to predict, aging trajectories of the immune system.

Recognizing the high potential for impact, researchers propose combining their joint strengths in data science, immunology and infectious diseases to address these challenges.

Through this project, researchers aim to:

  • Explore novel technologies for collecting new types of data in older adults, focusing specifically on the blood, the microbiome and the genomes of infecting pathogens.
  • Follow a cohort of elderly individuals in a nursing facility and use novel technologies to identify factors associated with the acquisition of upper respiratory infections.
  • Develop novel machine learning tools for modeling immune and microbiome trajectories of healthy aging.
  • Create a large, deidentified, comprehensive, longitudinal dataset pertaining to older adults that can be securely shared across institutions for studying aging populations (a combination of retrospective and prospective data).

Research Team

  • U-M: Betsy Foxman, Lona Mody and Jenna Wiens
  • Technion: Roy Kishony, Shai Shen-Orr and Naama Geva-Zatorsky
  • Weizmann: Amos Tanay, Ido Amit, Roi Avraham and Liran Shlush

Enabling a Robotic Farming Revolution

FarmingResearchers at U-M, Technion and Weizmann are working together to combine cutting-edge advances in assistive robotics, robot autonomy, advanced mechanics, computer vision, machine learning and human-machine interfaces to make farming safer, easier and more productive.

Agriculture is a basic human need that is highly constrained by the current availability of workforce. It often entails backbreaking work in exposed environments.

Better agricultural practices, utilizing intelligent robotic systems on land and in the air, will help reduce fatigue and health hazards for farmers, pollution and waste, and limit the enormous ecological damage caused by farming in its various forms. Any significant change in farming efficiencies could profoundly affect the problematic political and moral issues surrounding farm worker immigration and indenture, and thereby reduce human suffering worldwide.

Through this project, researchers aim to achieve the following:

  • Investigate new aerial vehicle concepts designed specifically for performing precision farming tasks within its unique operational constraints.
  • Revolutionize exoskeleton designs to increase productivity, reduce injuries, and enable aging and disabled farmers to continue to work productively.
  • Enable autonomous robots and exoskeletons to safely and reliably move through all terrains and environments of a farm, greatly reducing the need for un-enhanced human labor, and allow farming on terrains not accessible by car.
  • Develop algorithms for human intent recognition and multi-robot coordination that would allow robots to directly assist humans in farming tasks by providing them with reliable data and actions to take in the farm.

Research Team

  • U-M: Shai Revzen, Ella Atkins, Carlos Cesnik, Cynthia Chestek, Dimitra Panagou and Elliott Rouse
  • Technion: Yizhar Or, Amir Degni, Moti Karpel, Ron Kimmel, Raphael Linker, Daniella Raveh and Miriam Zacksenhouse
  • Weizmann: Shimon Ullmann, Ronen Basri, Shai Bagon, Michal Irani and Daniel Harari