Michigan Engineering | University of Michigan K-5 teachers and students throughout Michigan are building thriving learning communities online by using free deeply-digital, standards-aligned curricula and platform developed by the U-M Center for Digital Curricula.
Roadmap for teachers: U-M free online learning platform paves the way
-
CSE researchers help organize 10th anniversary workshop on internet freedom
Prof. Roya Ensafi and PhD candidate Reethika Ramesh led organizing efforts for USENIX’s Tenth Workshop on Free and Open Communications on the Internet.
-
New research teaches AI how people move with internet videos
The project enables neural networks to model how people are positioned based on only partial views of their bodies, like perspective shots in instructional videos or vlogs.
-
Alumnus Yi-Jun Chang Wins PODC Dissertation Award
His work is in complexity theory of distributed computing.
-
Prof. Danai Koutra recognized as rising star with ACM SIGKDD Award
The Rising Star Award is based on an individual’s whole body of work in the first five years after the PhD.
-
NIST finalists for post-quantum security standards include research results developed by Prof. Chris Peikert
A new secure code is needed to protect private information from the power of quantum computing.
-
“Hiding” network latency for fast memory in data centers
A new system called Leap earned a Best Paper award at USENIX ATC ‘20 for producing remote memory access speed on par with local machines over data center networks.
-
Baris Kasikci earns CAREER Award to automatically improve software quality with data from everyday program use
Kasikci will sift through the byproducts of hundreds of millions of common program executions to determine how this data can automate some key steps in bug finding and fixing.
-
New collaboration promises greater innovation in medical device security
The two organizations will connect their membership and partner networks to work on advancing security for life-saving devices.
-
Enabling fairer data clusters for machine learning
Their findings reduce average job completion time by up to 95% when the system load is high, while treating every job fairly.