Algorithms, Languages & Databases
Major breakthrough in dynamic graph algorithms earns Best PaperThatchaphol Saranurak and collaborators were recognized at SODA '23 for their work that broke an approximation barrier in dynamic graph matching.
Using negative probability for quantum solutionsProbabilities with a negative sign have been of great use in quantum physics.
Google Award to make widely used software testing technique more effectiveBaris Kasikci plans to improve software fuzzers by learning how deployed software is most commonly run by users.
Solution for restoring faulty graphs earns best paper awardProf. Greg Bodwin has devised a solution to an important open question in graph theory that offers promising new options for repairing and constructing resilient networks.
Postdoc Leqi Zhu wins PODC Dissertation AwardThe thesis completely solves a longstanding open problem in the theory of distributed computing.
Tim Dunn selected for NSF Graduate Research Fellowship
Through his work, Tim hopes to dramatically accelerate genomic sequencing analysis, enabling the use of handheld genomic sequencers to produce actionable diagnostic data within minutes.
New database sheds light on Michigan’s videogame boom
The Michigan Game Studios database, developed by lecturer Austin Yarger, helps organize the state’s rapidly growing scene.
Building a testing-free future
How automated guarantees that our most complex programs are secure and trustworthy can save us time, money, and anxiety.
Tool to automate popular security technique earns distinguished paper
The new technique automatically constructs policies for applications that keep them from compromising other programs.
Prof. Baris Kasikci recognized as rising star by Intel
The award recognizes early career faculty who show great promise in developing future computing technologies.
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.
Hunger and COVID: Fighting pandemic-related food insecurity in Detroit
Public policy and engineering team up to improve food access.
New method ensures complex programs are bug-free without testing
The system targets software that runs using concurrent execution, a widespread method for boosting performance, and proves whether a program will output what it’s supposed to.
Get to know: Xinyu Wang
“My research has the potential to democratize programming and make it possible for millions of people around the globe to automate otherwise tedious tasks using programming.”
How predictive modeling could help us reopen more safely
Graphical online simulation could spur more targeted COVID-19 protection measures.
Research team takes on food insecurity in Detroit in the face of coronavirus limitations
Researchers are working with the city on two key initiatives to address food availability for elderly and low-income populations.
Building better coronavirus databases with automatic quality checks
The team will build high-quality datasets to enable automatic quality checking and fraud detection of the new coronavirus data.
Undergraduate research on speeding up data centers earns ACM first prize
The student’s project targets critical moments where the next instruction in a program is only available in a slower type of memory.
Rackham Predoctoral Fellowship for design of robust, reliable and repairable software systems
Subarno Banerjee uses program analysis to improve software systems’ safety and security.
Predoctoral Fellowship for mathematically provable hardware design
Goel designs algorithms that can automatically demonstrate the correctness of hardware systems.
Programming around Moore's Law with automatic code translation
Most programs in use today have to be completely rewritten at a very low level to reap the benefits of hardware acceleration. This system demonstrates how to make that translation automatic.
Emotion recognition has a privacy problem – here’s how to fix it
Researchers have demonstrated the ability to “unlearn” sensitive identifying data from audio used to train machine learning models.
Generating realistic stock market data for deeper financial research
A team at Michigan proposed an approach to generating realistic and high-fidelity stock market data to enable broader study of financial markets.
Three faculty earn MIDAS grants to broaden the frontiers of data science
This round of funding strongly encourages pioneering work with the potential for major expansion.
Best Student Paper Award for work on faster network classification for machine learning
Comparing graphs the team’s tool is up to an order of magnitude faster than competitive baselines.
CSE faculty bring significant showing to major systems conference
Researchers designed three new systems to speed up code at several key bottlenecks.
$2M NSF grant to explore data equity systemsResearchers plan to establish a framework for a national institute that would enable research using sensitive data, while preventing misuse and misinterpretation.
$1M NSF grant supports new system for gathering, structuring data with ease
The team's new tool will combine of software and data to make gathering structured data dramatically easier.
Creating more efficient data centers for AI
Tang’s project will redesign data center systems to support large-scale use of hardware accelerators to meet future computational demand.
New browser strategy game has players tackle real-life bat catastrophe
As a fungal infection ravages bat populations, the new game hopes to promote public awareness of ongoing research to combat the issue.
“Mind reading” study looks inside coders’ brains
Using real-time fMRI readings, researchers linked spatial reasoning with CS problem solving.
Automated tool optimizes complex programs better than humans
Erie provided database repairs that were previously performed exclusively by human programmers.
CAREER Award for deeper insights into interconnected data: from neurons to web searches
Danai Koutra earned the award for her proposal to innovate the way we use networks to understand the world and speed up our technology.
Student awarded NSF Fellowship for automating speech-based disease classification
Perez’s research focuses on analyzing speech patterns of patients with Huntington Disease.
Paper award for identifying speaker characteristics in text messages
The goal of the work was to identify seven things about who the subject was talking to just by analyzing text messages.
Two solutions for GPU efficiency can boost AI performance
Chowdhury’s lab multiplied the number of jobs a GPU cluster can finish in a set amount of time
Personalized knowledge graphs for faster search and digital assistants
Graphs that are customized, stored locally, and able to change over time can enable faster and more accurate searching and digital assistants
Speeding up code with clever data manipulation
Kasikci presents a method to improve a program’s ability to use data in a straightforward, efficient way
Helping drivers use smart cars smarter
This conversational in-vehicle digital assistant can respond to drivers’ questions and commands in natural language
Gaining a deeper understanding of how personal values are expressed in text
Researchers used hierarchical trees to provide a better idea of how concepts are represented and related in a collection of text.
Making software failures a little less catastrophic
Researchers have implemented a new way to diagnose software failures with a high degree of accuracy and efficiency.
Detecting Huntington’s disease with an algorithm that analyzes speech
New, preliminary research found automated speech test accurately diagnoses Huntington’s disease 81 percent of the time and tracks the disease’s progression.
Fake news detector algorithm works better than a human
System sniffs out fakes up to 76 percent of the time.
Tool for structuring data creates efficiency for data scientists
Foofah is a tool that can help to minimize the effort and required background knowledge needed to clean up data.
Finding meaning in varied data
Jie Song devised a method to combine summarized datasets that group information by incompatible units.
Study maps careers of CS PhDs using decades of data
The researchers identified movement between industry, academia, and government work, tracked the growth of important organizations, and built predictive models for career transitions and employer retention.
“Stitching” together a web user from scattered, messy data
Even though we interact with different web services in different ways, there are clues in the data that can indicate trends and identify a unique profile.
Bringing smart banking to market
Jason Mars, CEO of Ann Arbor startup Clinc, was named #2 in Bank Innovations’s “10 Most innovative CEOs in Banking 2017” list. Clinc is leading the pack for development of intelligent banking assistant software.
“Learning database” speeds queries from hours to seconds
Verdict can make databases deliver answers more than 200 times faster while maintaining 99 percent accuracy.
Kurator Will Help You Curate Your Personal Digital Content
Kurator is a hybrid intelligence system leveraging mixed-expertise crowds to help families curate their personal digital content, including videos and photos.
Movie design for specific target audiences
Researchers are working to design a successful movie that will attract the interest of a targeted demographic by leveraging user ratings, reviews, and product characteristics.
CHORUS: The Crowd-Powered Conversational Assistant
Researchers have developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system.
Social interaction patterns provide clues to real life changes
The identified changes in social media behavior may point to real events and changes, some of which can benefit from intervention.
Designing for our own
CSE students designed technology for a fellow student who returned after a decade away because of a brain hemorrhage.
Emily Mower Provost receives NSF CAREER Award to develop emotion and mood recognition for mental health monitoring and treatment
Prof. Mower Provost’s research interests are in human-centered speech and video processing, multimodal interfaces design, and speech-based assistive technology.
Google-funded Flint water app helps residents find lead risk, resources
Mywater-Flint is an app built to help with the Flint water crises funded by Google and developed by Michigan Engineers.
Algorithms can be more fair than humans
Still, it’s not guaranteed, as seen in Amazon’s same-day delivery service. Algorithm designers may not even realize a problem has crept in.
Can slower financial traders find a haven in a world of high-speed algorithms?
A frequent call market may help prevent ‘flash crashes.’
Collecting data to better identify bipolar disorder
Prof. Emily Mower Provost is collaborating to develop new technologies that provide individuals with insight into how the disease changes over time.
Jenna Wiens receives NSF CAREER Award to increase the utility of machine learning in clinical care
Her primary research interests lie at the intersection of machine learning and healthcare.
The Promise and Perils of Predictive Policing Based on Big Data
Such tactics, even if effective in reducing crime, raise civil liberty concerns.
Advancing computation: 4th U-M alum wins Turing Award
The Turing Award has honors the computer scientists and engineers who create the systems and underlying theoretical foundations that propel the information technology industry.
Protean Code Allows Data Center Servers to Adapt to Changing Environments with Breakthrough Compiler Technology
Protean Code is an enabling technology for dynamically recompiling native applications and rebalancing the use of Warehouse Scale Computers resources as demands dictate.